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 |
|---|---|---|---|---|
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImageP... | 52 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_s... | 52 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test... | 52 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenizatio... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :int) -> int:
if not isinstance(a_ , a_):
raise TypeError('''only integers accepted as input''')
else:
__a : Optional[Any] = str(abs(a_))
__a : List[Any] = [list(a... | 52 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {'''configuration_opt''': ['''OPT_PRE... | 52 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''kakaobrain/alig... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
A = tuple[int, int]
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , ... | 52 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __A ( a_ :Tuple) -> List[str]:
return choice(a_)
def __A ( a_ :list[int] , a_ :int) -> int:
__a : Optional[int] = random_pivot(a... | 52 | 1 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
A = parse(importlib.metadata.version('''torch'''))
def __A ( a_ :Union[str, Version] , ... | 52 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAM... | 52 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
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_ba... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask... | 52 | 1 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transfo... | 52 |
"""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 = logging.get_logger(__name__)
A ... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :Tuple , a_ :Union[str, Any] , a_ :int=False) -> List[str]:
if isinstance(a_ , a_) and isinstance(a_ , a_):
__a : List[str] = len(set_a.intersection(a_))
if alternative... | 52 |
"""simple docstring"""
def __A ( a_ :Tuple , a_ :Union[str, Any] , a_ :int=False) -> List[str]:
if isinstance(a_ , a_) and isinstance(a_ , a_):
__a : List[str] = len(set_a.intersection(a_))
if alternative... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :Optional[int]) -> int:
__a : int = 0
__a : Optional[int] = len(a_)
for i in range(n - 1):
for j in range(i + 1 , a_):
if arr[i] > arr[j]:
num_inv... | 52 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
A = tuple[int, int]
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , ... | 52 | 1 |
"""simple docstring"""
from math import factorial
A = {str(digit): factorial(digit) for digit in range(10)}
def __A ( a_ :int) -> int:
if not isinstance(a_ , a_):
raise TypeError('''Parameter number must be int''')
if number < 0:
... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A = {'''configuration_glpn''': ['''GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GLPNConfig''']}
try:
if not is_vis... | 52 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __A ( a_ :Union[str, Any] , a_ :Union[str, Any] , a_ :Optional[Any] , a_ :Optional[int]=5) -> List[Any]:
# Adapted from https://github... | 52 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_s... | 52 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
__a : Optional[int] ... | 52 | 1 |
"""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_sentencepi... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {}
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__l... | 52 | 1 |
"""simple docstring"""
import os
import sys
import unittest
A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_fi... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 52 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int]) -> int:
if not nums:
return 0
__a : Any = nums[0]
__a : Optional[Any] = 0
for num in nums[1:]:
__a , __a : ... | 52 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase ... | 52 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 52 | 1 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
A = '''... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'''facebook/convnextv... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 52 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (DDPMScheduler,)
... | 52 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 52 |
"""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_to... | 52 | 1 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __A ( a_ :np.ndarray) -> np.ndarray:
__a , __a , __a : Dict = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7... | 52 |
"""simple docstring"""
from __future__ import annotations
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
__a , __a : List[Any] =... | 52 | 1 |
"""simple docstring"""
from itertools import permutations
def __A ( a_ :tuple) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__a : List... | 52 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
A = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(... | 52 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
A = '''\
'''
A = '''
Perplexity (PPL) is one of the most common ... | 52 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_s... | 52 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
... | 52 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenizatio... | 52 | 1 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def __A ( a_ :Any) -> List[Any]:
# getting number of pixels in the image
__a , __a : Dict = img.shape[0], img.shape[1]
# converting each pixel's color... | 52 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
A = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C''']... | 52 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''kakaobrain/alig... | 52 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __A ( a_ :str) -> int: # p... | 52 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __A ( a_ :Tuple) -> List[str]:
return choice(a_)
def __A ( a_ :list[int] , a_ :int) -> int:
__a : Optional[int] = random_pivot(a... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0 ):
... | 52 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAM... | 52 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
A = ''''''
A = ''''''
A = ''''''
A = 1 # (0 is vertical, 1 is horizontal)
def __A ( ) -> None:
__a , __a :... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask... | 52 | 1 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStrea... | 52 |
"""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 = logging.get_logger(__name__)
A ... | 52 | 1 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __A ( a_ :int) -> str:
__a : Any = np.max(a_ , axis=-1 , keepdims=a_)
__a : Optional[int] = np.exp(outputs - maxes)
return shifted_... | 52 |
"""simple docstring"""
def __A ( a_ :Tuple , a_ :Union[str, Any] , a_ :int=False) -> List[str]:
if isinstance(a_ , a_) and isinstance(a_ , a_):
__a : List[str] = len(set_a.intersection(a_))
if alternative... | 52 | 1 |
"""simple docstring"""
from manim import *
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
__a : Dict = Rectangle(height=0.5 , width=0.5 )
... | 52 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
A = tuple[int, int]
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , ... | 52 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''B... | 52 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __A ( a_ :Union[str, Any] , a_ :Union[str, Any] , a_ :Optional[Any] , a_ :Optional[int]=5) -> List[Any]:
# Adapted from https://github... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, ... | 52 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
__a : Optional[int] ... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :int , a_ :int) -> int:
return int((input_a, input_a).count(1) != 0)
def __A ( ) -> None:
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
as... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {}
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__l... | 52 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...on... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 52 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'''google/bit-50''': ... | 52 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int]) -> int:
if not nums:
return 0
__a : Any = nums[0]
__a : Optional[Any] = 0
for num in nums[1:]:
__a , __a : ... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __lowercase :
'''simple docstring'''
... | 52 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :int = 60_08_51_47_51_43) -> int:
try:
__a : Dict = int(a_)
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''')
if n <= 0:
raise Value... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'''facebook/convnextv... | 52 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copie... | 52 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (DDPMScheduler,)
... | 52 | 1 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __A ( a_ :Dataset , a_ :Dict[str, str]) -> int:
... | 52 |
"""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_to... | 52 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import... | 52 |
"""simple docstring"""
from __future__ import annotations
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
__a , __a : List[Any] =... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int] , a_ :int) -> list[int]:
__a : int = 0
__a : Union[str, Any] = len(a_) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 52 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
A = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(... | 52 | 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()
... | 52 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_s... | 52 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils impor... | 52 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenizatio... | 52 | 1 |
"""simple docstring"""
from collections import defaultdict
def __A ( a_ :str , a_ :str) -> bool:
__a : List[Any] = first_str.lower().strip()
__a : str = second_str.lower().strip()
# Remove whitespace
__a : ... | 52 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub... | 52 | 1 |
"""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 = {
'''roberta-base''': '''... | 52 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''kakaobrain/alig... | 52 | 1 |
"""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_u... | 52 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __A ( a_ :Tuple) -> List[str]:
return choice(a_)
def __A ( a_ :list[int] , a_ :int) -> int:
__a : Optional[int] = random_pivot(a... | 52 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (DDPMScheduler,)
... | 52 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAM... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :int) -> int:
__a : list[list[int]] = [[0 for _ in range(a_)] for _ in range(m + 1)]
for i in range(m + 1):
__a : int = 1
for n in range(m + 1):
for k in range(1 , a_)... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask... | 52 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
... | 52 |
"""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 = logging.get_logger(__name__)
A ... | 52 | 1 |
"""simple docstring"""
import argparse
import os
# New Code #
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_sch... | 52 |
"""simple docstring"""
def __A ( a_ :Tuple , a_ :Union[str, Any] , a_ :int=False) -> List[str]:
if isinstance(a_ , a_) and isinstance(a_ , a_):
__a : List[str] = len(set_a.intersection(a_))
if alternative... | 52 | 1 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __A ( a_ :int) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(a_ :float , ... | 52 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
A = tuple[int, int]
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , ... | 52 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'''shi-labs/dinat-min... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main... | 52 | 1 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run -... | 52 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __A ( a_ :Union[str, Any] , a_ :Union[str, Any] , a_ :Optional[Any] , a_ :Optional[int]=5) -> List[Any]:
# Adapted from https://github... | 52 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __A ( a_ :BertModel , a_ :str , a_ :str) -> str:
__a : List[str] = ('''dense.weigh... | 52 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
__a : Optional[int] ... | 52 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def __A ( a_ :Optional[Any]="ro" , a_ :List[str]="en" , a_ :str="wmt16" , a_ :str=None) -> None:
try:
import datasets
