code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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_configuration_common import ... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONF... | 667 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cla... | 667 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.s... | 667 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 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 TokenizerTesterMixin
... | 667 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[Any] = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''google/pix2struct-tex... | 667 | 1 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__(self , *lowercase , **lowercase ):
super().__init__(*lowercase , **lowercase )
A_ : Optional[int] = {}
de... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase :Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 667 | 1 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
lowerCamelCase :Optional[Any] = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is... | 667 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCa... | 667 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Tuple = (IPNDMScheduler,)
__SCREAMING_SNAKE_CASE : O... | 667 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 667 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase :int = logging.get_logger(__name__)
lowerCamelCase :int = {... | 667 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_s... | 667 | 1 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
... | 667 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_... | 667 | 1 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def a ( lowerCamelCase__ , lowerCamelCase__ , lowe... | 667 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamel... | 667 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = (EulerDis... | 667 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
Aut... | 667 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def a ( lowerCamelCase__ ):
'''simple docstring'''
if "img_encoder.pos_embed" in name:
A_ : Optional[int... | 667 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Optional[Any] = logging.get_logger(__name__)
lowerCamelCase :Tuple = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-st... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attent... | 667 |
'''simple docstring'''
import math
from collections.abc import Callable
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : float = xa
A_ : float = xa
while True:
if x_n == x_na or function(lowerCamel... | 667 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
'''... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 667 | 1 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase :int = logging.getLogger(__name__)
lowerCa... | 667 |
'''simple docstring'''
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase , lowercase ):
A_ : List[str] = name
A_ : Dict = value
A_ : Optional[int] = weight
def __repr__(self ):
return F'{self.__class__.__name__}({self.na... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_model... | 667 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase :int = logging.getLogger(__name__)
lowerCa... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : list[list[str]] = [[] for _ in range(lowerCamelCase__ )]
A_ : Tuple = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or n... | 667 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
assert (
isinstance(lowerCamelCase__ , lowerCamelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:... | 667 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_bytes:
raise ValueE... | 667 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : List[Any] = year % 19
A_ : Optional[Any] = year % 4
A_ : List[Any] = year % 7
A_ : Optional[Any] ... | 667 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fr... | 667 | 1 |
'''simple docstring'''
import os
def a ( ):
'''simple docstring'''
with open(os.path.dirname(lowerCamelCase__ ) + """/grid.txt""" ) as f:
A_ : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowerCamelCase__ ... | 667 |
'''simple docstring'''
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))''')) | 667 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['keras_nlp']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as... | 667 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_t... | 667 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowerCamelCase :Dict = get_logger(__name__)
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=None ):
A_ : Optional[int] = attrs or []
if m... | 667 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONF... | 667 | 1 |
'''simple docstring'''
from math import isqrt
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : List[str] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCamelCase... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cla... | 667 | 1 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 667 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 1 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_avai... | 667 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[Any] = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''google/pix2struct-tex... | 667 | 1 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMi... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase :Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 667 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowerCAmelCase ( __UpperCAmelCase ):
@staticmethod
@abstractmethod
def _a (lowercase ):
raise NotImplementedError()
@abstractmethod
def _a (self ... | 667 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCa... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
if any(not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len... | 667 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ ):
'''simple docstring'''
create_state_space_tree(lowerCamelCase__ , [] , 0 , [0 for i in range(len(lowerCamelCase__ ) )] )
def a ( lowerCamelCase__ , lower... | 667 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_s... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase :Optional[Any] = {'''configuration_vit''': ['''VI... | 667 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_... | 667 | 1 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ ... | 667 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamel... | 667 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 667 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
Aut... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def a ( lowerCamelCase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even ... | 667 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Optional[Any] = logging.get_logger(__name__)
lowerCamelCase :Tuple = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-st... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase :Dict = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetC... | 667 |
'''simple docstring'''
import math
from collections.abc import Callable
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : float = xa
