code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def A(__a: list[int] , __a: int ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(__a ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowerCAmelCase_ ... | 122 |
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 intent you should run this script from the r... | 122 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
f... | 122 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 1 |
def A(__a: int = 100_0000 ):
lowerCAmelCase_ = set(range(3 , __a , 2 ) )
primes.add(2 )
for p in range(3 , __a , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , __a , __a ) ) )
... | 122 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 1 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
''... | 122 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, ... | 122 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart ... | 122 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 1 |
from __future__ import annotations
def A(__a: list[int] ): # This function is recursive
lowerCAmelCase_ = len(__a )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
lowerCAmelCa... | 122 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 1 |
def A(__a: int | float | str ):
try:
lowerCAmelCase_ = float(__a )
except ValueError:
raise ValueError("Please enter a valid number" )
lowerCAmelCase_ = decimal - int(__a )
if fractional_part == 0:
return int(__a ), 1
else:
lowerCAmelCas... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 1 |
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.tokenization_transfo_xl import CORPUS... | 122 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@req... | 122 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_t... | 122 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneT... | 122 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 1 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine impor... | 122 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 1 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''torch''', '''scipy''']
def __init__( self , *_a , **_a ) -> Optional[int]:
requires_backends(self , ["torch", "scipy"] )
... | 122 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_ten... | 122 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}
class __magic_na... | 122 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 1 |
def A(__a: int ):
if not isinstance(__a , __a ):
raise TypeError("only integers accepted as input" )
else:
lowerCAmelCase_ = str(abs(__a ) )
lowerCAmelCase_ = [list(__a ) for char in range(len(__a ) )]
for index in range(len(_... | 122 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def A(__a: Optional[Any] ):
lowerCAmelCase_ = torch.loa... | 122 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 1 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __magic_name__ :
lowerCamelCase__ = 42
lowerCamelCase__ = No... | 122 |
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 intent you should run this script from the r... | 122 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, Res... | 122 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 1 |
def A(__a: int , __a: Optional[int] ):
lowerCAmelCase_ = ""
for i in table:
res += inp[i - 1]
return res
def A(__a: Dict ):
return data[1:] + data[0]
def A(__a: Tuple , __a: Union[str, Any] ):
lowerCAmelCase_ = ""
for... | 122 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markup... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 1 |
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, is_vision_available
from ...test_configuration_com... | 122 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, ... | 122 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 1 |
def A(__a: int = 10 , __a: int = 22 ):
lowerCAmelCase_ = range(1 , __a )
lowerCAmelCase_ = range(1 , __a )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":... | 122 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 122 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __magic_name__ (__lowercase ... | 122 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_log... | 122 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 1 |
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 intent you should run this script from the r... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 1 |
import argparse
from collections import defaultdict
import yaml
lowerCamelCase__ = '''docs/source/en/_toctree.yml'''
def A(__a: int ):
lowerCAmelCase_ = defaultdict(__a )
lowerCAmelCase_ = []
lowerCAmelCase_ = []
for doc in doc_list:
... | 122 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 1 |
def A(__a: List[str] , __a: List[str] ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase_ = (boundary[1] - boundary[0]) / steps
lowerCAmelCase_ = boundary[0]
lowerCAmelCase_ = boundary[1]
lowerCAmelCase_... | 122 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 1 |
from __future__ import annotations
from typing import Any
class __magic_name__ :
def __init__( self , _a = 6 ) -> None:
lowerCAmelCase_ = None
lowerCAmelCase_ = None
self.create_linked_list(_a )
def __a ( ... | 122 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase__ = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],... | 122 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRe... | 122 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __magic_name__ (datasets.BuilderConfig ):
lowerCamelCase__ = None
class __magic_name__ (dat... | 122 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 1 |
from __future__ import annotations
def A(__a: list[int] ):
return len(set(__a ) ) == len(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 122 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 1 |
import argparse
import copy
def A(__a: Optional[int] ):
lowerCAmelCase_ = {}
with open(__a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
lowerCAmelCase_ = []
_list.append([line.split()[1], line.split()[2]] )
... | 122 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __magic_name__ :
def __init__( self , _a ) -> List[str]:
lowerCAmelCase_ = data
lowerCAmelCase_ = None
class __magic_name__ :
... | 122 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.ut... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 122 |
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 intent you should run this script from the r... | 122 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParse... | 122 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __magic_name__ (unittest.TestCase ):
def __a ( self ) -> List[Any]:
lowerCAmel... | 122 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCamelCase__ = input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
lowerCamelCase__ = BeautifulSoup(requests.get(url).content, '''html.parser... | 122 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 122 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 1 |
import copy
import random
from transformers import CLIPTokenizer
class __magic_name__ (__lowercase ):
def __init__( self , *_a , **_a ) -> int:
super().__init__(*_a , **_a )
lowerCAmelCase_ = {}
def __a ( self , _a , ... | 122 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_blenderbot_small''': [
'''BLENDERBOT_SMAL... | 122 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 122 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https:... | 122 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 122 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 1 |
