code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowercase ( _A ) -> List[Tuple[int, ...]]:
SCREAMING_SNAK... | 245 |
import os
import pytest
from attr import dataclass
UpperCAmelCase__ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class a__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : Union[str, ... | 245 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series... | 245 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : Tuple = TypeVar("""T""")
class a__ ( Generic[T] ):... | 245 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __lowercase ( _A , _A , _A ) -> Any:
# Initialise PyTorch model
SCREAMING_SNAKE_CASE ... | 245 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : List[Any] = logging.get_logger... | 245 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 245 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ : List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath... | 245 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 245 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..t... | 245 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
Uppe... | 245 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : Optional[Any] = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""JukeboxP... | 245 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase__ : Optional[int] ) ->str:
"""simple docstring"""
... | 245 | 1 |
import mpmath # for roots of unity
import numpy as np
class a__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : str=None ) ->Dict:
"""simple docstring"""
... | 245 |
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 __lowercase ( _A , _A ... | 245 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.con... | 245 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCAmelCase__ ... | 245 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __lowercase ( _A ) -> list[list[float]]:
SCREAMING_SNAKE_CASE : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this i... | 245 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
Random... | 245 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowercase ( _A , _A , _A ) -> Tuple:
SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_A )
SCREAMING_SNAKE_CASE ... | 245 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 245 | 1 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 245 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[Any] ) ->Optional[int]:
"""simple docstri... | 245 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 245 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quie... | 245 | 1 |
from math import isqrt, loga
def __lowercase ( _A ) -> list[int]:
SCREAMING_SNAKE_CASE : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _A , _A )... | 245 |
def __lowercase ( _A ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
UpperCAmelCase__ : Optional[int] = int(input("""Ente... | 245 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall... | 245 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __lowercase ( _A , _A , _A ) -> int:
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Маши... | 245 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCAmelCase__ : str = {
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 ... | 245 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
... | 245 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowercase ( _A ) -> bool:
SCREAMING_SNAKE_CASE : int = int(number**0.5 )
return number == sq * sq
def __lowercase ( _A , _A ... | 245 |
# 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 requ... | 245 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAmelCase__... | 245 |
import sys
from collections import defaultdict
class a__ :
"""simple docstring"""
def __init__( self : Union[str, Any] ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ... | 245 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 245 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if ... | 245 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 245 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowercase ( _A ) -> List[Tuple[int, ...]]:
SCREAMING_SNAK... | 245 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class a__ ( datasets.BuilderConfig ):
"""simple docstring"""
U... | 245 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : int = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig"... | 245 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 245 | 1 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
UpperCAmelCase__ : List[str] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def __lowe... | 245 |
import os
import pytest
from attr import dataclass
UpperCAmelCase__ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class a__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : Union[str, ... | 245 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
UpperCAmelCase__ : Optional[Any] ... | 245 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : Tuple = TypeVar("""T""")
class a__ ( Generic[T] ):... | 245 | 1 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
... | 245 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : List[Any] = logging.get_logger... | 245 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if ... | 245 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ : List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_t... | 245 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..t... | 245 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a__ ... | 245 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 245 | 1 |
# Copyright 2022 The HuggingFace 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 ... | 245 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase__ : Optional[int] ) ->str:
"""simple docstring"""
... | 245 | 1 |
def __lowercase ( _A ) -> str:
return "".join(chr(ord(_A ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 245 |
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 __lowercase ( _A , _A ... | 245 | 1 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 245 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCAmelCase__ ... | 245 | 1 |
import math
def __lowercase ( _A ) -> bool:
SCREAMING_SNAKE_CASE : List[Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_A )
def __lowercase ( _A = 1 / 12345 ) -> int:
SCREAMIN... | 245 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
Random... | 245 | 1 |
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
UpperCAmelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCAmel... | 245 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 245 | 1 |
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 __lowercase ( _A , _A ... | 245 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[Any] ) ->Optional[int]:
"""simple docstri... | 245 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCAmelCase__ : List[Any] = """\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang... | 245 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quie... | 245 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] =(IPNDMScheduler,)
UpperCAmelCa... | 245 |
