code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
def __magic_name__( lowerCamelCase = 6_0_0_8_5_1_4_7_5_1_4_3):
try:
__lowerCAmelCase = int(lowerCamelCase)
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''')
if n <= 0:
... | 9 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTest... | 9 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : int ... | 9 |
'''simple docstring'''
import argparse
import datetime
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thur... | 9 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class a__ ( unittest.TestCase ):
"""simple docs... | 9 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
fr... | 9 | 1 |
'''simple docstring'''
from math import sqrt
def __magic_name__( lowerCamelCase):
assert isinstance(lowerCamelCase, lowerCamelCase) and (
number >= 0
), "'number' must been an int and positive"
__lowerCAmelCase = True
# 0 and 1 are none primes... | 9 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_UpperCAmelCase : List[Any] = datasets.load_iris()
_UpperCAmelCase : Dict = np.array(data["""data"""])
_UpperCA... | 9 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = set()
# edges = list of graph's edges
__lowerCAmelCase = get_edges(lowerCamelCase)
# While there are still elements in edges list, take an arbitrary edge
# (from_n... | 9 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
fro... | 9 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = ... | 9 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_UpperCAmelCase : str = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER... | 9 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1E-12, lowerCamelCase = 1_0_0, ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCa... | 9 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,
... | 9 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 9 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase = " "):
__lowerCAmelCase = []
__lowerCAmelCase = 0
for index, char in enumerate(lowerCamelCase):
if char == separator:
split_words.append(string[las... | 9 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__A ):
"""simple docstring"""
__UpperCamelCase : int = ['torch', 'scipy']
def __init__(self , *__lowercase , **__lowercase ):
... | 9 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils impo... | 9 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProces... | 9 | 1 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''', [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0])
@pytest.mark.parametrize('''input_in_memory_max_size''', ['''default''', 0, 1_0_0 * 2**2_0, 9_0_0 *... | 9 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies... | 9 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase=False):
if isinstance(lowerCamelCase, lowerCamelCase) and isinstance(lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = len(set_a.intersection(lowerCamelCase))
... | 9 |
'''simple docstring'''
from math import sqrt
def __magic_name__( lowerCamelCase):
assert isinstance(lowerCamelCase, lowerCamelCase) and (
number >= 0
), "'number' must been an int and positive"
__lowerCAmelCase = True
# 0 and 1 are none primes... | 9 | 1 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.to... | 9 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from acceler... | 9 | 1 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_UpperCAmelCase : List[str] ... | 9 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : str ... | 9 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCAmelCase : Optional[int] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE... | 9 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassifi... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = ... | 9 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != 2 or len(a[0]) != 2 or len(lowerCamelCase) != 2 or len(b[0]) != 2:
raise Exception('''Matrices are not 2x2''')
__lowerC... | 9 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class a__ ( __A ):
"""simple docstring"""
def __init__(self ... | 9 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
if not isinstance(lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(lowerCamelCase)
if number < 1:
... | 9 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a__ ( _... | 9 | 1 |
'''simple docstring'''
_UpperCAmelCase : List[Any] = """Input must be a string of 8 numbers plus letter"""
_UpperCAmelCase : Any = """TRWAGMYFPDXBNJZSQVHLCKE"""
def __magic_name__( lowerCamelCase):
if not isinstance(lowerCamelCase, lowerCamelCase):
... | 9 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | 9 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,... | 9 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = 1
__lowerCAmelCase = 2
while i * i <= n:
__lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity +=... | 9 | 1 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_UpperCAmelCase : Optional[Any] = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>... | 9 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a__ ( unittest.TestCase ):
... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != 2 or len(a[0]) != 2 or len(lowerCamelCase) != 2 or len(b[0]) != 2:
raise Exception('''Matrices are not 2x2''')
