code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from ut... | 357 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"BAAI/AltCLIP": "https://huggingfa... | 358 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common im... | 339 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : Any = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitCon... | 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 360 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 0 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCAmelCase__ ( __lowercase ):
a__ : Union[str, Any] = """EncodecFeatureExtractor"""
a__ : Union[str, Any] = ("""T5Tokenizer""", ""... | 361 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 0 |
import qiskit
def __magic_name__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> qiskit.result.counts.Counts:
__lowerCamelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
... | 362 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.ut... | 363 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 339 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampl... | 364 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common... | 365 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __magic_name__ ( __lowerCAmelCase ... | 366 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : str ) -> str:
__lowerCamelCase = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __magic_name__ ( __lowerCAmelCase ... | 367 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_... | 368 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (... | 369 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__nam... | 339 | 0 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : str , __lowerCAmelCase : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : str ) -> Any: # noqa: E741
while r - l > 1:
__lowerCamelCase = ... | 370 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list[int] ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : str ) -> list[list[float]]:
__lowerCamelCase = []
for data in source_data:
for i, el in enumerate(_lowerCAmelCase ):
if len(_lowerCAmelCase ) < i + 1:
data_lists.append([] )... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():... | 339 | 0 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ = "▁"
SCREAM... | 350 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 0 |
"""simple docstring"""
def __magic_name__ ( __lowerCAmelCase : Optional[int] ) -> str:
return "".join(chr(ord(_UpperCAmelCase ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
... | 351 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, Fl... | 339 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""facebook/en... | 352 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 0 |
import numpy as np
from PIL import Image
def __magic_name__ ( __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : Any ) -> Dict:
__lowerCamelCase = np.array(lowerCamelCase__ )
if arr.shape[0] != arr.shape[1]:
... | 353 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : str , __lowerCAmelCase : int ) -> Dict:
return int(input_a == input_a == 0 )
def __magic_name__ ( ) -> Dict:
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2... | 354 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 0 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeli... | 355 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 339 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"google/pix2struct-textcaps-base": (
"ht... | 356 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase )... | 339 | 0 |
from __future__ import annotations
class lowerCAmelCase__ :
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Tuple=None ) -> Optional[int]:
__lowerCamelCase = data
__lowerCamelCase = None
def __repr__( self : str ... | 357 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[Any] = {"configuration_xlnet": ["XLNET_PRETRAINED... | 358 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common im... | 339 | 0 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_dat... | 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
S... | 360 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformer... | 361 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
... | 362 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_logger(__name__)
class lowerCAmelCase__ ( enum.Enum ):
a__ : Optional[int] = """all_... | 363 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : int ) -> str:
if number > 0:
raise ValueError('''input must be a negative integer''' )
__lowerCamelCase = len(bin(__snake_case )[3:] )
__lowerCamelCase = bin(abs(__snake_case ) - (1... | 364 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
imp... | 365 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 0 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 366 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 339 | 0 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import A... | 367 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 0 |
from math import factorial
def __magic_name__ ( __lowerCAmelCase : int = 100 ) -> int:
return sum(map(__lowerCAmelCase , str(factorial(__lowerCAmelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
... | 368 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 0 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAK... | 369 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__nam... | 339 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def __magic_name__ ( ) -> Union[str, Any]:
__lowerCamelCase = 9
__lowerCamelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
... | 370 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list[int] ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
# Checks if the entire collection has been sorted
if len(__lowerCAmelCase ) <= 1 or n <= 1:
return
... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():... | 339 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ = tuple[int, int, int]
SCREAMING_SNAKE_CASE__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE__ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- defau... | 350 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 0 |
"""simple docstring"""
def __magic_name__ ( __lowerCAmelCase : float , __lowerCAmelCase : int ) -> float:
if digit_amount > 0:
return round(number - int(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
return number - int(_SCREAMIN... | 351 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, Fl... | 339 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SN... | 352 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : int = 50 ) -> List[Any]:
__lowerCamelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + ... | 353 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
... | 339 | 0 |
import math
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int = 0 , __lowerCAmelCase : int = 0 ) -> list:
__lowerCamelCase = end or len(_lowerCAmelCase )
for i in range(_lowerCAmelCase , _lowerCAmelCase ):
... | 354 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 0 |
import logging
from transformers import PretrainedConfig
SCREAMING_SNAKE_CASE__ : Tuple = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summ... | 355 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 339 | 0 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( _lowerCamelCase ):
a__ : Union[str, Any] = (DDPMParallelScheduler,)
def __A ( self : List[Any] , **SCREAMING_SNAKE_CASE__ ... | 356 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase )... | 339 | 0 |
from manim import *
class lowerCAmelCase__ ( A__ ):
def __A ( self : int ) -> Union[str, Any]:
__lowerCamelCase = Rectangle(height=0.5 , width=0.5 )
__lowerCamelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(wi... | 357 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optiona... | 358 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common im... | 339 | 0 |
import os
SCREAMING_SNAKE_CASE__ : Dict = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def __magic_name__ ( __lowerCAmelCase : List[str] ) -> List[str]:
__lowerCamelCase = 0
__lowerCamelCase = 0
w... | 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __magic_name__ ( __lowerCAmelCase : Opti... | 360 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 0 |
from ...configuration_utils import PretrainedConfig
class lowerCAmelCase__ ( __lowercase ):
a__ : List[str] = """bert-generation"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any]=5_03_58 , SCREAMING_SNAKE_CASE__ : ... | 361 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 0 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNA... | 362 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 0 |