def __lowercase ( _A ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
UpperCAmelCase__ : Optional[int] = int(input("""Ente... | 245 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __lowercase ( _A , _A , _A , _A ) -> Optional[int]:
SCREAMING_SNAKE_CASE : Tuple = s.rsplit(_A , _A )
return new.join(_A )... | 245 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __lowercase ( _A , _A , _A ) -> int:
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Маши... | 245 | 1 |
from math import pi
def __lowercase ( _A , _A ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 245 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ : Optional[Any] = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2Form... | 245 |
# 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 requ... | 245 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import Polyno... | 245 |
import sys
from collections import defaultdict
class a__ :
"""simple docstring"""
def __init__( self : Union[str, Any] ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ... | 245 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
UpperCAmelCase__ : ... | 245 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if ... | 245 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : str = logging.get_logger(__name__)
UpperCAmelCase__ : List[Any] = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config... | 245 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowercase ( _A ) -> List[Tuple[int, ...]]:
SCREAMING_SNAK... | 245 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config... | 245 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall... | 245 | 1 |
import math
def __lowercase ( _A ) -> bool:
assert isinstance(_A , _A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:... | 245 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 245 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] )
def __low... | 245 |
import os
import pytest
from attr import dataclass
UpperCAmelCase__ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class a__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : Union[str, ... | 245 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __lowercase ( ) -> int:
SCREAMING_SNAKE_CASE : int = {
"""repo_name""": ["""test_repo1""", """test_repo2... | 245 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : Tuple = TypeVar("""T""")
class a__ ( Generic[T] ):... | 245 | 1 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase__ : Tuple = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logging i... | 245 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : List[Any] = logging.get_logger... | 245 | 1 |
# Copyright 2023 The HuggingFace 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 ... | 245 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ : List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath... | 245 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import ... | 245 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..t... | 245 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Dict = {
"""BAAI/AltCLIP""": """https://huggingface.co/BAA... | 245 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 245 | 1 |
def __lowercase ( _A = 600851475143 ) -> int:
try:
SCREAMING_SNAKE_CASE : Optional[Any] = int(_A )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise Val... | 245 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase__ : Optional[int] ) ->str:
"""simple docstring"""
... | 245 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_... | 245 |
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 __lowercase ( _A , _A ... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : Dict = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise Op... | 245 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCAmelCase__ ... | 245 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torc... | 245 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
Random... | 245 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __lowercase ( _A ) -> List[str... | 245 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 245 | 1 |
def __lowercase ( ) -> List[Any]:
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : str = 1
while len(_A ) < 1e6:
constant.append(str(_A ) )
i += 1
SCREAMING_SNAKE_CASE : Tuple ... | 245 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[Any] ) ->Optional[int]:
"""simple docstri... | 245 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class a__ ( ... | 245 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quie... | 245 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Bac... | 245 |
def __lowercase ( _A ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
UpperCAmelCase__ : Optional[int] = int(input("""Ente... | 245 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 245 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __lowercase ( _A , _A , _A ) -> int:
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Маши... | 245 | 1 |
# Copyright 2021 The HuggingFace 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 ... | 245 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
... | 245 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __lowercase ( _A = True , *_A , **_A ) -> str:
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqdm` m... | 245 |
# 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 requ... | 245 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
d... | 245 |
import sys
from collections import defaultdict
class a__ :
"""simple docstring"""
def __init__( self : Union[str, Any] ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ... | 245 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
Ima... | 245 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if ... | 245 | 1 |
from math import factorial
def __lowercase ( _A , _A ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("""Please ent... | 245 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowercase ( _A ) -> List[Tuple[int, ...]]:
SCREAMING_SNAK... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : str = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MA... | 245 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall... | 245 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : str = logging.get_logger(__name__)
UpperCAmelCase__ : List[Any] = {
"""SenseTime/deformable-detr""": """https://huggingface.co... | 245 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 245 | 1 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
UpperCAmelCase__ : Tuple = False
try:
UpperCAmelC... | 245 |
import os
import pytest
from attr import dataclass
UpperCAmelCase__ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class a__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : Union[str, ... | 245 | 1 |
import math
def __lowercase ( _A ) -> bool:
return math.sqrt(_A ) * math.sqrt(_A ) == num
def __lowercase ( _A ) -> bool:
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = n
... | 245 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : Tuple = TypeVar("""T""")
class a__ ( Generic[T] ):... | 245 | 1 |
