code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
# TODO: upload to AWS
lowerCAmelCase_ = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/c... | 371 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = ''''''
snake_case_ = (
None # pr... | 332 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCAmelCase_ = logging.get_logger(_... | 350 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A , A , A ) -> int | float:
if len(A ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(A )
or left < -len(A )
or right >= len(A ... | 332 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTest... | 351 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder,... | 332 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase_ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def __m... | 352 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCAmelCase_ ... | 353 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
... | 332 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def __magic_name__ ( A ) -> int:
snake_case = {key: len(A ) for key, value in gen_kwargs.items() if isinstance(A , A )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
... | 354 |
'''simple docstring'''
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
fr... | 332 | 0 |
def __magic_name__ ( A , A , A , A ) -> Any:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case = mf_knapsack(i - 1 , A , A , A )
else:
snake_case = max(
mf_knapsack(i - 1 , A ,... | 355 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( A , A , A ) -> Any:
# Initialise PyTorch model
snake_c... | 332 | 0 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
def __init__( self, lowercase_ ) -> Any:
snake_case = str(id_ )
snake_case = None
snake_case = None
snake_case = []
s... | 356 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in ra... | 332 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common i... | 357 |
'''simple docstring'''
def __magic_name__ ( A ) -> float:
return 1_0 - x * x
def __magic_name__ ( A , A ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(A ) * equation(A ) >= 0:
raise ValueError('Wrong ... | 332 | 0 |
'''simple docstring'''
def __magic_name__ ( A ) -> bool:
snake_case = (1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def __magic_name__ ( A = 5_0_0_0 ) -> int:
snake_case = [(i * (3 * i - 1)) // 2 for i in range(1 , A )]
for i, ... | 358 |
'''simple docstring'''
import pytest
lowerCAmelCase_ = "__dummy_dataset1__"
lowerCAmelCase_ = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", ... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> bool:
return len(set(A ) ) == len(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global process_lock... | 332 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attenti... | 360 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> None:
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def __magic_name__ ( A , A , A , A , ) -> None:
if index ... | 332 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lo... | 361 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 332 | 0 |
'''simple docstring'''
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0 for i in range(n + 1 )]
snake_case = 1
snake_case = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , ... | 362 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrate... | 332 | 0 |
'''simple docstring'''
import unittest
from transformers import 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 ModelTeste... | 363 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __magic_name__ ( A ) -> Tuple:
snake_case ... | 332 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"kssteven/ibert-roberta-ba... | 364 |
'''simple docstring'''
from pathlib import Path
import fire
def __magic_name__ ( A , A , A ) -> Union[str, Any]:
snake_case = Path(A )
snake_case = Path(A )
dest_dir.mkdir(exist_ok=A )
for path in src_dir.iterdir():
snake_case = [... | 332 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@... | 365 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCAmelCase_ = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 332 | 0 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
... | 332 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 367 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import Lear... | 332 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCAmelCase )
class lowerCamelCase ( __lowerCAmelCase ):
# `task` is not a Cl... | 368 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=__lowerCAmelCase ):
snake_case_ = ['''note_seq''']
def __init__( self, *lowercase_, **lowercase_ ) -> str:
requires_backends(self, ['note_seq'] )
@cla... | 332 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/... | 369 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase ( __lowerCAmelCase ):
def __init__( self, *lowercase_, **lowercase_ ) -> None:
war... | 332 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase ( __lowerCAmelCase ):
def __init__( self, lowercase_, lowercase_ ) -> Tuple:
super().__init__()
self.register_modul... | 370 |
'''simple docstring'''
