code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
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.modelin... | 711 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( ) -> list[list[int]]:
return [list(range(1000 - i, -1000 - i, -1 ) ) for i in range(1000 )]
_A : List[Any] =generate_large_matrix()
_A : Optional[int] =(
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1,... | 712 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCamelCase ( ) -> None:
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ... | 713 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import j... | 714 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 0 |
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
_A : Union[str, Any] =logging.getLogger()
def ... | 715 |
'''simple docstring'''
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,
Bert... | 4 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 716 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils im... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 0 |
'''simple docstring'''
import math
def __UpperCamelCase ( _lowercase, _lowercase ) -> float:
if (
not isinstance(_lowercase, (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and ... | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 0 |
import math
def __UpperCamelCase ( _lowercase ) -> int:
if not isinstance(_lowercase, _lowercase ):
_lowercase : Any = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowercase )
if number < 1:
_lowercase : Optio... | 719 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axi... | 720 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identifie... | 721 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> List[Any]:
'''simple docstring'''
_lowercase : List[Any] = min(_lowercase ) # min() finds the minimum value
_lowercase : List[str] = max(_lowercase ) # max() finds the maximum val... | 701 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 0 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __UpperCamelCase ( _lowercase ) -> Union[str, Any]:
return x + 2
class lowerCamelCase__ ( unittest.TestCase ):
'''simple do... | 702 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 0 |
'''simple docstring'''
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_chan... | 703 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 0 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
... | 704 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_A : List[str] ={
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''... | 705 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 0 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def __UpperCamelCase ( _lowercase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all eve... | 706 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_A : Optional[int] =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa... | 707 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 0 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_A : int =pytest.mark.integration
@pytest.mark.parametrize('path... | 708 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def __UpperCamelCase ( _lowercase, _lowercase ) -> Union[str, Any]:
_lowercase : int ... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 0 |
'''simple docstring'''
import operator as op
def __UpperCamelCase ( _lowercase ) -> Optional[int]:
_lowercase : Optional[Any] = []
_lowercase : Any = lambda _lowercase, _lowercase : int(x / y ) # noqa: E731 integer division operation
_... | 710 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _lowercase ... | 711 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_A : Union[str, Any] ={
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'... | 712 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 0 |
'''simple docstring'''
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... | 713 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _lowercase, _lowercase ... | 714 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase = 1000 ) -> int:
_lowercase : Dict = -1
_lowercase : Optional[int] = 0
for a in range(1, n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowercase : ... | 715 |
'''simple docstring'''
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,
Bert... | 4 | 0 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_A : int =(
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS ... | 716 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_A : Optional[Any] =TypeVar('''T''')
def __UpperCamelCase ( _lowercase ) -> int:
return (position - 1) // 2
def __UpperCamelCase ( ... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 0 |
'''simple docstring'''
import numpy as np
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase = 1E-1_2, _lowercase = 100, ) -> tuple[float, np.ndarray]:
assert np.shape(_lowercase )[0] == np.shape(_lowercase )[1]
# Ensure proper dimensionality.
assert np.shape(_... | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A )
class lowerCamelCase__ ( A ):
'''simple docstring'''
A_ = field(default="""summarization""" , ... | 719 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A : Any ={
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitCo... | 720 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 0 |
'''simple docstring'''
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 721 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> List[str]:
_lowercase : str = [False] * len(_lowercase )
_lowercase : Dict = [-1] * len(_lowercase )
def dfs(_lowercase, _lowercase ):
_lowercase : Dict = True
... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils im... | 701 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : List[str] =logging.get_logger(__name__)
_A : int ={
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large'... | 702 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINE... | 703 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_A : Optional[Any] =logging.get_... | 704 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 0 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase__ ( tf.keras.layers.Layer ):
... | 705 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 0 |
'''simple docstring'''
import os
import sys
import unittest
_A : Tuple =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to... | 706 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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 .tokenizat... | 707 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 0 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp... | 708 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 0 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __UpperCamelCase ( _lowercase, _lowerca... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipel... | 710 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 711 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .toke... | 712 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_... | 713 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transfo... | 714 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cud... | 715 |
'''simple docstring'''
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,
Bert... | 4 | 0 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
_A : int ='''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavat... | 716 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 0 |
'''simple docstring'''
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
_A : Any ='''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
_... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 0 |
from typing import Any
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : str , UpperCamelCase_ : Any ) -> Union[str, Any]:
'''simple docstring'''
_lowercase : Optional[int] = data
_lowercase : U... | 719 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class lowerCamelCase__ ( unittest.TestCase ... | 720 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 0 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_A : int ='''examples/'''
_A : Optional[int] ={
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.... | 721 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 0 |
'''simple docstring'''
import math
def __UpperCamelCase ( _lowercase, _lowercase ) -> int:
'''simple docstring'''
_lowercase : Dict = len(_lowercase )
_lowercase : Any = int(math.floor(math.sqrt(_lowercase ) ) )
_lowercase ... | 701 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( A ):
'''simple docstring'''
A_ = (DDIMParallelScheduler,)
A_ = (("""eta""", 0.0), ("""num_inference_steps""", 50))... | 702 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 0 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_at... | 703 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCamelCase ( _lowercase, _lowercase ) -> float:
_lowercase : str = u
for i in range(1, _lowercase ):
_lowercase : str = temp * (u - i)
return temp
... | 704 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> float:
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_lowercase ) * abs(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod(v... | 705 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __UpperCamelCase ( ) -> Any:
_lowercase : int = ArgumentParser(
description=(
'... | 706 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> ... | 707 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 0 |
'''simple docstring'''
import unittest
from transformers import 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 import ModelTes... | 708 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __UpperCamelCase ( ) -> List[str]:
_lowercase : Optional[int] = 9, 14 # noqa: F841
_lowercase : int = [
[0, 1, 4],... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 0 |
'''simple docstring'''
_A : Dict ={
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
1_0: '''a''',
1_1: '''b''',
1_2: '''c''',
1_3: '''d''',
1_4: '''e''',
1_5: '... | 710 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> int:
if not isinstance(_lowercase, _lowercase ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
divisor for divisor in range(1, ... | 711 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuratio... | 712 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
'''simple docstring'''
A_ = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *UpperCamelCase_ : Tuple , ... | 713 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 0 |
'''simple docstring'''
import qiskit
def __UpperCamelCase ( _lowercase, _lowercase ) -> qiskit.result.counts.Counts:
_lowercase : Tuple = qiskit.Aer.get_backend('aer_simulator' )
_lowercase : Optional[Any] = qiskit.QuantumCircuit(4, 2 )... | 714 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 0 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 715 |
'''simple docstring'''
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,
Bert... | 4 | 0 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust... | 716 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( ) -> int:
return 1
def __UpperCamelCase ( _lowercase ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __UpperCamelCase ( _lowercase ) -> int:
return 0 if x < 0 else five... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 0 |
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : Dict =[{'''typ... | 719 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_availa... | 720 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_comm... | 721 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A ... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 0 |
'''simple docstring'''
from collections import defaultdict
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str] ) -> int:
'''simple docstring'''
_lowerc... | 701 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 0 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 702 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_A : Tuple =1.0_54_57_18_17e-34 # unit of ℏ : J * s
_A : str =3e8 # unit of c : m * s^-1
def ... | 703 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Union[str, Any] ={
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip... | 704 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 705 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 0 |
'''simple docstring'''
import math
import os
import sys
def __UpperCamelCase ( _lowercase ) -> str:
_lowercase : Dict = ''
try:
with open(_lowercase, 'rb' ) as binary_file:
_lowercase : List[Any] = binary_file.read()
for dat in data:
_low... | 706 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_A : Union[str, Any] ={
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''Patch... | 707 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 0 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __UpperCamelCase ( ) -> List[str]:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import d... | 708 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 0 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 0 |
'''simple docstring'''
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__ ( ... | 710 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 0 |
'''simple docstring'''
from collections import deque
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Any , UpperCamelCase_ : str , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> None:
'''simple docstring'''
... | 711 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 0 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_A : Optional[Any] =[
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''laye... | 712 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_A : str ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''t... | 713 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_A : Tuple =datasets.logging.get_logger(__name__)
_A : Optional[Any] ='''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for T... | 714 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 0 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_... | 715 |
'''simple docstring'''
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,
Bert... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 716 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int =logging.get_logger(__name__)
_A : Optional[int] ={
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# S... | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : Optional[int] =logging.get_logger(__name__)
_A : Optional[int] ={
'''bert-base-uncased''': '''ht... | 719 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A : int ={
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_av... | 720 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_A : List[Any] =2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of t... | 721 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 0 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( _A : str ):
"""simple docstring"""
return x + 2
class _lowercase ( unittest.TestCase):
... | 5 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 5 | 1 |
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