code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You c... | 320 | 1 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_a = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_... | 320 |
"""simple docstring"""
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 c... | 320 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion impor... | 320 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 320 | 1 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
fro... | 320 |
"""simple docstring"""
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
... | 320 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__snake_case = logging.g... | 320 |
"""simple docstring"""
import os
import sys
import unittest
__snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_t... | 320 | 1 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__snake_case = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1)... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
import re
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
if len(re.findall('''[ATCG]''', _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketran... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : str ):
"""simple docstring"""
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''String lengths must match!''' )
_a = 0
for chara, chara... | 320 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
def wrapper(*_lowerCAmelCase : Unio... | 320 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unisp... | 320 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : str = ['keras_nlp']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
r... | 320 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 320 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : Tuple = ['image_processor', 'tokenizer']
A_ : Union[str, Any] = 'AutoImageProc... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIV... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : list, _lowerCAmelCase : list, _lowerCAmelCase : int ):
"""simple docstring"""
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''The length of profit and weight must be sa... | 320 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@require_torch
def _UpperCA... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You c... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
... | 320 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A_ ( ):
"""simple docstring"""
_a , _a = 9, 14 # noqa: F841
_a = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8]... | 320 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__snake_case = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 320 | 1 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .... | 320 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_a = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_... | 320 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__snake_case = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle ... | 320 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization... | 320 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__snake_case = None
try:
import msvcrt
except ImportError:
__snake_case = None
try:
import fcntl
except ImportError:
__snake_case = None
# Backward com... | 320 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gy... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__snake_case = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__snake_case = [ord(letter) for letter in string.ascii_lowerca... | 320 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unisp... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 320 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from trans... | 320 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell p... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
'''config... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelC... | 320 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
f... | 320 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You c... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def A_ ( _lowerCAmelCase : list[float] ):
"""simple docstring"""
return np.maximum(0, _lowerCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 320 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(... | 320 | 1 |
"""simple docstring"""
import math
def A_ ( ):
"""simple docstring"""
_a = input('''Enter message: ''' )
_a = int(input(f'Enter key [2-{len(_lowerCAmelCase ) - 1}]: ' ) )
_a = input('''Encryption/Decryption [e/d]: ''' )
if mode.lower().... | 320 |
"""simple docstring"""
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 c... | 320 | 1 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell p... | 320 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : int, _lowerCAmelCase : list[list[int]] ):
"""simple docstring"""
def update_area_of_max_square(_lowerCAmelCase : int, _lowerCAmelCase : int ) -> int:
# BASE CA... | 320 |
"""simple docstring"""
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
... | 320 | 1 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __lowerCamelCase ( a__ ):
'... | 320 |
"""simple docstring"""
import os
import sys
import unittest
__snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_t... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def A_ ( _lowerCAmelCase : int = 1_00 ):
"""simple ... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__snake_case = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
... | 320 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : int ):
"""simple docstring"""
_a = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_a = n - k
# Calculate C(n,k)
for i in r... | 320 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unisp... | 320 | 1 |
"""simple docstring"""
import math
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float ):
"""simple docstring"""
if (
not isinstance(_lowerCAmelCase, (int, float) )
or power_factor < -1
or power_factor > 1
):
ra... | 320 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 320 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def A_ ( _lowerCAmelCase : Dict ... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIV... | 320 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vi... | 320 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@require_torch
def _UpperCA... | 320 | 1 |
"""simple docstring"""
import argparse
__snake_case = '''docs/source/_static/js/custom.js'''
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
with open(_lowerCAmelCase, encoding='''utf-8''', newline='''\n''' ) as f:
_a = f.readlines... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
... | 320 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/reso... | 320 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__snake_case = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 320 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__snake_case = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : str ):
... | 320 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_a = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_... | 320 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common impo... | 320 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization... | 320 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 320 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gy... | 320 | 1 |
"""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 A_ ( _lowerCAmelCase : List[str], _lowerC... | 320 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 60_08_51_47_51_43 ):
"""simple docstring"""
try:
_a = int(_lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : List[Any], _lowerCAmelCase : List[Any], _lowerCAmelCase : Optional[int], _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : int ):
"""simple docstring"""
... | 320 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell p... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TrajectoryTran... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelC... | 320 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
Aut... | 320 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You c... | 320 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
... | 320 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(... | 320 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 |
"""simple docstring"""
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 c... | 320 | 1 |
"""simple docstring"""
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 c... | 320 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[float], _lowerCAmelCase : list[float] ):
"""simple docstring"""
_a = sorted(numsa + numsa )
_a , _a = divmod(len(_lowerCAmelCase ), 2 )
if m... | 320 |
"""simple docstring"""
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
... | 320 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__Upper... | 320 |
"""simple docstring"""
import os
import sys
import unittest
__snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_t... | 320 | 1 |
"""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_av... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