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... | 332 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Data... | 371 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = ''''''
snake_case_ = (
None # pr... | 332 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def __magic_name__ ( A ) -> str:
def decorator(A ):
snake_case = getattr(A , 'handle_key' , [] )
handle += [key]
setattr(A , 'handle_key' , ... | 350 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A , A , A ) -> int | float:
if len(A ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(A )
or left < -len(A )
or right >= len(A ... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCamelCase :
snake_case_ = 42
snake_case_ = None
snake_case_ = None
lowerCAmelCase_ = namedtuple("CoinsD... | 351 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder,... | 332 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolv... | 352 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase_ = logging.g... | 353 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
... | 332 | 0 |
'''simple docstring'''
def __magic_name__ ( A ) -> Optional[Any]:
snake_case = len(A )
while cur > 1:
# Find the maximum number in arr
snake_case = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
snake_case = arr[mi::-1] + arr[mi + 1 ... | 354 |
'''simple docstring'''
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
fr... | 332 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase_ = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowerCAmelCase_ = _LazyModule(_... | 355 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( A , A , A ) -> Any:
# Initialise PyTorch model
snake_c... | 332 | 0 |
'''simple docstring'''
import argparse
import json
import subprocess
def __magic_name__ ( A , A ) -> Optional[Any]:
snake_case = []
snake_case = (
F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
' https://api.github.... | 356 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in ra... | 332 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]}
... | 357 |
'''simple docstring'''
def __magic_name__ ( A ) -> float:
return 1_0 - x * x
def __magic_name__ ( A , A ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(A ) * equation(A ) >= 0:
raise ValueError('Wrong ... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A , A , A , A , ) -> list[float]:
snake_case , snake_case = coefficient_matrix.shape
snake_case , snake_case ... | 358 |
'''simple docstring'''
import pytest
lowerCAmelCase_ = "__dummy_dataset1__"
lowerCAmelCase_ = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", ... | 332 | 0 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __magic_name__ ( A ) -> float:
return np.dot(A , A )
class lowerCamelCase :
def __init__( self, *,
lowercase_ = np.inf, lowe... | 359 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global process_lock... | 332 | 0 |
'''simple docstring'''
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils i... | 360 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> None:
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def __magic_name__ ( A , A , A , A , ) -> None:
if index ... | 332 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 361 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 332 | 0 |
'''simple docstring'''
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
lowerCAmelCase_ = logging.get_logger(__name__)
def __magic_name__ ( A ) -> int:
snake_case = ... | 362 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrate... | 332 | 0 |
'''simple docstring'''
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
fro... | 363 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __magic_name__ ( A ) -> Tuple:
snake_case ... | 332 | 0 |
'''simple docstring'''
import pytest
lowerCAmelCase_ = "__dummy_dataset1__"
lowerCAmelCase_ = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", ... | 364 |
'''simple docstring'''
from pathlib import Path
import fire
def __magic_name__ ( A , A , A ) -> Union[str, Any]:
snake_case = Path(A )
snake_case = Path(A )
dest_dir.mkdir(exist_ok=A )
for path in src_dir.iterdir():
snake_case = [... | 332 | 0 |
'''simple docstring'''
import os
def __magic_name__ ( A ) -> List[Any]:
snake_case = len(grid[0] )
snake_case = len(A )
snake_case = 0
snake_case = 0
snake_case = 0
# Check vertically, horizontally, diagonally at the same time ... | 365 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCAmelCase_ = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 332 | 0 |
'''simple docstring'''
lowerCAmelCase_ = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/hugg... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
... | 332 | 0 |
'''simple docstring'''
import os
import sys
import unittest
lowerCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_te... | 367 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import Lear... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( A ) -> int:
if not nums:
return 0
snake_case = nums[0]
snake_case = 0
for num in nums[1:]:
snake_case , snake_case = (
max_excluding + num,
max(A , A ),
... | 368 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=__lowerCAmelCase ):