# using dfs for finding eulerian path traversal
def A(__a: Dict , __a: List[Any] , __a: List[str] , __a: Optional[Any]=None ):
lowerCAmelCase_ = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
lowerCAmelCase_ , lowerCAm... | 122 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 1 |
from itertools import permutations
def A(__a: tuple ):
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
lowerCAmelCase_ = [7, 11, 13, 17]
for i, test in enumerate(__a ):
if (num[i + 4] *... | 122 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCamelCase__ = {
# 1536-bit
5: {
'''prime''': int(
'... | 122 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from... | 122 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 1 |
def A(__a: Dict ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(__a )
for i in range(n - 1 ):
for j in range(i + 1 , __a ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def A(__a: Union[str, Any] ):
if l... | 122 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 1 |
from __future__ import annotations
from typing import Any
class __magic_name__ :
def __init__( self , _a ) -> None:
lowerCAmelCase_ = num_of_nodes
lowerCAmelCase_ = []
lowerCAmelCase_ = {}
def __a ( ... | 122 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowerCamelCase__ = '''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh,... | 122 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 1 |
import numpy as np
from PIL import Image
def A(__a: np.ndarray , __a: int , __a: int ):
lowerCAmelCase_ = np.array(__a )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
lowerCAmelCase_ = 0
lowe... | 122 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 1 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowerCamelCase__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: ... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 1 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCamelCase__ = datasets.utils.logging.get_logger(__name__)
class __magic_name__ (folder_based_builder.FolderBasedBuilderConfig ):
lowerCamelCas... | 122 |
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 intent you should run this script from the r... | 122 | 1 |
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
from diffusers.utils.testing_utils import... | 122 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import Stabl... | 122 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowerCamelCase__ = False
class __magic_name__ (unittest.TestCase ):
pass... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __magic_name__ (unittest.TestCase ):
def __a ( self ) -> None:
lowerCAmelCase_ = Vector([1, 2, 3] )
s... | 122 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (__lowercase ):
lowerCamelCase__ = (KDPMaDiscreteScheduler,)
lowerCamelCase__ = 10
def _... | 122 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCamelCase__ = TypeVar('''T''')
lowerCamelCase__ = TypeVar('''U''')
class __magic_name__ (Generic[T, U] ):
def __init__( self , _a , _a ) -> Di... | 122 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 1 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCamelCase__ = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
... | 122 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 1 |
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_torchaudio
from transformers.utils.import_utils impor... | 122 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Per... | 122 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 1 |
from collections import namedtuple
lowerCamelCase__ = namedtuple('''from_to''', '''from_ to''')
lowerCamelCase__ = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.001, 10_00),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.00_454, 264.172),
'''cubic... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 1 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils ... | 122 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator,... | 122 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 1 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCamelCase__ = logging.get_logger(__name__)
class __magic_name__ (__lowercase ):
def __init__( self , *_a , **_a ) -> None:
warnings.warn(
"The... | 122 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 1 |
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: Tuple ): # picklable for multiprocessing
return i + 1
@require_dill_gt_... | 122 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 1 |
def A(__a: int , __a: int ):
return base * power(__a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
lowerCamelCase__ = int(input('''Enter the base: ''').strip())
lowerCamelCase... | 122 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 1 |
from __future__ import annotations
lowerCamelCase__ = '''Muhammad Umer Farooq'''
lowerCamelCase__ = '''MIT'''
lowerCamelCase__ = '''1.0.0'''
lowerCamelCase__ = '''Muhammad Umer Farooq'''
lowerCamelCase__ = '''contact@muhammadumerfarooq.me'''
lowerCamelC... | 122 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 1 |
def A(__a: int , __a: list ):
_enforce_args(__a , __a )
if n == 0:
return 0
lowerCAmelCase_ = float("-inf" )
for i in range(1 , n + 1 ):
lowerCAmelCase_ = max(
__a , prices[i - 1] + naive_cut_rod_recursive(n - i ... | 122 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __magic_name__ (__lowercase , __lowercase ):
lowerCamelCase__ =... | 122 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 1 |
lowerCamelCase__ = {
'''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''': '''cooki... | 122 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 1 |
import tensorflow as tf
from ...tf_utils import shape_list
class __magic_name__ (tf.keras.layers.Layer ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False , **_a ) -> int:
super().__init__(**_a )
lowerCAmelCase_ = vocab_size
... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 1 |
from __future__ import annotations
from cmath import sqrt
def A(__a: int , __a: int , __a: int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
lowerCAmelCase_ = b * b - 4 * a * c
lowerCAmelCase_ = (-b + sqrt(__a )) /... | 122 |
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 intent you should run this script from the r... | 122 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''facebook/xlm-roberta-xl''': '''http... | 122 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCamelCase__ = logging.get_lo... | 122 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 1 |
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 ...test_configuration_common import Co... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/re... | 122 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 1 |
from PIL import Image
def A(__a: Image , __a: float ):
def brightness(__a: int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.point(__a )
if __name_... | 122 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
O... | 122 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_... | 122 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 1 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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
from ..pipeline_params import TEXT_GUIDED_I... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamel... | 122 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 1 |
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