except (ModuleNotFoundErr... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {}
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__l... | 52 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 52 | 1 |
"""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_pr... | 52 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int]) -> int:
if not nums:
return 0
__a : Any = nums[0]
__a : Optional[Any] = 0
for num in nums[1:]:
__a , __a : ... | 52 | 1 |
"""simple docstring"""
import string
from math import logaa
def __A ( a_ :str , a_ :str) -> int:
__a : Optional[int] = document.translate(
str.maketrans('''''' , '''''' , string.punctuation)).replace('''\n''' , '''''... | 52 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :int) -> str:
if isinstance(a_ , a_):
raise TypeError('''\'float\' object cannot be interpreted as an integer''')
if isinstance(a_ , a_):
raise TypeError('''\'str\' object cannot be interpreted as an ... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'''facebook/convnextv... | 52 | 1 |
"""simple docstring"""
import math
import random
def __A ( a_ :float , a_ :bool = False) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value))
# Initial Value
A = 0.02
def __A ( a_ :int ,... | 52 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (DDPMScheduler,)
... | 52 | 1 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __A ( a_ :int , a_ :int , a_ :float = 1 / sqrt(2)) -> IIRFilter:
__a : str = tau * frequency / samplerate
__a : ... | 52 |
"""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_to... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int] , a_ :list[int] , a_ :list[int] , a_ :list[list[str]] , a_ :int , ) -> None:
__a : Tuple = len(a_)
# If row is equal to th... | 52 |
"""simple docstring"""
from __future__ import annotations
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
__a , __a : List[Any] =... | 52 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org... | 52 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
A = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(... | 52 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NA... | 52 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_s... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_prope... | 52 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenizatio... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __A ( a_ :Tuple) -> List[str]:
return choice(a_)
def __A ( a_ :list[int] , a_ :int) -> int:
__a : Optional[int] = random_pivot(a... | 52 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub... | 52 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import ... | 52 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''kakaobrain/alig... | 52 | 1 |
"""simple docstring"""
import argparse
import struct
import unittest
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase ):
__a : Any = data
# Initialize hash ... | 52 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __A ( a_ :Tuple) -> List[str]:
return choice(a_)
def __A ( a_ :list[int] , a_ :int) -> int:
__a : Optional[int] = random_pivot(a... | 52 | 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 ImagePro... | 52 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAM... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :List[Any]) -> Optional[int]:
__a , __a : int = [], []
while len(a_) > 1:
__a , __a : Any = min(a_), max(a_)
start.append(a_)
end.append(a_)
col... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask... | 52 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
fro... | 52 |
"""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 = logging.get_logger(__name__)
A ... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {'''configuration_xlnet''': ... | 52 |
"""simple docstring"""
def __A ( a_ :Tuple , a_ :Union[str, Any] , a_ :int=False) -> List[str]:
if isinstance(a_ , a_) and isinstance(a_ , a_):
__a : List[str] = len(set_a.intersection(a_))
if alternative... | 52 | 1 |
"""simple docstring"""
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet impor... | 52 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
A = tuple[int, int]
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , ... | 52 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggin... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __A ( a_ :np.ndarray) -> tuple[np.ndarray, np.ndarray]:
__a , __a : Any = np.shape(a_)
if rows != columns:
__a : Optional[int] = ... | 52 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __A ( a_ :Union[str, Any] , a_ :Union[str, Any] , a_ :Optional[Any] , a_ :Optional[int]=5) -> List[Any]:
# Adapted from https://github... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :Optional[Any] , a_ :Optional[Any]) -> List[Any]:
__a : List[str] = [0 for i in range(r + 1)]
# nc0 = 1
__a : str = 1
for i in range(1 , n + 1):
# to compute curren... | 52 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
__a : Optional[int] ... | 52 | 1 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {}
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__l... | 52 | 1 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_d... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 52 | 1 |
"""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 = {
'''distilbert-base-unca... | 52 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int]) -> int:
if not nums:
return 0
__a : Any = nums[0]
__a : Optional[Any] = 0
for num in nums[1:]:
__a , __a : ... | 52 | 1 |
"""simple docstring"""
from collections import defaultdict
def __A ( a_ :int) -> int:
__a : Dict = 1
__a : Any = True
for v in tree[start]:
if v not in visited:
ret += dfs(a_)
if ret % 2 == 0:
... | 52 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 52 | 1 |
"""simple docstring"""
from random import randint, random
def __A ( a_ :int , a_ :int , a_ :int , a_ :bool = False , a_ :bool = False , a_ :int = 5 , ) -> list:
__a : List[Any] = [[-1] * number_of_cells... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'''facebook/convnextv... | 52 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order... | 52 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (DDPMScheduler,)
... | 52 | 1 |
"""simple docstring"""
import requests
A = '''''' # <-- Put your OpenWeatherMap appid here!