__snake_case = logging.get_logger(__name__)
__sn... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 1 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from... | 320 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def A_ ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
_a = Path(_lowerCAmelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_a = [
root /... | 320 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unisp... | 320 | 1 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__snake_case = datasets.logging.get_logger(__name__)
__snake_case = '''\
@InProceedings{moosavi2019minimum... | 320 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 320 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.uti... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIV... | 320 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_co... | 320 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@require_torch
def _UpperCA... | 320 | 1 |
"""simple docstring"""
import os
def A_ ( _lowerCAmelCase : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_lowerCAmelCase ), _lowerCAmelCase ) ) as input_file:
_a = [
[int(_lowerCAmelCase ) fo... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
... | 320 | 1 |
"""simple docstring"""
import re
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_a = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(_lowerCAmelCase, _lowerCAmelCase ):
return match.string == phone
r... | 320 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__snake_case = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 320 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__snake_case = logging.get_logger(__name__)
def ... | 320 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_a = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_... | 320 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __lowerCamelCase ( unittest.TestCase... | 320 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization... | 320 | 1 |
"""simple docstring"""
from PIL import Image
def A_ ( _lowerCAmelCase : Image, _lowerCAmelCase : int ):
"""simple docstring"""
_a = (2_59 * (level + 2_55)) / (2_55 * (2_59 - level))
def contrast(_lowerCAmelCase : int ) -> int:
return in... | 320 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gy... | 320 | 1 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_... | 320 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at htt... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
... | 320 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell p... | 320 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelC... | 320 | 1 |
"""simple docstring"""
import requests
__snake_case = '''YOUR API KEY'''
def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : str = giphy_api_key ):
"""simple docstring"""
_a = '''+'''.join(query.split() )
_a = f'https://api.giphy.com/v1/gifs/s... | 320 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You c... | 320 | 1 |
"""simple docstring"""
from functools import reduce
__snake_case = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''125406987471585238630507156932909632952274430... | 320 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(... | 320 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Tuple = ['transformers', 'torch', 'note_seq']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -... | 320 |
"""simple docstring"""
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 c... | 320 | 1 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''', [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', ''... | 320 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 320 | 1 |
"""simple docstring"""
__snake_case = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecut... | 320 |
"""simple docstring"""
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
... | 320 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import... | 320 |
"""simple docstring"""
import os
import sys
import unittest
__snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_t... | 320 | 1 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class __lowerCamelCase :
'''simple docstring'''
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None ) -> Union[str, Any]:
# Input as list
_a = list(p... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 1 |
"""simple docstring"""
from typing import Any
class __lowerCamelCase :
'''simple docstring'''
def __init__( self , __UpperCAmelCase ) -> Union[str, Any]:
_a = data
_a = None
class __lowerCamelCase :
'''simple docstring''... | 320 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 | 1 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case = HfApi()
__snake_case = {}
# fmt: off
__snake_case = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2... | 320 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unisp... | 320 | 1 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__snake_case = logging.getLogger()
@unittest.skip('Temporarily disable the... | 320 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 320 | 1 |
"""simple docstring"""
import os
def A_ ( ):
"""simple docstring"""
with open(os.path.dirname(_lowerCAmelCase ) + '''/p022_names.txt''' ) as file:
_a = str(file.readlines()[0] )
_a = names.replace('''"''', '''''' ).split(''',''' )
... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIV... | 320 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)... | 320 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@require_torch
def _UpperCA... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_avail... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : str ):
"""simple docstring"""
_a = [1]
for i in range(2, _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k... | 320 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__snake_case = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float ):
"""simple docstring"""
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(_lowerCAmelCase ) * abs(_lowerCAmelC... | 320 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_a = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_... | 320 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase, _lowerCAmelCase ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(_lowerCAmelCase, _lowerCA... | 320 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization... | 320 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''roberta-base''': '''https://h... | 320 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gy... | 320 | 1 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__snake_case = {
# 1536-bit
5: {
'''prime''': in... | 320 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__snake_case = {
'''facebook/maskformer-swin-base-ade'... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 320 | 1 |
"""simple docstring"""
import argparse
import json
import subprocess
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : Tuple ):
"""simple docstring"""
_a = []
_a = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Be... | 320 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell p... | 320 | 1 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : Dict = 'M-CLIP'
def __init__( self , __UpperCAmelCase=1024 , __UpperCAmelCase=768 , **__... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelC... | 320 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''junnyu/roformer_chinese_small... | 320 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You c... | 320 | 1 |
"""simple docstring"""
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate imp... | 320 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(... | 320 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 320 |
"""simple docstring"""
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 c... | 320 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 320 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXL... | 320 |
"""simple docstring"""
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
... | 320 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
... | 320 |
"""simple docstring"""
import os
import sys
import unittest
__snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_t... | 320 | 1 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : Dict = 'EncodecFeatureExtractor'
A_ : Union[str, Any]... | 320 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 1 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__snake_case = 637_8137.0
__snake_case = 635_6752.31_4245
__snake_case = 6378137
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _l... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCamelCase ( ... | 320 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : str = 'MCTCTFeatureExtractor'
A_ : Tuple = 'AutoTokenizer'
def __ini... | 320 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unisp... | 320 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDe... | 320 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 320 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, preci... | 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIV... | 320 | 1 |
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