from collections.abc import Sequence
def __lowercase ( _A , _A ) -> float:
return sum(c * (x**i) for i, c in enumerate(_A ) )
def __lowercase ( _A , _A ) -> float:
SCREAMING_SNAKE_CASE : Optional[Any] = 0.0
for coef... | 245 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : List[Any] = logging.get_logger... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ : Tuple = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRL... | 245 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ : List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath... | 245 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDataset... | 245 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..t... | 245 | 1 |
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,
Compu... | 245 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 245 | 1 |
import torch
from diffusers import DiffusionPipeline
class a__ ( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : str , UpperCAmelCase__ : str , UpperCAmelCase__ : str ) ->List[Any]:
"""simple docstrin... | 245 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase__ : Optional[int] ) ->str:
"""simple docstring"""
... | 245 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
UpperCAmelCase__ : List[Any] = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_bloc... | 245 |
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 __lowercase ( _A , _A ... | 245 | 1 |
import argparse
import copy
def __lowercase ( _A ) -> Dict:
SCREAMING_SNAKE_CASE : Optional[int] = {}
with open(_A ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
SCREAMING_... | 245 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCAmelCase__ ... | 245 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_o... | 245 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
Random... | 245 | 1 |
from __future__ import annotations
import bisect
def __lowercase ( _A , _A , _A = 0 , _A = -1 ) -> int:
if hi < 0:
SCREAMING_SNAKE_CASE : Optional[int] = len(_A )
while lo < hi:
SCREAMING_SNAKE_CASE : int... | 245 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 245 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertT... | 245 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[Any] ) ->Optional[int]:
"""simple docstri... | 245 | 1 |
def __lowercase ( _A ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
UpperCAmelCase__ : Optional[int] = int(input("""Ente... | 245 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quie... | 245 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __lowercase ( _A ) -> int:
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE : List[Any] ... | 245 |
def __lowercase ( _A ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
UpperCAmelCase__ : Optional[int] = int(input("""Ente... | 245 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : str = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec... | 245 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __lowercase ( _A , _A , _A ) -> int:
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Маши... | 245 | 1 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 245 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCAmelCase__ : List[str] = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 245 |
# 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 requ... | 245 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common imp... | 245 |
import sys
from collections import defaultdict
class a__ :
"""simple docstring"""
def __init__( self : Union[str, Any] ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ... | 245 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
UpperCAmelCase__ : List[str] = logging.get_logger(__name... | 245 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if ... | 245 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 245 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowercase ( _A ) -> List[Tuple[int, ...]]:
SCREAMING_SNAK... | 245 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __lowercase ( _A , _A=None ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE : Tuple = None
if token i... | 245 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall... | 245 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __lowercase ( _A , _A , _A = None ) -> str:
if version.parse(hfh.__version__ ).release < version.parse("""0.11.0""" ).release:
# old ver... | 245 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 245 | 1 |
import os
import pytest
from attr import dataclass
UpperCAmelCase__ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class a__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : Union[str, ... | 245 |
import os
import pytest
from attr import dataclass
UpperCAmelCase__ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class a__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : Union[str, ... | 245 | 1 |
def __lowercase ( _A , _A ) -> bool:
SCREAMING_SNAKE_CASE : str = len(_A ) + 1
SCREAMING_SNAKE_CASE : int = len(_A ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches wi... | 245 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : Tuple = TypeVar("""T""")
class a__ ( Generic[T] ):... | 245 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 245 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : List[Any] = logging.get_logger... | 245 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __lowercase ( _A , _A , _A , _A=1024 ) -> str:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = [], []
SCREAM... | 245 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ : List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath... | 245 | 1 |
from manim import *
class a__ ( UpperCAmelCase ):
"""simple docstring"""
def _lowercase ( self : Tuple ) ->int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = R... | 245 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..t... | 245 | 1 |
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 tokenize... | 245 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 245 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_f... | 245 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase__ : Optional[int] ) ->str:
"""simple docstring"""
... | 245 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ : Optional[Any] = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas"... | 245 |
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 __lowercase ( _A , _A ... | 245 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSe... | 245 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCAmelCase__ ... | 245 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_... | 245 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
Random... | 245 | 1 |
# 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 requ... | 245 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 245 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : Union[str, Any] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""... | 245 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[Any] ) ->Optional[int]:
"""simple docstri... | 245 | 1 |
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