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 |
|---|---|---|---|---|
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 1 |
from __future__ import annotations
from statistics import mean
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = [0] * no_of_processes
snake_case_ = [0] * no_of_proce... | 285 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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_attention_mask
from... | 285 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 1 |
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,
OpenAIGPTDoubleHeadsMo... | 285 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 1 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
def count_of_possible_combinations(UpperCamelCase__ ) -> int:
if target < 0:
return 0
if target == 0:
... | 285 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ..... | 285 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface... | 285 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 1 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __lowerCamelCase ( UpperCamelCase__ = 3 ):
'''simple docstring'''
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
... | 285 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowerCamelCase ( UpperCamelCase__ = "laptop" ):
'''simple docstring'''
snake_case_ = F'''https://www.amazon.in/laptop/s?k={product}'''
snake_cas... | 285 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 1 |
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
from transformers.utils import... | 285 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=lowercase_ ):
__SCREAMING_SNAKE_CASE : Optional[int] = ['''flax''', '''transformers''']
def __init__( self , *snake_case , **snake_case ):
requires_backends(s... | 285 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_... | 285 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 1 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : int = get_tests_dir("""fixtures/test_sent... | 285 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 1 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 285 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 1 |
from collections.abc import Sequence
from queue import Queue
class lowercase :
def __init__( self , snake_case , snake_case , snake_case , snake_case=None , snake_case=None ):
snake_case_ = start
snake_case_ = end
... | 285 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : List[str] ... | 285 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 1 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_UpperCAmelCase : str = """src/transformers"""
_UpperCAmelCase : Dic... | 285 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 1 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging... | 285 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"""goog... | 285 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 285 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingf... | 285 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 1 |
from collections.abc import Generator
def __lowerCamelCase ( ):
'''simple docstring'''
snake_case_ , snake_case_ = 0, 1
while True:
snake_case_ , snake_case_ = b, a + b
yield b
def __lowerCamelCase ( UpperC... | 285 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {... | 285 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
Ber... | 285 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 1 |
# Lint as: python3
import itertools
import os
import re
_UpperCAmelCase : Any = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase : int = re.compile(R"""([a-z\d])([A-Z])""")
_UpperCAmelCase : List[Any] = re.compile(R"""(?<!_)_(?!_)""")
_UpperCAmelCase : ... | 285 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 1 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
for i in range(len(UpperCamelCase__ ) - 1 , 0 , -1 ):
snake_case_ = False
for j in range(UpperCamelCase__ , 0 , -1 ):
... | 285 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConf... | 285 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 285 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tr... | 285 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 1 |
import math
def __lowerCamelCase ( UpperCamelCase__ ):
'''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 numbers, all m... | 285 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 1 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_UpperCAmelCase : Dict = (
"""This metric will be removed from the library so... | 285 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 1 |
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 (
AutoProcessor,
BertTokeniz... | 285 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
fr... | 285 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForced... | 285 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : str = {}
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : Optional[int] = '''llam... | 285 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Tuple = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """De... | 285 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
def is_in_circle(UpperCamelCase__ , UpperCamelCase__ ) -> bool:
s... | 285 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and th... | 285 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 1 |
from torch import nn
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
... | 285 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class lowercase ( nn.Module )... | 285 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 1 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 1 |
import numpy as np
_UpperCAmelCase : Optional[Any] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""",... | 285 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 1 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ = n - k
# Calculate C(n,k)
... | 285 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Paddin... | 285 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
B... | 285 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_ve... | 285 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 1 |
import math
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase__ )
else:
... | 285 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatur... | 285 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_UpperCAmelCase : Dict = logging.get_logger(__name__)
class lowercase ( lowercase_ ):
def __init__( self , *snake_case , **snake_case ):
... | 285 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCamelCase ( UpperCamelCase__ , Up... | 285 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, An... | 285 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/re... | 285 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 1 |
_UpperCAmelCase : Tuple = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""":... | 285 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 1 |
import unittest
from transformers import LiltConfig, 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_modeling_common import ModelTesterMixin, ids_... | 285 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 1 |
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = int(np.ceil((x_end - x... | 285 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 1 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Train... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 1 |
from __future__ import annotations
from typing import Any
class lowercase :
def __init__( self , snake_case = 6 ):
snake_case_ = None
snake_case_ = None
self.create_linked_list(snake_case )
def a ( self... | 285 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ):
'''simple docstring'''
raise RuntimeError('CUDA out of memory.' )
... | 285 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTes... | 285 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
... | 285 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-handwr... | 285 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 1 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_t... | 285 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_UpperCAmelCase : Optional[int] = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .s... | 285 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
... | 285 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 1 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ = " " ):
'''simple docstring'''
snake_case_ = []
snake_case_ = 0
for index, char in enumerate(UpperCamelCase__ ):
if char == separator:
split... | 285 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 285 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 1 |
class lowercase :
def __init__( self , snake_case , snake_case=None , snake_case=None ):
snake_case_ = data
snake_case_ = previous
snake_case_ = next_node
def __str__( self ):
return F''... | 285 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=lowercase_ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *snake_case , **snake_case ):
requir... | 285 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 1 |
from bisect import bisect
from itertools import accumulate
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = sorted(zip(UpperCamelCase__ , UpperCame... | 285 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 1 |
from __future__ import annotations
from math import pi
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and o... | 285 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 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
... | 285 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 1 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = int(np.ceil((x_end - xa) / h ) )
snake_case... | 285 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 1 |
import qiskit
def __lowerCamelCase ( UpperCamelCase__ = 2 ):
'''simple docstring'''
snake_case_ = qubits
# Using Aer's simulator
snake_case_ = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q r... | 285 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
sma... | 285 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if... | 285 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
@wraps(UpperCamelCase__ )
def _inner_fn(*UpperCamelCase__ , **UpperCamelCase__ ):
warnings.warn(
... | 285 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenize... | 285 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impor... | 285 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 1 |
from functools import reduce
_UpperCAmelCase : int = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
... | 285 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils... | 285 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 1 |
import math
from datetime import datetime, timedelta
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = year % 19
snake_case_ = year % 4
snake_case_ = year % 7
snake_case_ = math.floor(year ... | 285 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 1 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if n_term == "":
return []
snake_case_ = []
for temp in range(int(UpperCamelCase__ ) ):
series.append(F'''1/{temp + 1}''' if series else '1' )
... | 285 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 285 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryT... | 285 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 1 |
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Dict = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingfa... | 285 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 1 |
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 lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE... | 285 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 1 |
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
from transformers.utils import... | 285 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""t5-small""": """https://hugg... | 285 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_UpperCAmelCase : Dict = logging.get_logger(__name__)
class lowercase ( lowercase_ ):
def __init__( self , *snake_case , **snake_case ):
... | 285 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Optional[int] = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_ava... | 285 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 285 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 1 |
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