__lowerC... | 9 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
_UpperCAmelCase : List[str] = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the gro... | 9 | 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_utils import requ... | 9 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTest... | 9 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''')
__lowerCAmelCase = str(bin(lowerCamelCase))[2:] # remove the leading "0b"
__lowerCAmelCase ... | 9 |
'''simple docstring'''
import argparse
import datetime
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thur... | 9 | 1 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
def __magic_name__( lowerCamelCase, lowerCamelCase):
_... | 9 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
fr... | 9 | 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, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__... | 9 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_UpperCAmelCase : List[Any] = datasets.load_iris()
_UpperCAmelCase : Dict = np.array(data["""data"""])
_UpperCA... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__( lowerCamelCase): # This function is recursive
__lowerCAmelCase = len(lowerCamelCase)
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
... | 9 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 1 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1E-12, lowerCamelCase = 1_0_0, ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCa... | 9 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = ... | 9 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
_UpperCAmelCase : Tuple = get_logger(__name__)
class a__ :
"""simple docstring"""
def __init__(self , __lowercase , __lowercase=None ):
... | 9 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1E-12, lowerCamelCase = 1_0_0, ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCa... | 9 | 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_toke... | 9 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 9 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCAmelCase : str = ... | 9 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__A ):
"""simple docstring"""
__UpperCamelCase : int = ['torch', 'scipy']
def __init__(self , *__lowercase , **__lowercase ):
... | 9 | 1 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_UpperCAmelCase : Dict = """__DUMMY_TRANSFORMERS_USER__"""
_UpperCAmelCase : str = """D... | 9 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProces... | 9 | 1 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_UpperCAmelCase : Any = """src/diffusers"""
# Matches is_xxx_available()
_UpperCAmelC... | 9 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies... | 9 | 1 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : int = logging.ge... | 9 |
'''simple docstring'''
from math import sqrt
def __magic_name__( lowerCamelCase):
assert isinstance(lowerCamelCase, lowerCamelCase) and (
number >= 0
), "'number' must been an int and positive"
__lowerCAmelCase = True
# 0 and 1 are none primes... | 9 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_UpperCAmelCase : List[Any] = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball... | 9 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from acceler... | 9 | 1 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''... | 9 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : str ... | 9 | 1 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers... | 9 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassifi... | 9 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_... | 9 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != 2 or len(a[0]) != 2 or len(lowerCamelCase) != 2 or len(b[0]) != 2:
raise Exception('''Matrices are not 2x2''')
__lowerC... | 9 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...te... | 9 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
if a < 0:
raise ValueError('''Input value must be a positive integer''')
elif isinstance(lowerCamelCase, lowerCamelCase):
raise TypeError('''Input value must be a \'int\' type''')
return bin(low... | 9 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a__ ( _... | 9 | 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_utils import requ... | 9 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | 9 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
fro... | 9 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = 1
__lowerCAmelCase = 2
while i * i <= n:
__lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity +=... | 9 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""microsoft/unispeech-sat-base-100h-l... | 9 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a__ ( unittest.TestCase ):
... | 9 | 1 |
'''simple docstring'''
from math import ceil
def __magic_name__( lowerCamelCase = 1_0_0_1):
__lowerCAmelCase = 1
for i in range(1, int(ceil(n / 2.0))):
__lowerCAmelCase = 2 * i + 1
__lowerCAmelCase = 2 * i
_... | 9 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
_UpperCAmelCase : List[str] = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the gro... | 9 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != len(lowerCamelCase):
raise ValueError('''String lengths must match!''')