snake_case_ = ['''note_seq''']
def __init__( self, *lowercase_, **lowercase_ ) -> str:
requires_backends(self, ['note_seq'] )
@cla... | 332 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase ( __lowerCAmelCase ):
def __init__( self, *lowercase_, **lowercase_ ... | 369 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase ( __lowerCAmelCase ):
def __init__( self, *lowercase_, **lowercase_ ) -> None:
war... | 332 | 0 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def __magic_name__ ( A , A = "cpu" , A = None ) -> None:
snake_case = torch.load(A , map_location=A )
for k, v in tqdm(state_dict.items() ):
if not isinstan... | 370 |
'''simple docstring'''
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... | 332 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
fr... | 371 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = ''''''
snake_case_ = (
None # pr... | 332 | 0 |
'''simple docstring'''
class lowerCamelCase ( __lowerCAmelCase ):
pass
class lowerCamelCase ( __lowerCAmelCase ):
pass
class lowerCamelCase :
def __init__( self ) -> Dict:
snake_case = [
[],
[],
[],... | 350 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A , A , A ) -> int | float:
if len(A ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(A )
or left < -len(A )
or right >= len(A ... | 332 | 0 |
'''simple docstring'''
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
lowerCAmelCase_ = "facebook/wmt19-en-de"
lowerCAmelCase_ = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCAmelCase_ = FSMTConfig.fr... | 351 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder,... | 332 | 0 |
'''simple docstring'''
def __magic_name__ ( A ) -> List[Any]:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
snake_case = len(A )
snake_case = max(A )
snake_case =... | 352 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_availabl... | 353 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
... | 332 | 0 |
'''simple docstring'''
import math
import random
def __magic_name__ ( A , A = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowerCAmelCase_ = 0.02
def __magic_name__ ( A , A ) -> ... | 354 |
'''simple docstring'''
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
fr... | 332 | 0 |
def __magic_name__ ( A ) -> list:
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(A ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(A , sequence[1... | 355 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( A , A , A ) -> Any:
# Initialise PyTorch model
snake_c... | 332 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __magic_name__ ( A ) -> List... | 356 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in ra... | 332 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = (DDPMScheduler,)
def _lowerCamelCase ( self, **lowercase_ ) -> List[str]:
snake_cas... | 357 |
'''simple docstring'''
def __magic_name__ ( A ) -> float:
return 1_0 - x * x
def __magic_name__ ( A , A ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(A ) * equation(A ) >= 0:
raise ValueError('Wrong ... | 332 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers... | 358 |
'''simple docstring'''
import pytest
lowerCAmelCase_ = "__dummy_dataset1__"
lowerCAmelCase_ = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", ... | 332 | 0 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_ava... | 359 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global process_lock... | 332 | 0 |
'''simple docstring'''
import pprint
import requests
lowerCAmelCase_ = "https://zenquotes.io/api"
def __magic_name__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def __magic_name__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/... | 360 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> None:
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def __magic_name__ ( A , A , A , A , ) -> None:
if index ... | 332 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __magic_name__ ( A ) -> Tuple:
snake_case ... | 361 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 332 | 0 |
'''simple docstring'''
import os
def __magic_name__ ( A = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(A ) , A ) ) as input_file:
snake_case = [
[int(A ) for element in line.split(',' )]
for line in input_file.readli... | 362 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrate... | 332 | 0 |
'''simple docstring'''
import numpy as np
def __magic_name__ ( A ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __magic_name__ ( A ) -> Tuple:
snake_case ... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
class lowerCamelCase :
def __init__( self, lowercase_ = 0 ) -> Optional[int]:
snake_case = key
def _lowerCamelCase ( self, lowercase_, lowercase_ ) -> list[str]:
assert isinstance(lowercase_, lower... | 364 |
'''simple docstring'''
from pathlib import Path
import fire
def __magic_name__ ( A , A , A ) -> Union[str, Any]:
snake_case = Path(A )
snake_case = Path(A )
dest_dir.mkdir(exist_ok=A )
for path in src_dir.iterdir():
snake_case = [... | 332 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"configuration_xmod": [
"XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XmodConfig",
