code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__lowercase = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__lowercase = [file for file in filepaths if file != file... | 43 | import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowercase = 1 / 100
__lowercase ... | 43 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller th... | 43 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',... | 43 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassif... | 43 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size... | 43 | 1 |
from __future__ import annotations
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return [ord(SCREAMING_SNAKE_CASE ) - 96 for elem in plain]
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encod... | 43 | from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 43 | 1 |
from __future__ import annotations
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not nums:
return 0
__UpperCamelCase :str = nums[0]
__UpperCamelCase :Optional[int] = 0
for num in nums[1:]:
__UpperCamelCase , __UpperCamelC... | 43 | import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase ... | 43 | 1 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if number > 0:
raise ValueError('''input must be a negative integer''' )
__UpperCamelCase :str = len(bin(SCREAMING_SNAKE_CASE )[3:] )
__UpperCamelCase :Optional[Any] = bin(abs(SCREAMING_SNAKE_CASE ) -... | 43 | import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamel... | 43 | 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_dimens... | 43 | import math
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 43 | 1 |
def lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__lowercase = generate_large_matrix()
__lowercase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
... | 43 | import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_C... | 43 | 1 |
# Algorithm for the pigeonhole sorting
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = min(SCREAMING_SNAKE_CASE ) # min() finds the minimum value
__UpperCamelCase :List[Any] = max(SCREAMING_SNAKE_CASE ) # max() finds th... | 43 | def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1_000 ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = 1
__UpperCamelCase :Any = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
__UpperCamelCase :list[i... | 43 | 1 |
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 lowerCamelCase_ ( UpperCAmelCase_ ):
'... | 43 | import argparse
import json
from tqdm import tqdm
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=SCREAMING_SNAKE_CASE , default='''biencoder-nq-dev... | 43 | 1 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.uti... | 43 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller th... | 43 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_availabl... | 43 | 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 Accele... | 43 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
... | 43 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config... | 43 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert import Re... | 43 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 43 | 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.utils im... | 43 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLI... | 43 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
'''configuration_data2vec_text... | 43 | import numpy as np
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1e-12 , SCREAMING_SNAKE_CASE = 100 , ):
'''simple docstring'''
assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[1]
# Ensure proper dimensionality.... | 43 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''... | 43 | import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 43 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markuplm-large''': '''ht... | 43 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''... | 43 | 1 |
from typing import Any
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase) -> List[Any]:
__UpperCamelCase :Optional[int] = data
__UpperCamelCase :List[Any] = None
class lowerCamelCase_ :
'''simple docstring'... | 43 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available,... | 43 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowercase = loggin... | 43 | def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = [0 for i in range(len(SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__UpperCamelCase , __UpperCamelCase :str = 0, 0
for i in range(1 ... | 43 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase) -> Tuple:
__UpperCamelCase :str = list_of_points
# Degree determines the flexibility of the curve.... | 43 | import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
... | 43 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_... | 43 | 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.set_verbosity_info()
__lowercase... | 43 | 1 |
__lowercase = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-builder''': '''hf... | 43 | import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowercase = 1 / 100
__lowercase ... | 43 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERA... | 43 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',... | 43 | 1 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1_000_000 ):
'''simple docstring'''
__UpperCamelCase :str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , SCREAMING_SNAKE_CASE ):
... | 43 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size... | 43 | 1 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = 0 ):
'''simple docstring'''
__UpperCamelCase :List[Any] = right or len(SCREAMING_SNAKE_CASE ) - 1
if left > right:
return -1
elif list_data[left] == ke... | 43 | from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 43 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torc... | 43 | import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase ... | 43 | 1 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Any = ''''''
for word_or_phrase in separated:
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise Exception('''join() accepts only stri... | 43 | import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamel... | 43 | 1 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , ):
'''simple docstring'''
__UpperCamelCase :List[Any] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in p... | 43 | import math
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 43 | 1 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowercase = logging... | 43 | import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_C... | 43 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__lowercase = logging.get_logger(__name__)
__lowercase ... | 43 | def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1_000 ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = 1
__UpperCamelCase :Any = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
__UpperCamelCase :list[i... | 43 | 1 |
from math import factorial
__lowercase = {str(digit): factorial(digit) for digit in range(10)}
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError('''Parameter number must be int'... | 43 | import argparse
import json
from tqdm import tqdm
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=SCREAMING_SNAKE_CASE , default='''biencoder-nq-dev... | 43 | 1 |
from __future__ import annotations
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = str(SCREAMING_SNAKE_CASE )
