code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 0 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 81 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 0 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_c... | 82 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 0 |
"""simple docstring"""
from torch import nn
def snake_case_ ( A_ : int ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
retu... | 83 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 0 |
import os
def UpperCAmelCase_ ( ):
lowercase = os.path.join(os.path.dirname(__SCREAMING_SNAKE_CASE ) , 'num.txt' )
with open(__SCREAMING_SNAKE_CASE ) as file_hand:
return str(sum(int(__SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]
if __name__ == "__mai... | 84 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import F... | 86 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = JukeboxTokenizer
UpperCAmelCase__ = {
'''artist''': '''Z... | 87 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 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 _snake_case ( __snake_case : List[s... | 88 | 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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
_lowercase : Any = len(bin(lowerCamelCase_ )[3:] )
_lowercase : List[Any] = bin(abs(lowerCamelCase_ ) - (1 << binar... | 89 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stable... | 85 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_mo... | 90 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm... | 91 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 0 |
'''simple docstring'''
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_a... | 92 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 0 |
"""simple docstring"""
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(... | 93 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 0 |
'''simple docstring'''
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... | 94 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 0 |
"""simple docstring"""
def snake_case ( A__ = 50 ):
UpperCAmelCase_ : Any = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += wa... | 95 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : int = 2_0_0_0_0_0_0 ) -> int:
__magic_name__: str = [0 for i in range(n + 1 )]
__magic_name__: Tuple = 1
__magic_name__: Dict = 1
for i in range(2 ... | 96 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',... | 97 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 0 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentPa... | 98 | import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDepe... | 99 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 0 |
def __snake_case ( lowerCAmelCase_ = 1_0_0 ) -> int:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ ... | 100 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
fr... | 101 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 102 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> float:
return round(float(moles / volume ) * nfactor )
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> float:
r... | 103 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
... | 104 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ : Tuple = 10
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCa... | 105 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCAmelCase__ : list[int] ) -> list[int]:
'''simple docstring'''
if len(lowerCAmelCase__ ) == 0:
return array
A , A = min(lowerCAmelCase__ ), max(lowerCAmelCas... | 106 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : str = {
'''configuration_vision_encoder_decoder''': ['''VisionEnco... | 107 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 0 |
import numpy as np
__a: List[str] = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
cl... | 108 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@data... | 109 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not ... | 110 | 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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int , A__ : float , A__ : float ):
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def UpperCamelCase_ ( A__ : float , ... | 275 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stable... | 85 | 0 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_im... | 336 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf i... | 505 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_lowercase : Any =logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ... | 364 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixi... | 116 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_imag... | 615 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"google/bigbird-roberta-base": "https... | 7 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 0 |
def _a ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> Any:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def _a ( ) -> Optional[int]:
'''simple docstring'''
... | 663 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 0 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
_SCREAMING_SNAKE_CASE : Optional[int] = {
"linear": PI... | 549 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 0 |
"""simple docstring"""
from ....utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE( UpperCamelCase_ ):
def __init__( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__=None ,SCREAMING_SNAKE_C... | 498 | import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensi... | 286 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[int] = {"configuration_encoder_decoder": ["EncoderDecod... | 275 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Any = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "... | 336 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from tr... | 505 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
... | 364 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowercase : List[str] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 ... | 116 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 0 |
from collections.abc import Sequence
from queue import Queue
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , __lowerCamelCase : Tuple , __lowerCamelCase : str , __lowerCamelCase : Union[str, Any] , __lowerCamelCase... | 615 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 0 |
"""simple docstring"""
import os
def _snake_case ( _snake_case : str = "input.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) as input_file:
_A = [
[int(low... | 7 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCame... | 663 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteSchedu... | 549 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing imp... | 498 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklea... | 286 | 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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( A__ : list ):
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(lowercase__ ) / len(lowercase__ )
if __name__ == ... | 275 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stable... | 85 | 0 |
from heapq import heappop, heappush
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ) -> Tuple:
lowercase : Union[str, Any] = grid.shape
lowercase : Opt... | 336 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase (_snake_case ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