"XmodOnnxConfig",
],
}
try:
i... | 365 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCAmelCase_ = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 332 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase_ ... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
... | 332 | 0 |
'''simple docstring'''
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def __magic_n... | 367 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import Lear... | 332 | 0 |
'''simple docstring'''
lowerCAmelCase_ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCa... | 368 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=__lowerCAmelCase ):
snake_case_ = ['''note_seq''']
def __init__( self, *lowercase_, **lowercase_ ) -> str:
requires_backends(self, ['note_seq'] )
@cla... | 332 | 0 |
'''simple docstring'''
from functools import reduce
lowerCAmelCase_ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"1254069874715852386305071569329096329522744304355... | 369 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase ( __lowerCAmelCase ):
def __init__( self, *lowercase_, **lowercase_ ) -> None:
war... | 332 | 0 |
'''simple docstring'''
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 370 |
'''simple docstring'''
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... | 332 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase_ = datasets.utils.logging.get_logger(__name__)
class lowerCamelCase ( folder_based_builder.FolderBasedBuilderConfig ):
snake_case... | 371 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = ''''''
snake_case_ = (
None # pr... | 332 | 0 |
'''simple docstring'''
def __magic_name__ ( A ) -> list[int]:
snake_case = len(A )
for i in range(A ):
for j in range(i + 1 , A ):
if numbers[j] < numbers[i]:
snake_case , snake_case = numbers[j], numbers[i]
return numbers
if... | 350 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A , A , A ) -> int | float:
if len(A ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(A )
or left < -len(A )
or right >= len(A ... | 332 | 0 |
'''simple docstring'''
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 OptionalDepende... | 351 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder,... | 332 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestra... | 352 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 0 |
'''simple docstring'''
from torch import nn
def __magic_name__ ( UpperCamelCase_ ) -> int:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'''Unsupported activation function: ... | 353 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
... | 332 | 0 |
'''simple docstring'''
import math
def __magic_name__ ( A , A ) -> int:
snake_case = len(A )
snake_case = int(math.floor(math.sqrt(A ) ) )
snake_case = 0
while arr[min(A , A ) - 1] < x:
snake_case = step
step... | 354 |
'''simple docstring'''
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
fr... | 332 | 0 |
def __magic_name__ ( A = 6_0_0_8_5_1_4_7_5_1_4_3 ) -> int:
try:
snake_case = int(A )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater than or equal to one.' ... | 355 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( A , A , A ) -> Any:
# Initialise PyTorch model
snake_c... | 332 | 0 |
'''simple docstring'''
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 __magic_name__ ( ) -> Optional[int]:
raise RuntimeError('CUDA out of memory.' )
... | 356 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in ra... | 332 | 0 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( A ) -> tuple:
... | 357 |
'''simple docstring'''
def __magic_name__ ( A ) -> float:
return 1_0 - x * x
def __magic_name__ ( A , A ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(A ) * equation(A ) >= 0:
raise ValueError('Wrong ... | 332 | 0 |
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( A , A ) -> bool:
snake_case = first_str.lower().strip()
snake_case = second_str.lower().strip()
# Remove whitespace
snake_case = first_str.replace(' ' , '' )
snake... | 358 |
'''simple docstring'''
import pytest
lowerCAmelCase_ = "__dummy_dataset1__"
lowerCAmelCase_ = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", ... | 332 | 0 |
'''simple docstring'''
def __magic_name__ ( A ) -> int:
snake_case = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __magic_name__ ( A = 1_0_0 ) -> int:
snake_case = 1
snake_case = 2
for i in range(2 , ... | 359 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global process_lock... | 332 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 360 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> None:
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def __magic_name__ ( A , A , A , A , ) -> None:
if index ... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> None:
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def __magic_name__ ( A , A , A , A , ) -> None:
if index ... | 361 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 332 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available()... | 362 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrate... | 332 | 0 |
'''simple docstring'''