A_ : float = xa
while True:
if x_n == x_na or function(lowerCamel... | 667 | 1 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization i... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = 1_00 , ):
'''simple docstring'''
A_ : Optional[int] = x_start
A_ : Dict = f... | 667 |
'''simple docstring'''
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase , lowercase ):
A_ : List[str] = name
A_ : Dict = value
A_ : Optional[int] = weight
def __repr__(self ):
return F'{self.__class__.__name__}({self.na... | 667 | 1 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import v... | 667 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase :int = logging.getLogger(__name__)
lowerCa... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Dict = list(range(len(lowerCamelCase__ ) ) )
A_ : Optional[Any] = [v / w for v, w... | 667 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 667 | 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 require_vision
f... | 667 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_bytes:
raise ValueE... | 667 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
cla... | 667 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fr... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase :Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 667 |
'''simple docstring'''
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))''')) | 667 | 1 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class _lowerCAmelCase ( unittest.TestCase ):
def _a (self ):
A_ : Dict = 0
A_ : Tuple = [0]
A_ : int = [0]
A_ : str = len(lowercase ... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase :int = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_ava... | 667 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowerCamelCase :Dict = get_logger(__name__)
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=None ):
A_ : Optional[int] = attrs or []
if m... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
A_ : Dict = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
A_ : List[str]... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONF... | 667 | 1 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def a ( lowerCamelCase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cla... | 667 | 1 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _lowerCAmelCase ( __UpperCAmelCase ):
@require_torch
def _a (self ):
... | 667 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCamelCase :List[str] = logging.get_logger(__name__)
class _lowerCAmelCase ( __UpperCAmelCase ... | 667 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[Any] = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''google/pix2struct-tex... | 667 | 1 |
'''simple docstring'''
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 _lowerCAmelCase ( datasets.BuilderConfig ):
__SCREAMING_SNAKE_CASE : ... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase :Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 667 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_s... | 667 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCa... | 667 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowerCamelCase :Dict = get_logger(__name__)
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=None ):
A_ : Optional[int] = attrs or []
if m... | 667 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ... | 667 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_s... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase :Tuple = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torc... | 667 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_... | 667 | 1 |
'''simple docstring'''
import argparse
import datetime
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[Any] = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4"""... | 667 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamel... | 667 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMo... | 667 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
Aut... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def a ( lowerCamelCase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even ... | 667 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Optional[Any] = logging.get_logger(__name__)
lowerCamelCase :Tuple = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-st... | 667 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
Aut... | 667 |
'''simple docstring'''
import math
from collections.abc import Callable
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : float = xa
A_ : float = xa
while True:
if x_n == x_na or function(lowerCamel... | 667 | 1 |
'''simple docstring'''
import math
import sys
def a ( lowerCamelCase__ ):
'''simple docstring'''
if number != int(lowerCamelCase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of ... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 667 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 667 |
'''simple docstring'''
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase , lowercase ):
A_ : List[str] = name
A_ : Dict = value
A_ : Optional[int] = weight
def __repr__(self ):
return F'{self.__class__.__name__}({self.na... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
A_ : Dict = 4
A_ : List[str] = (1 << p) - 1
for _ in range(p - 2 ... | 667 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase :int = logging.getLogger(__name__)
lowerCa... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only on... | 667 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 667 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 667 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_bytes:
raise ValueE... | 667 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 667 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fr... | 667 | 1 |
'''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata... | 667 |
'''simple docstring'''
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))''')) | 667 | 1 |
'''simple docstring'''
import math
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowerCamelCase__ )
def a ( lowerCam... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as... | 667 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[str] = logging.get_logger(__name__)
# TODO Update this
lowerCamelCase :Dict = ... | 667 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowerCamelCase :Dict = get_logger(__name__)
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=None ):
A_ : Optional[int] = attrs or []
if m... | 667 | 1 |
'''simple docstring'''
class _lowerCAmelCase : # Public class to implement a graph
def __init__(self , lowercase , lowercase , lowercase ):
A_ : Dict = row
A_ : Optional[Any] = col
A_ : Union[str, Any] = graph
def _a (self , low... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONF... | 667 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCam... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cla... | 667 | 1 |
'''simple docstring'''
lowerCamelCase :int = range(2, 2_0 + 1)
lowerCamelCase :Optional[int] = [1_0**k for k in range(ks[-1] + 1)]