return len(SCREAMING_SNAKE_CASE ) == 9 and set(SCREAMING_SNAKE_CASE ) == set('''123456789''' )
def lowerCamelCase ( ):
''... | 43 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller th... | 43 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowercase = 1 / 100
__lowercase ... | 43 | 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 Accele... | 43 | 1 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase) -> None:
__UpperCamelCase :Optional[int] = data
__UpperCamelCase :Node | None = None
__UpperCamelCase :Node | None = Non... | 43 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config... | 43 | 1 |
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
__lowercase = logging.getLogger(__name__)
__lowercase = 50 # max width of layer nam... | 43 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 43 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase ... | 43 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLI... | 43 | 1 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self) -> int:
__UpperCamelCase :List[Any] = ''''''
__UpperCamelCase :Dict = ''''''
__UpperCamelCase :Union[str, Any] = []
def UpperCamelCase__ ( self , __lowerca... | 43 | import numpy as np
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1e-12 , SCREAMING_SNAKE_CASE = 100 , ):
'''simple docstring'''
assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[1]
# Ensure proper dimensionality.... | 43 | 1 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 43 | import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 43 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowercase = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
try:
if n... | 43 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''... | 43 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=lowercase ):
'''simple docstring'''
__snake_case = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Dict , ... | 0 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available,... | 43 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.uti... | 1 | def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = [0 for i in range(len(SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__UpperCamelCase , __UpperCamelCase :str = 0, 0
for i in range(1 ... | 43 | 0 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 2 | import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
... | 43 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A ( __snake_case ):
__magic_name__ = ['''image_processor''', '''tokenizer''']
__magic_name__ = '''ViTImageProc... | 3 | 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.set_verbosity_info()
__lowercase... | 43 | 0 |
'''simple docstring'''
def a_ ( lowerCamelCase : str , lowerCamelCase : str ):
lowerCAmelCase = len(lowerCamelCase )
lowerCAmelCase = []
for i in range(len(lowerCamelCase ) - pat_len + 1 ):
lowerCAmelCase = True
for j in range(lowerCame... | 4 | import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowercase = 1 / 100
__lowercase ... | 43 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transfor... | 5 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',... | 43 | 0 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __lowerCAmelCase ( a__ ) -> Dict:
return 1 / (1 + np.exp(-z ))
def __lowerCAmelCase ( a__ , ... | 6 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size... | 43 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 7 | from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 43 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 8 | import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase ... | 43 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format... | 9 | import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamel... | 43 | 0 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )
@pytest.mark.parametrize("revision" , ... | 10 | import math
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 43 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsM... | 11 | import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_C... | 43 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
el... | 12 | def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1_000 ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = 1
__UpperCamelCase :Any = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
__UpperCamelCase :list[i... | 43 | 0 |
import numpy as np
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
return np.where(vector > 0 , _UpperCAmelCase , (alpha * (np.exp(_UpperCAmelCase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 | import argparse
import json
from tqdm import tqdm
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=SCREAMING_SNAKE_CASE , default='''biencoder-nq-dev... | 43 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transf... | 14 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller th... | 43 | 0 |
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.utils i... | 15 | 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 Accele... | 43 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXL... | 16 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config... | 43 | 0 |
"""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 _lowerCAmelCase (... | 17 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 43 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Path , lowe... | 18 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLI... | 43 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A ={
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileBertConfig... | 19 | import numpy as np
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1e-12 , SCREAMING_SNAKE_CASE = 100 , ):
'''simple docstring'''
assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[1]
# Ensure proper dimensionality.... | 43 | 0 |
from __future__ import annotations
import numpy as np
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> tuple[np.ndarray, np.ndarray]:
lowercase , lowercase : Optional[int] = np.shape(SCREAMING_SNAKE_CASE__ )
if rows != columns:
lowercase : ... | 20 | import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 43 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> float:
_lowercase : Dict = 0
while len(lowerCamelCase_ ) > 1:
_lowercase : Dict = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
_lowercase :... | 21 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''... | 43 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__SCREAMING_SNAKE_CASE :Any = 50000
__SCREAMING_SNAKE_CASE :List[str] = 5000
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Dict = ... | 22 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available,... | 43 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: int = logging.get_logger(__name__)
UpperCamelCase__: List[Any] = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-... | 23 | def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = [0 for i in range(len(SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__UpperCamelCase , __UpperCamelCase :str = 0, 0
for i in range(1 ... | 43 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : float , snake_case_ : float , snake_case_ : float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only on... | 24 | import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
... | 43 | 0 |