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... | 363 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __magic_name__ ( A ) -> Tuple:
snake_case ... | 332 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCAmelCase_ = collections.namedtuple("_Datasets"... | 364 |
'''simple docstring'''
from pathlib import Path
import fire
def __magic_name__ ( A , A , A ) -> Union[str, Any]:
snake_case = Path(A )
snake_case = Path(A )
dest_dir.mkdir(exist_ok=A )
for path in src_dir.iterdir():
snake_case = [... | 332 | 0 |
'''simple docstring'''
def __magic_name__ ( A = 3 , A = 7 , A = 1_0_0_0_0_0_0 ) -> int:
snake_case = 0
snake_case = 1
for current_denominator in range(1 , limit + 1 ):
snake_case = current_denominator * numerator // denominator
if curren... | 365 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCAmelCase_ = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 332 | 0 |
'''simple docstring'''
import os
from pathlib import Path
def __magic_name__ ( ) -> str:
from torch.utils.cpp_extension import load
snake_case = Path(A ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
snake_case = [
root / filename
for filename... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
... | 332 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
def __magic_name__ ( A ) -> bytes:
snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
snake_case = requests.get(base_url + url ).json()[0]['urls'][0]['src']
retur... | 367 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import Lear... | 332 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils... | 368 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=__lowerCAmelCase ):
snake_case_ = ['''note_seq''']
def __init__( self, *lowercase_, **lowercase_ ) -> str:
requires_backends(self, ['note_seq'] )
@cla... | 332 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/re... | 369 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase ( __lowerCAmelCase ):
def __init__( self, *lowercase_, **lowercase_ ) -> None:
war... | 332 | 0 |
'''simple docstring'''
lowerCAmelCase_ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCa... | 370 |
'''simple docstring'''
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... | 332 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW... | 371 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = ''''''
snake_case_ = (
None # pr... | 332 | 0 |
'''simple docstring'''
from manim import *
class lowerCamelCase ( __lowerCAmelCase ):
def _lowerCamelCase ( self ) -> Optional[Any]:
snake_case = Rectangle(height=0.5, width=0.5 )
snake_case = Rectangle(height=0.46, width=0.46 ).set_... | 350 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A , A , A ) -> int | float:
if len(A ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(A )
or left < -len(A )
or right >= len(A ... | 332 | 0 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrate... | 351 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder,... | 332 | 0 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global pr... | 352 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProc... | 353 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
... | 332 | 0 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy... | 354 |
'''simple docstring'''
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
fr... | 332 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase ( __lowerCAmelCase , __lowerCAmelCase ... | 355 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( A , A , A ) -> Any:
# Initialise PyTorch model
snake_c... | 332 | 0 |
'''simple docstring'''
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 OptionalDependencyNotAva... | 356 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in ra... | 332 | 0 |
'''simple docstring'''
from collections.abc import Callable
def __magic_name__ ( A , A , A ) -> float:
snake_case = a
snake_case = b
if function(A ) == 0: # one of the a or b is a root for the function
return a
elif function(A ) == 0:
return b... | 357 |
'''simple docstring'''
def __magic_name__ ( A ) -> float:
return 1_0 - x * x
def __magic_name__ ( A , A ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(A ) * equation(A ) >= 0:
raise ValueError('Wrong ... | 332 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=__lowerCAmelCase ):
snake_case_ = ['''note_seq''']
def __init__( self, *lowercase_, **lowercase_ ) -> str:
requires_backends(self, ['note_seq'] )
@cla... | 358 |
'''simple docstring'''
import pytest
lowerCAmelCase_ = "__dummy_dataset1__"
lowerCAmelCase_ = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", ... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class lowerCamelCase :
snake_case_ = 42
snake_case_ = None
snake_case_ = ... | 359 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def __magic_name__ ( A , A , A , A , A , A , A ) -> Any:
global process_lock... | 332 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase ( __lowerCAmelCase ):
snake_cas... | 360 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> None:
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def __magic_name__ ( A , A , A , A , ) -> None:
if index ... | 332 | 0 |
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