... | 505 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
... | 364 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 0 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCamelCase__ ( __lowercase = 3 ):
if isinstance(lowercase__ , lowercase__ ):
... | 116 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 0 |
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 615 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( _snake_case : int ) -> str:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number %... | 7 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 663 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 0 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transfor... | 549 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _SCREAMING_S... | 498 | import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 0 |
'''simple docstring'''
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 AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing... | 286 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 0 |
'''simple docstring'''
from __future__ import annotations
import bisect
def UpperCamelCase_ ( A__ : list[int] , A__ : int , A__ : int = 0 , A__ : int = -1 ):
'''simple docstring'''
if hi < 0:
lowerCAmelCa... | 275 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
lowercase : Any = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _snake_case( SCREAMING_SNAKE_CASE__ = 5_000 ) -> int:
lowercase : Any = [(i * (3 * i - 1)) /... | 336 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_snake_case ) -> Dict:
'''simple docstring'''
__UpperCamelCase = [True] * limit
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = True... | 505 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 0 |
from typing import Any
import numpy as np
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
return np.array_equal(lowercase__ ,matrix.conjugate().T )
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ : Tuple = ... | 364 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 0 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase ):
snake_case : Optional[Any] = [0] * len(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
# use last results for better performance - dynamic programming
... | 116 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__magic_name__ : List[str] = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__mag... | 615 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 0 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
a = 4
a = 3
class lowercase_ ( UpperCamelCase_... | 7 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _a ( SCREAMING_SNAKE_CASE_... | 663 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_m... | 549 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaPro... | 498 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 0 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A__ ... | 286 | 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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : str = {
"google/pix2struct-textcaps-base":... | 275 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stable... | 85 | 0 |
from __future__ import annotations
from typing import Any
class __snake_case :
def __init__( self ,snake_case ,snake_case ,snake_case = 0 ):
'''simple docstring'''
lowercase : str = row, column
lowercase : List[str] = [[defau... | 336 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 0 |
"""simple docstring"""
from statistics import mean
import numpy as np
def lowercase (_snake_case ,_snake_case ,_snake_case ,_snake_case ) -> Any:
'''simple docstring'''
__UpperCamelCase = 0
# Number of processes finished
__UpperCamelCase = 0
# Display... | 505 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 0 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_lowercase : Any =logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ( ):
lowerCamelCase_ : Optional[int] = argparse.ArgumentParser... | 364 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from... | 116 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ (UpperCamelCase_ ):
def __init__( self : Optional[Any] , __lowerCamelCase : List[str] , __lowerCamelCase : List[str] ):
... | 615 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 0 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_avail... | 7 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : complex , SCREAMING_SNAKE_CASE__ : str = "x" , SCREAMING_SNAKE_CASE__ : float = 10**-10 , SCREAMIN... | 663 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 0 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_... | 549 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {"configuration_fnet": ["FNET_PRETRAINED_CON... | 498 | import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
A__ : List[Any] = ""
A__ : Optional[int] = ""
A__ : Optional[Any] = ""
A__ : int = 1 # (0 is vertical, 1 is horizontal)
... | 286 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 0 |
'''simple docstring'''
from __future__ import annotations
import requests
def UpperCamelCase_ ( A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pre... | 275 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 0 |
from PIL import Image
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
lowercase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(SCREAMING_SNAKE_CASE__ ) -> int:
return int(128 + fa... | 336 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
_A = tuple[int, int]
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , A_ : set[int] , A_ : Mapping[EdgeT, int] )-> None:... | 505 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
for i in range(1 ,len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ,len(lowercase__ ) ):
matrix[i][... | 364 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a (UpperCamelCase_, unittest.TestCase ):
... | 116 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__magic_name__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE... | 615 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : Union[tf.Tensor, np.ndarray] ) -> int:
'''simple docstrin... | 7 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import ... | 663 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 0 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noq... | 549 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 0 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowerCamelCase_ = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE:
SCREAMING_SNAKE_CASE_ : str ... | 498 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 0 |
'''simple docstring'''
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
A__ : Dict = logging.get_logger(__name__)
A__ : Any ... | 286 | 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
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaud... | 275 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stable... | 85 | 0 |
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_available, is_vision_available
f... | 336 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 0 |
"""simple docstring"""
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_o... | 505 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Any ={
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_t... | 364 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 0 |
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