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 |
|---|---|---|---|---|
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging... | 77 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
... | 77 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
... | 77 |
# 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 by app... | 77 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
__A : Any = get_activation('swish' )
self.asse... | 77 |
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 : Dic... | 77 | 1 |
import math
def _SCREAMING_SNAKE_CASE ( a , a = 0 , a = 0 ) -> list:
__A : Union[str, Any] = end or len(a )
for i in range(a , a ):
__A : Union[str, Any] = i
__A : List[Any] = ... | 77 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class _A( snake_case__ ):
"""simple docstring"""
def __init__( self , *_A , **_A ):
... | 77 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
UpperCAmelCase : Optional[Any] ... | 77 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 | 1 |
import requests
UpperCAmelCase : Dict = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def _SCREAMING_SNAKE_CASE ( a ) -> None:
# fetching a list of articles in json format
__A : Any = requests.get(_NEWS_API + bbc_... | 77 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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 import ConfigTester
fr... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> int:
if len(a ) != len(a ):
raise ValueError('String lengths must match!' )
__A : Tuple = 0
for chara, chara in zip(a , a ):
if chara != chara:
count ... | 77 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : str = HfApi()
UpperCAmelCase : List[str] = {}
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1... | 77 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, P... | 77 |
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> np.ndarray:
__A : Union[str, Any] = np.array(a )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' ... | 77 | 1 |
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 ModelTesterMixin, ids_tensor
f... | 77 |
from __future__ import annotations
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( a , a , a , a = 1_00 , ) -> float:
__A : Any = x_start
__A : List[str] = fnc(a )
__A : Optional[Any] ... | 77 | 1 |
from __future__ import annotations
UpperCAmelCase : Tuple = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
UpperCAmelCase : List[Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _SCREAMING_SNAKE_CASE ( a ) -> list[float... | 77 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ) -> None:
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print(... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a , a , a , a , a ) -> List[Any]:
if index == r:
for j in range(a ):
print(data[j] , end=' ' )
print(' ' )
return
# When no more elements are there to p... | 77 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, P... | 77 | 1 |
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 .tokenization_big_bird im... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelC... | 77 | 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 by app... | 77 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
debug_launcher(test_script.main )
de... | 77 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( a , a ) -> Optional[Any]:
# Checks if the entire collection has been sorted
if len(a ) <= 1 or n <= 1:
return
insert_next(a , n - 1 )
rec_insertion_sort(a , n - 1... | 77 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 77 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 77 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
loggi... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( ) -> int:
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(a , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() = ... | 77 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _A( snake_case__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( _A ):
raise NotImplementedError()
@abstractmethod
def UpperCAmelCas... | 77 | 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_format,
)
... | 77 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Optional[int] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''U... | 77 | 1 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
fro... | 77 |
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_numpy, ... | 77 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, an... | 77 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a ) -> list:
if len(a ) != 2 or len(a[0] ) != 2 or len(a ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__A : Optional[int] ... | 77 | 1 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import to... | 77 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : List[str] = []
__A : Tuple = []
__A : Union[str, Any] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priori... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : List[Any] = [1]
__A , __A , __A : Union[str, Any] = 0, 0, 0
__A : Optional[int] = ugly_nums[ia] * 2
__A : Any = ugly_nums[ia] * 3
... | 77 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase : Tuple = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/... | 77 | 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 by app... | 77 |
import copy
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[str] = logging.get_log... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> str:
__A : int = len(a )
__A : int = len(a )
__A : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
__A :... | 77 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
... | 77 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
UpperCAmelCase : List[str] = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],... | 77 |
# 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 by app... | 77 | 1 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _SCREAMING_SNAKE_CASE ( a , a , a , a=5 ) -> Dict:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input... | 77 |
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 : Dic... | 77 | 1 |
# Algorithm for the pigeonhole sorting
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : Dict = min(a ) # min() finds the minimum value
__A : str = max(a ) # max() finds the maximum value
__A : str = max_... | 77 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class _A( snake_case__ ):
"""simple docstring"""
def __init__( self , *_A , **_A ):
... | 77 | 1 |
import requests
from bsa import BeautifulSoup
def _SCREAMING_SNAKE_CASE ( a = "AAPL" ) -> str:
__A : int = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__A : Any = BeautifulSoup(requests.get(a ).text , 'html.pa... | 77 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils imp... | 77 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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 import ConfigTester
fr... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> int:
while a != 0:
__A , __A : str = b % a, a
return b
def _SCREAMING_SNAKE_CASE ( a , a ) -> int:
if gcd(a , a ) != 1:
__A : int ... | 77 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : str = HfApi()
UpperCAmelCase : List[str] = {}
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1... | 77 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
debug_launcher(test_script.main )
de... | 77 |
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> np.ndarray:
__A : Union[str, Any] = np.array(a )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' ... | 77 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCAmelCase : str = pd.read_csv('''sample_data.csv''', header=None)
UpperCAmelC... | 77 |
from __future__ import annotations
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( a , a , a , a = 1_00 , ) -> float:
__A : Any = x_start
__A : List[str] = fnc(a )
__A : Optional[Any] ... | 77 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavL... | 77 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ) -> None:
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print(... | 77 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase : List[str] = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