from __future__ import annotations
from typing import Any
def __magic_name__ ( __lowerCAmelCase : list ) -> int:
if not postfix_notation:
return 0
__lowerCamelCase = {"+", "-", "*", "/"}
__lowerCamelCase = []
for token in postfix_n... | 363 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
__lowerCamelCase = _modexpt(__lowerCAmelCase , exponent // 2 , __... | 364 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_ut... | 365 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 0 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# This ... | 366 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 339 | 0 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__ ... | 367 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
... | 368 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : in... | 369 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__nam... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ) -> Union[str, Any]:
__lowerCamelCase = len(snake_case_ )
__lowerCamelCase = []
for i in range(len(snake_case_ ) - pat_len + 1 ):
__lowerCamelCa... | 370 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list[int] ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokeniz... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : Dict ) -> Any:
if isinstance(__a , __a ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(__a , __a ):
raise TypeError('''\'str\' object cannot be inter... | 350 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase__ :
pass
| 351 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, Fl... | 339 | 0 |
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
... | 352 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"""Salesforce/blip-vqa-base""": ... | 353 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
... | 339 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def __magic_name__ ( __lowerCAmelCase : float ) -> Optional[Any]:
if num <= 0:
raise ValueError('''math domain error''' )
return quad(__lowerCAmelCase , 0 , __lowerCAmelCase , ... | 354 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 0 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any ,... | 355 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 339 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSo... | 356 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase )... | 339 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def __magic_name__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ) -> None:
"""simple docstring"""
__lowerCa... | 357 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 | 0 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurati... | 358 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common im... | 339 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __magic_name__ ( __lowerCAmelCase : str ) -> Optional[Any]:
for param in module.parameters():
__lowerCamelCase = False
def __magic_name__ ( ) -... | 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def __magic_name__ ( __lowerCAmelCase : List[Any] ) -> typing.Counter[int]:
__lowerCamelCase = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular... | 360 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num_examples=42 , dataset_name='''my_dataset''' ... | 361 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class lowerCAmelCase__ ( __lowercase ):
a__ : Tuple = field(default="""audio-c... | 362 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 0 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 363 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 339 | 0 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
a__ : int
a__ : TreeNode | None = None
a__ : TreeNode | None = None
SCREAMING_SNAKE_CASE__ : Tuple = name... | 364 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
SCREAMING_S... | 365 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pre... | 366 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 339 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIF... | 367 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowerCAmelCase__ ( UpperCa... | 368 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 0 |
import math
import tensorflow as tf
from packaging import version
def __magic_name__ ( __lowerCAmelCase : List[str] ) -> Union[str, Any]:
__lowerCamelCase = tf.convert_to_tensor(lowerCAmelCase_ )
__lowerCamelCase = 0.5 * (1.0 + tf.math.erf(x /... | 369 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__nam... | 339 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( __snake_case ):
a__ : Union[str, Any] = (IPNDMScheduler,)
a__ : Optional[Any] = (("""num_inference_steps""", 50),)
... | 370 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list[int] ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
SCREAMING_SNAKE_CASE__ : Tuple = datasets.uti... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():... | 339 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab... | 350 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowe... | 339 | 0 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone impo... | 351 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, Fl... | 339 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...t... | 352 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 0 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialStat... | 353 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
... | 339 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
class lowerCAmelCase__ ( __lowercase ):
def __init__( self : str , *SCREAMING_SNAKE_C... | 354 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 355 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 339 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 356 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase )... | 339 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.w... | 357 |
from functools import lru_cache
def __magic_name__ ( __lowerCAmelCase : int ) -> set:
__lowerCamelCase = 2
__lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lower... | 339 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
SCREAMING_SNAKE_CASE__ : Tuple = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
... | 358 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common im... | 339 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ : Optional[Any] = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import datac... | 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_im... | 360 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("KEY")
SCREAMING_SNAKE_CASE__ : Dict = TypeVar("VAL")
@dataclass(frozen=__lowercase , slots=__lowercase )... | 339 | 0 |
import math
def __magic_name__ ( __lowerCAmelCase : int ) -> list[int]:
__lowerCamelCase = []
__lowerCamelCase = 2
__lowerCamelCase = int(math.sqrt(__lowerCAmelCase ) ) # Size of every segment
__lowerCamelCase = [True] * ... | 361 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : list ) -> list:
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
__lowerCamelCase = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 362 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCamelCase = str(bin(__lowerCAmelCase ) )[2:] # remove th... | 339 | 0 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 363 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 339 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipeline... | 364 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list , __lowerCAmelCase : int | None = None , __lowerCAmelCase : int | None = None ) -> None:
if start is None:
__lowerCamelCase = 0
if end is None:
... | 339 | 0 |
def __magic_name__ ( __lowerCAmelCase : float , __lowerCAmelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'{price_plus_tax(100, 0.2_5) = }')
print(F'{price_plus_tax(125.50, 0.0_5) = }')
| 365 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {... | 339 | 0 |
from __future__ import annotations
def __magic_name__ ( __lowerCAmelCase : list ) -> float:
if not nums:
raise ValueError('''List is empty''' )
return sum(__lowerCAmelCase ) / len(__lowerCAmelCase )
if __name__ == "__main__":
import doct... | 366 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 339 | 0 |
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggin... | 367 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ : str = ""
SCREAMING_SNAKE_CASE__ : Any = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ""
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 339 | 0 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grise... | 368 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 0 |
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