__lowerCAmelCase = 0
for chara, chara in zip(lowerCamelCase, lowerCamelCase... | 9 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTest... | 9 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_UpperCAmelCase : str = {"""vocab_file""": """vocab.txt""",... | 9 |
'''simple docstring'''
import argparse
import datetime
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thur... | 9 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
_UpperCAmelCase : Union[str, Any] = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
auth... | 9 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
fr... | 9 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
f... | 9 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_UpperCAmelCase : List[Any] = datasets.load_iris()
_UpperCAmelCase : Dict = np.array(data["""data"""])
_UpperCA... | 9 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__A ):
"""simple docstring"""
__UpperCamelCase : int = ['torch', 'scipy']
def __init__(self , *__lowercase , **__lowercase ):
... | 9 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_uti... | 9 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = ... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__( lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = []
create_all_state(1, lowerCamelCase, lowerCamelCase, [], lowerCamelCase)
return result
def __magic_name__( lowerCa... | 9 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1E-12, lowerCamelCase = 1_0_0, ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCa... | 9 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/marku... | 9 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 9 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
... | 9 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__A ):
"""simple docstring"""
__UpperCamelCase : int = ['torch', 'scipy']
def __init__(self , *__lowercase , **__lowercase ):
... | 9 | 1 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = sorted(zip(lowerCamelCase, lowerCamelCase), key=lambda lowerCamelCase: x[0] / x[1], ... | 9 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProces... | 9 | 1 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_UpperCAmelCase : Optional[Any] = (
"""This me... | 9 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies... | 9 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__( lowerCamelCase):
if "cls_token" in name:
__lowerCAmelCase = name.rep... | 9 |
'''simple docstring'''
from math import sqrt
def __magic_name__( lowerCamelCase):
assert isinstance(lowerCamelCase, lowerCamelCase) and (
number >= 0
), "'number' must been an int and positive"
__lowerCAmelCase = True
# 0 and 1 are none primes... | 9 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a__ ( unittest.TestCase ):
... | 9 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from acceler... | 9 | 1 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def __magic_name__( lowerCamelCase):
if num <= 0:
raise ValueError('''math domain error''')
return quad(lowerCamelCase, 0, lowerCamelCase, args=(lowerCamelCase))[0]
def... | 9 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : str ... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__( lowerCamelCase, lowerCamelCase):
if b == 0:
return (1, 0)
((__lowerCAmelCase) , (__lowerCAmelCase)) = extended_euclid(lowerCamelCase, a % b)
__lowerCAmelCase ... | 9 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassifi... | 9 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_UpperCAmelCase : Tuple = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_UpperCAmelCase : ... | 9 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != 2 or len(a[0]) != 2 or len(lowerCamelCase) != 2 or len(b[0]) != 2:
raise Exception('''Matrices are not 2x2''')
__lowerC... | 9 | 1 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import imag... | 9 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase : str = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase : List[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.downlo... | 9 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a__ ( _... | 9 | 1 |
'''simple docstring'''
import math
def __magic_name__( lowerCamelCase = 1_0_0):
__lowerCAmelCase = sum(i * i for i in range(1, n + 1))
__lowerCAmelCase = int(math.pow(sum(range(1, n + 1)), 2))
return square_of_sum - sum_of_squares
if __name__ =... | 9 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | 9 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def __magic_name__( lowerCamelCase):
return np.array_equal(lowerCamelCase, matrix.conjugate().T)
def __magic_name__( lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = v.conjugate().T
... | 9 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = 1
__lowerCAmelCase = 2
while i * i <= n:
__lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity +=... | 9 | 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 PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : int = """▁"""
_UpperCAmelCase : Op... | 9 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a__ ( unittest.TestCase ):
... | 9 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaCon... | 9 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
_UpperCAmelCase : List[str] = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the gro... | 9 | 1 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a__ ( __A ):
"""simple docstring"""
... | 9 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTest... | 9 | 1 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def __magic_name__( lowerCamelCase):
if not isinstance(lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(lo... | 9 |
'''simple docstring'''
import argparse
import datetime
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thur... | 9 | 1 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import Wava... | 9 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
fr... | 9 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Any = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
... | 9 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_UpperCAmelCase : List[Any] = datasets.load_iris()
_UpperCAmelCase : Dict = np.array(data["""data"""])
_UpperCA... | 9 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : str = 5_0_0_0_0_0
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = os.path.split(... | 9 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 1 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassifi... | 9 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = ... | 9 | 1 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
_UpperCAmelCase : Optional[int] = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": opera... | 9 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1E-12, lowerCamelCase = 1_0_0, ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCa... | 9 | 1 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 9 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 9 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Tuple ... | 9 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__A ):
"""simple docstring"""
__UpperCamelCase : int = ['torch', 'scipy']
def __init__(self , *__lowercase , **__lowercase ):
... | 9 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def __magic_name__( lowerCamelCase = 8, lowerCamelCase = None):