lowerCamelCase :dict[int, dict[int, list[list[int]]]] = {}
def a ( lowerCamelCase__ , lowerCamelCase__ , low... | 667 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _lowerCAmelCase ( nn.Module ):
def __init__(self , lowercase = 16 , lowercase = 88 , lowercase = None , lowercase = 1 , lo... | 667 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[Any] = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''google/pix2struct-tex... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase :Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 667 | 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.activations import gelu_new, gelu_python, get_activation
@require_torch
class _lowerCAmelCa... | 667 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCa... | 667 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Optional[Any] = logging.get_logger(__name__)
lowerCamelCase :Tuple = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-st... | 667 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 667 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamel... | 667 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_s... | 667 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=0.2 , lowercase=0.2 ):
A_ : Union[str, Any] = bp_numa... | 667 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_... | 667 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
) | 667 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamel... | 667 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase :Optional[int] = logging.get_logger(__name__)
lowerCamelCase :str = {
'''nielsr/canine-s''': 2... | 667 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
Aut... | 667 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeniz... | 667 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Optional[Any] = logging.get_logger(__name__)
lowerCamelCase :Tuple = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-st... | 667 | 1 |
'''simple docstring'''
from collections import deque
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[Any] = len(lowerCamelCase__ )
A_ : int = deque()
A_ : Dict = [False for _ in range(lowerCamelCase__ )]
A_ ... | 667 |
'''simple docstring'''
import math
from collections.abc import Callable
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : float = xa
A_ : float = xa
while True:
if x_n == x_na or function(lowerCamel... | 667 | 1 |
'''simple docstring'''
import os
lowerCamelCase :Dict = {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Dict = 0
A_ : Any ... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 667 | 1 |
'''simple docstring'''
lowerCamelCase :Optional[Any] = 8.314_462 # Unit - J mol-1 K-1
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs... | 667 |
'''simple docstring'''
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase , lowercase ):
A_ : List[str] = name
A_ : Dict = value
A_ : Optional[int] = weight
def __repr__(self ):
return F'{self.__class__.__name__}({self.na... | 667 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from... | 667 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase :int = logging.getLogger(__name__)
lowerCa... | 667 | 1 |
'''simple docstring'''
import math
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Any = len(lowerCamelCase__ )
A_ : Any = int(math.floor(math.sqrt(lowerCamelCase__ ) ) )
A_ : Optional[int] ... | 667 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 667 | 1 |
'''simple docstring'''
import operator as op
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : List[Any] = []
A_ : Dict = lambda lowerCamelCase__ , lowerCamelCase__ : int(x / y ) # noqa: E731 integer division operation
A_ : ... | 667 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_bytes:
raise ValueE... | 667 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
cl... | 667 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fr... | 667 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 667 |
'''simple docstring'''
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))''')) | 667 | 1 |
'''simple docstring'''
import cmath
import math
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : int = math.radians(lowerCamelCase__ )
A_ : List[str] = math.radians(lowerCamelCase_... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as... | 667 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowerCAmelCase ( __UpperCAmelCase ):
@staticmethod
@abstractmethod
def _a (lowercase ):
raise NotImplementedError()
@abstractmethod
def _a (self ... | 667 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowerCamelCase :Dict = get_logger(__name__)
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=None ):
A_ : Optional[int] = attrs or []
if m... | 667 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytor... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONF... | 667 | 1 |
'''simple docstring'''
import requests
lowerCamelCase :List[Any] = '''''' # <-- Put your OpenWeatherMap appid here!
lowerCamelCase :Union[str, Any] = '''https://api.openweathermap.org/data/2.5/'''
def a ( lowerCamelCase__ = "Chicago" , lowerCamelCase__ = A... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cla... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A_ : Tuple = 1
A_ : Optional[int] = 1
while repunit:
A_ : Optional[int] = (10 * repunit + 1) % divisor
... | 667 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 1 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[Any] = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''google/pix2struct-tex... | 667 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import Ba... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase :Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 667 | 1 |
'''simple docstring'''
import sys
lowerCamelCase :Union[str, Any] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''125406987471585238630507156932909... | 667 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCa... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCamelCase :Any = 1_0
def a ( lowerCamelCase__ , lowerCamelCase__ , l... | 667 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 667 | 1 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 667 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_s... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase :str = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLC... | 667 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_... | 667 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCa... | 667 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamel... | 667 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _lowerCAmelCase ( __UpperCAmelCase ):
__... | 667 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
Aut... | 667 | 1 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase :Union[str, Any] = {
'''facebook/mask2former-swin-small-coco-instance''': (
... | 667 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Optional[Any] = logging.get_logger(__name__)
lowerCamelCase :Tuple = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-st... | 667 | 1 |
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