A = '''https://api.openweathermap.org/data/2.5/'''
def __A ( a_ :str = "Chicago" , a_ :str = APPID) -> dict:
return requests.get(URL_BASE + '''weath... | 52 |
"""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_to... | 52 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
... | 52 |
"""simple docstring"""
from __future__ import annotations
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
__a , __a : List[Any] =... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''... | 52 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
A = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(... | 52 | 1 |
"""simple docstring"""
A = 8.314462 # Unit - J mol-1 K-1
def __A ( a_ :float , a_ :float , a_ :float) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''')
return moles ... | 52 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_s... | 52 | 1 |
"""simple docstring"""
A = 0 # The first color of the flag.
A = 1 # The second color of the flag.
A = 2 # The third color of the flag.
A = (red, white, blue)
def __A ( a_ :list) -> list:
if not sequence:
return []
if l... | 52 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenizatio... | 52 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( a_ :int) -> bool:
__a : int = int(number**0.5)
return number == sq * sq
def __A ( a_ :int , a_... | 52 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub... | 52 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
... | 52 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''kakaobrain/alig... | 52 | 1 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokeniz... | 52 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __A ( a_ :Tuple) -> List[str]:
return choice(a_)
def __A ( a_ :list[int] , a_ :int) -> int:
__a : Optional[int] = random_pivot(a... | 52 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_co... | 52 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAM... | 52 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger... | 52 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :int) -> int:
if not isinstance(a_ , a_) or number < 0:
raise ValueError('''Input must be a non-negative integer''')
__a : List[Any] = 0
while number:
# This way we arrive at next set ... | 52 |
"""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 = logging.get_logger(__name__)
A ... | 52 | 1 |
"""simple docstring"""
import math
def __A ( a_ :int) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not pr... | 52 |
"""simple docstring"""
def __A ( a_ :Tuple , a_ :Union[str, Any] , a_ :int=False) -> List[str]:
if isinstance(a_ , a_) and isinstance(a_ , a_):
__a : List[str] = len(set_a.intersection(a_))
if alternative... | 52 | 1 |
"""simple docstring"""
def __A ( a_ :int , a_ :int) -> int:
return int((input_a, input_a).count(0) == 0)
def __A ( ) -> None:
assert and_gate(0 , 0) == 0
assert and_gate(0 , 1) == 0
assert and_gate(1 , 0) == 0
... | 52 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
A = tuple[int, int]
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , ... | 52 | 1 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
def _lowerCamelCase ( self , _UpperCAmelCase=None , _... | 52 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main... | 52 | 1 |
"""simple docstring"""
import os
def __A ( ) -> Tuple:
with open(os.path.dirname(a_) + '''/grid.txt''') as f:
__a : Dict = [] # noqa: E741
for _ in range(20):
l.append([int(a_) for x in f.readline().split()])
__a ... | 52 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __A ( a_ :Union[str, Any] , a_ :Union[str, Any] , a_ :Optional[Any] , a_ :Optional[int]=5) -> List[Any]:
# Adapted from https://github... | 52 | 1 |
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