__lowerCAmelCase = np.random.default_rng(seed=lowerCamelCase)
# Roughly 25% of the qubits will contribute to the key.
# So we take more than w... | 9 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProces... | 9 | 1 |
'''simple docstring'''
import pprint
import requests
_UpperCAmelCase : str = """https://zenquotes.io/api"""
def __magic_name__( ):
return requests.get(API_ENDPOINT_URL + '''/today''').json()
def __magic_name__( ):
return requests.get(API_ENDPOINT_URL + ... | 9 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import ... | 9 |
'''simple docstring'''
from math import sqrt
def __magic_name__( lowerCamelCase):
assert isinstance(lowerCamelCase, lowerCamelCase) and (
number >= 0
), "'number' must been an int and positive"
__lowerCAmelCase = True
# 0 and 1 are none primes... | 9 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTest... | 9 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from acceler... | 9 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Convers... | 9 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : str ... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_... | 350 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassifi... | 9 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
class a__ ( A__ ):
"""simple docstring"""
def __init__(self , ... | 351 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != 2 or len(a[0]) != 2 or len(lowerCamelCase) != 2 or len(b[0]) != 2:
raise Exception('''Matrices are not 2x2''')
__lowerC... | 9 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin... | 352 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __ma... | 353 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a__ ( _... | 9 | 0 |
'''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_co... | 354 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | 9 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_UpperCAmelCase : Optional[int] = """docs/source/en/_toctree.yml"""
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = defaultdict(__a)
for doc in ... | 355 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = 1
__lowerCAmelCase = 2
while i * i <= n:
__lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity +=... | 9 | 0 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stab... | 356 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a__ ( unittest.TestCase ):
... | 9 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.ut... | 357 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
_UpperCAmelCase : List[str] = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the gro... | 9 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[Any] = 5_0_0_0_0
_UpperCAmelCase : str = 5_0_0_0
_UpperCAmelCase : str = os.pat... | 358 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTest... | 9 | 0 |
'''simple docstring'''
from PIL import Image
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = image.size
__lowerCAmelCase = 0
__lowerCAmelCase = image.load()
for i in range(__lowerCamelCase):
for j in range(__lowerCamelCase):
... | 359 |
'''simple docstring'''
import argparse
import datetime
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thur... | 9 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
_UpperCAmelCase : List[str] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, ... | 360 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
fr... | 9 | 0 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase):
return np.where(vector > 0, lowercase__, (alpha * (np.exp(lowercase__) - 1)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_UpperCAmelCase : List[Any] = datasets.load_iris()
_UpperCAmelCase : Dict = np.array(data["""data"""])
_UpperCA... | 9 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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
if is_flax_available():
... | 362 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
... | 363 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = ... | 9 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the inten... | 364 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1E-12, lowerCamelCase = 1_0_0, ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCa... | 9 | 0 |
'''simple docstring'''
from statistics import mean
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = 0
# Number of processes finished
__lowerCAmelCase = 0
# Displays ... | 365 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 9 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Any = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 366 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__A ):
"""simple docstring"""
__UpperCamelCase : int = ['torch', 'scipy']
def __init__(self , *__lowercase , **__lowercase ):
... | 9 | 0 |
'''simple docstring'''
_UpperCAmelCase : Optional[int] = {
'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',
'cookiecutter': 'cookiec... | 367 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProces... | 9 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase ... | 368 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies... | 9 | 0 |
'''simple docstring'''
def __magic_name__( lowerCamelCase = 4_0_0_0_0_0_0):
__lowerCAmelCase = [0, 1]
__lowerCAmelCase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i... | 369 |
'''simple docstring'''
from math import sqrt
def __magic_name__( lowerCamelCase):
assert isinstance(lowerCamelCase, lowerCamelCase) and (
number >= 0
), "'number' must been an int and positive"
__lowerCAmelCase = True
# 0 and 1 are none primes... | 9 | 0 |
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