Upper... | 77 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, P... | 77 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _SCREAMING_SNAKE_CASE ( a = 8 ) -> str:
__A : Union[str, Any] = ascii_letters + digits + punctuation
return "".join(secret... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelC... | 77 | 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 import Mode... | 77 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
debug_launcher(test_script.main )
de... | 77 | 1 |
from __future__ import annotations
import time
UpperCAmelCase : Optional[int] = list[tuple[int, int]]
UpperCAmelCase : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
... | 77 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> str:
__A : List[str] = ''
for word_or_phrase in separated:
if not isinstance(a , a ):
raise Exception('join() accepts only strings to be joined' )
joined += word_or_ph... | 77 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
loggi... | 77 | 1 |
from ..utils import DummyObject, requires_backends
class _A( metaclass=snake_case__ ):
"""simple docstring"""
UpperCamelCase : Optional[Any] = ['''torch''']
def __init__( self , *_A , **_A ):
requires_backends(self , ['torch'] ... | 77 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _A( snake_case__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( _A ):
raise NotImplementedError()
@abstractmethod
def UpperCAmelCas... | 77 | 1 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : Any = logging.get_logger(__name__)
class _A( snake_case__ ):
"""simple docstring"""
UpperCame... | 77 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Optional[int] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''U... | 77 | 1 |
import pytest
UpperCAmelCase : int = '''__dummy_dataset1__'''
UpperCAmelCase : Union[str, Any] = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.js... | 77 |
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_numpy, ... | 77 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
'''... | 77 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a ) -> list:
if len(a ) != 2 or len(a[0] ) != 2 or len(a ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__A : Optional[int] ... | 77 | 1 |
from typing import Any
class _A:
"""simple docstring"""
def __init__( self , _A ):
__A : str = data
__A : Union[str, Any] = None
class _A:
"""simple docstring"""
def __init__( self ):
__A : Dict ... | 77 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : List[str] = []
__A : Tuple = []
__A : Union[str, Any] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priori... | 77 | 1 |
UpperCAmelCase : str = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : Tuple = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
__A : Stack[int] = ... | 77 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase : Tuple = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/... | 77 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import 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_available():
import t... | 77 |
import copy
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[str] = logging.get_log... | 77 | 1 |
import warnings
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_axis_dime... | 77 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
... | 77 | 1 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutpu... | 77 |
# 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 by app... | 77 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _A( nn.Module ):
"""simple docstring"""
def __init__( self , _A = 16 , _A = 88 , _A = None , _A = 1 , _A = 0.0 , _A =... | 77 |
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 : Dic... | 77 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 77 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class _A( snake_case__ ):
"""simple docstring"""
def __init__( self , *_A , **_A ):
... | 77 | 1 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : str = HfApi()
UpperCAmelCase : List[str] = {}
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1... | 77 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class _A( snake_case__ , snake_case__... | 77 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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 import ConfigTester
fr... | 77 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepie... | 77 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : str = HfApi()
UpperCAmelCase : List[str] = {}
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1... | 77 | 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 by app... | 77 |
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> np.ndarray:
__A : Union[str, Any] = np.array(a )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' ... | 77 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : int = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerC... | 77 |
from __future__ import annotations
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( a , a , a , a = 1_00 , ) -> float:
__A : Any = x_start
__A : List[str] = fnc(a )
__A : Optional[Any] ... | 77 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ) -> None:
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print(... | 77 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
... | 77 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, P... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> int:
return number | (1 << position)
def _SCREAMING_SNAKE_CASE ( a , a ) -> int:
return number & ~(1 << position)
def _SCREAMING_SNAKE_CASE ( a , a ) -> int:
r... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelC... | 77 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : List... | 77 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
debug_launcher(test_script.main )
de... | 77 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _SCREAMING_SNAKE_CASE ( a ) -> List[str]:
__A : Tuple = [
'encoder.version',
'decoder.version',
'model.encod... | 77 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 77 | 1 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _SCREAMING_SNAKE_CASE ( a , a ... | 77 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
loggi... | 77 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( a , a , a , a ) -> Optional[int]: # noqa: E741
while r - l > 1:
__A : int = (l + r) // 2
if v[m] >= key:
__A : str = m
... | 77 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _A( snake_case__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( _A ):
raise NotImplementedError()
@abstractmethod
def UpperCAmelCas... | 77 | 1 |
from __future__ import annotations
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( a , a , a , a = 1_00 , ) -> float:
__A : Any = x_start
__A : List[str] = fnc(a )
__A : Optional[Any] ... | 77 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Optional[int] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''U... | 77 | 1 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_EN... | 77 |
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_numpy, ... | 77 | 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( unittest.TestCase... | 77 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a ) -> list:
if len(a ) != 2 or len(a[0] ) != 2 or len(a ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__A : Optional[int] ... | 77 | 1 |
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 .tokenization_camembert i... | 77 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : List[str] = []
__A : Tuple = []
__A : Union[str, Any] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priori... | 77 | 1 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
... | 77 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase : Tuple = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/... | 77 | 1 |
import sys
def _SCREAMING_SNAKE_CASE ( a ) -> Dict:
__A : List[Any] = len(a )
__A : int = [[0 for x in range(a )] for x in range(a )]
__A : Any = [[0 for x in range(a )] for x in range(a )]
... | 77 |
import copy
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[str] = logging.get_log... | 77 | 1 |
import math
def _SCREAMING_SNAKE_CASE ( a = 1_00 ) -> int:
__A : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
__A : Optional[int] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
... | 77 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
... | 77 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_test... | 77 |
# 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 by app... | 77 | 1 |
from __future__ import annotations
UpperCAmelCase : int = []
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> bool:
for i in range(len(a ) ):
if board[row][i] == 1:
return False
for i in range(len(a ) ):
... | 77 |
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 : Dic... | 77 | 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, load_numpy, slo... | 77 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class _A( snake_case__ ):
"""simple docstring"""
def __init__( self , *_A , **_A ):
... | 77 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _A( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase :... | 77 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 | 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()
def _SCREAMING_... | 77 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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 import ConfigTester
fr... | 77 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 77 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : str = HfApi()
UpperCAmelCase : List[str] = {}
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1... | 77 | 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__)
Upp... | 77 |
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> np.ndarray:
__A : Union[str, Any] = np.array(a )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' ... | 77 | 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_bert import BertTokenizer
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelC... | 77 |
from __future__ import annotations
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( a , a , a , a = 1_00 , ) -> float:
__A : Any = x_start
__A : List[str] = fnc(a )
__A : Optional[Any] ... | 77 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForced... | 77 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ) -> None:
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print(... | 77 | 1 |
UpperCAmelCase : List[Any] = 0 # The first color of the flag.
UpperCAmelCase : str = 1 # The second color of the flag.
UpperCAmelCase : List[Any] = 2 # The third color of the flag.
UpperCAmelCase : Optional[Any] = (red, white, blue)
def _SC... | 77 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, P... | 77 | 1 |
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 ..test_modeli... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelC... | 77 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class _A:
"""simple docstring"""
def __init__( self , _A ):
__A : Tuple = str(id_ )
__A : Optional[int] = None
__A : List[str] = None
... | 77 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
debug_launcher(test_script.main )
de... | 77 | 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_numpy, ... | 77 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 77 | 1 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase : List[Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
de... | 77 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
loggi... | 77 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from t... | 77 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _A( snake_case__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( _A ):
raise NotImplementedError()
@abstractmethod
def UpperCAmelCas... | 77 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor imp... | 77 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Optional[int] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''U... | 77 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Optional[int] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''U... | 77 |
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_numpy, ... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> bool:
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__A : List[Any] = 4
__A : Any = (1 << p) - 1
for _ in range(p - 2 ):
... | 77 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a ) -> list:
if len(a ) != 2 or len(a[0] ) != 2 or len(a ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__A : Optional[int] ... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> list:
__A : Union[str, Any] = len(a )
for _ in range(a ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__A , __A : Tuple ... | 77 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
__A : List[str] = []
__A : Tuple = []
__A : Union[str, Any] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priori... | 77 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCAmelCase : str = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation an... | 77 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase : Tuple = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/... | 77 | 1 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase : Any = 6378137.0
UpperCAmelCase : str = 6356752.314245
UpperCAmelCase : Tuple = 6_37_81_37
def _SCREAMING_SNAKE_CASE ( a , a ... | 77 |
import copy
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[str] = logging.get_log... | 77 | 1 |
from __future__ import annotations
UpperCAmelCase : int = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0... | 77 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
... | 77 | 1 |
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> np.ndarray:
__A : Union[str, Any] = np.array(a )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' ... | 77 |
# 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 by app... | 77 | 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 required b... | 77 |
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 : Dic... | 77 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
... | 77 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class _A( snake_case__ ):
"""simple docstring"""
def __init__( self , *_A , **_A ):
... | 77 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-... | 77 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : Union[str, Any] = 1 # (0 is vert... | 77 | 1 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 77 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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 import ConfigTester
fr... | 77 | 1 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 77 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : str = HfApi()
UpperCAmelCase : List[str] = {}
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> float:
__A : str = 0
while len(a ) > 1:
__A : Optional[Any] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
__A : List[str] ... | 77 |
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> np.ndarray:
__A : Union[str, Any] = np.array(a )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' ... | 77 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> str:
__A : list[list[str]] = [[] for _ in range(a )]
__A : Optional[Any] = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 o... | 77 |
from __future__ import annotations
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( a , a , a , a = 1_00 , ) -> float:
__A : Any = x_start
__A : List[str] = fnc(a )
__A : Optional[Any] ... | 77 | 1 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a ) -> list:
if len(a ) != 2 or len(a[0] ) != 2 or len(a ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__A : Optional[int] ... | 77 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ) -> None:
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print(... | 77 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Tuple = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 77 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, P... | 77 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase : Union[str, Any] = {
'''configuration_owlvi... | 77 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelC... | 77 | 1 |
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