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXL... | 25 | 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.set_verbosity_info()
__lowercase... | 43 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_snake_case = datasets.utils.logging.get_logge... | 26 | import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowercase = 1 / 100
__lowercase ... | 43 | 0 |
'''simple docstring'''
from math import factorial
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not po... | 27 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',... | 43 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
@staticmethod
@abstractmethod
def A ( UpperCamelCase__ : ArgumentParser ):
"... | 28 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size... | 43 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def lowercase__ ( __snake_case : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_a... | 29 | from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 43 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
# See all AltCLIP models at... | 30 | import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase ... | 43 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_com... | 31 | import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamel... | 43 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE_ ( __A : Callable[[int | float], int | float] , __A : int | float , __A : int | float , __A : int = 1_00 , ) -> float:
"""simple docstrin... | 32 | import math
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 43 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A : Tuple = {
'''configuration_trocr''': ['''TROCR... | 33 | import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_C... | 43 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case_ ():
UpperCAmelCase = ArgumentParser(
description=(
'''PyTorch... | 34 | def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1_000 ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = 1
__UpperCamelCase :Any = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
__UpperCamelCase :list[i... | 43 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__a = "Usage of script: script_name <size_of_canvas:int>"
__a = [0] * 100 + [1] * 10
random.shuffle(choice)
def __snake_case( ... | 35 | import argparse
import json
from tqdm import tqdm
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=SCREAMING_SNAKE_CASE , default='''biencoder-nq-dev... | 43 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperC... | 36 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller th... | 43 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gp... | 37 | 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 Accele... | 43 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase_ : Tuple = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(... | 38 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config... | 43 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a = logging.get_logger(__name__)
_a = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingfac... | 39 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 43 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_avail... | 40 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLI... | 43 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .sche... | 41 | import numpy as np
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1e-12 , SCREAMING_SNAKE_CASE = 100 , ):
'''simple docstring'''
assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[1]
# Ensure proper dimensionality.... | 43 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, 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... | 42 | import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 43 | 0 |
"""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_avai... | 44 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''... | 43 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers ... | 45 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available,... | 43 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcesso... | 46 | def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = [0 for i in range(len(SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__UpperCamelCase , __UpperCamelCase :str = 0, 0
for i in range(1 ... | 43 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : str ) -> str:
"""simple docstring"""
return "".join(chr(ord(_UpperCamelCase ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod... | 47 | import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
... | 43 | 0 |
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Any:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCamelCase : str = (boundary[1] - boundary[0]) / steps
lowerCamelCase : List[str] = boundary[0]... | 48 | 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.set_verbosity_info()
__lowercase... | 43 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
__a , __a = coefficient_matrix.shape
__a , __a ... | 49 | import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowercase = 1 / 100
__lowercase ... | 43 | 0 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distribut... | 50 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',... | 43 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : int = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class __sn... | 51 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size... | 43 | 0 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase , UpperCamelCase : List[Any] = 1, 1
UpperCamelCase : Union[str, Any] = 2
while True:
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : Tuple = fa + fa
UpperCamelCase , ... | 52 | from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 43 | 0 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import C... | 53 | import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase ... | 43 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, loa... | 54 | import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamel... | 43 | 0 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
... | 55 | import math
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 43 | 0 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : str = {
'kakaobrain/align-base': ... | 56 | import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_C... | 43 | 0 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple do... | 57 | def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1_000 ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = 1
__UpperCamelCase :Any = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
__UpperCamelCase :list[i... | 43 | 0 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : int = 10 , __lowerCamelCase : int = 1000 , __lowerCamelCase : bool = True ) ->int:
assert (
isinstance(__lowerCamelCase , __lowerCamelCase )
and isinstance(__lowerCamelCas... | 58 | import argparse
import json
from tqdm import tqdm
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=SCREAMING_SNAKE_CASE , default='''biencoder-nq-dev... | 43 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelC... | 59 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowercase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller th... | 43 | 0 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nno... | 60 | 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 Accele... | 43 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.