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 collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict ... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> float:
return 10 - x * x
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_UpperCAmelCase ) * equation(_Up... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 ... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[Any] = get_failure_array(_UpperCAmelCase )
# 2) Step through text searching for pattern
lowerCamelCase__ , lowerCamelCase__ ... | 45 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mod... | 45 |
from collections import deque
class lowerCAmelCase :
def __init__( self : str , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : int ) -> None:
lowerCamelCase__ : Optional[int] = process_name # process name
... | 45 | 1 |
class lowerCAmelCase : # Public class to implement a graph
def __init__( self : Tuple , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[bool]] ) -> None:
lowerCamelCase__ : Tuple = row
lowerCamelC... | 45 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 45 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Tuple:
# vision encoder
if "img_encoder.pos_embed" in name:
lowerCamelCase__ : Tuple =... | 45 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCAmelCase : Any = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig... | 45 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCAmelCase : Tuple = False
class lowerCAmelCase ( unittest.TestCase ):
def A_... | 45 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def A_ ( self : List[str] , UpperCAmelCase : Dict ) -> Optional[Any]:
lowerCamelCase__ : List[A... | 45 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig... | 45 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Tuple = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MA... | 45 | 1 |
import doctest
from collections import deque
import numpy as np
class lowerCAmelCase :
def __init__( self : List[Any] ) -> None:
lowerCamelCase__ : List[str] = [2, 1, 2, -1]
lowerCamelCase__ : int = [1, 2, 3, 4]
def A_ ( self : ... | 45 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCAmelCase ( pl.LightningModule ):
def __init__( self : List[str] , UpperCAmelCase : Optional[Any] ) ... | 45 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"... | 45 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
_UpperCAmelCase : Optional[Any] = logging.ge... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is eq... | 45 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = ... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_UpperCAmelCase ) == 0:
raise ValueError('Input list must be a non empt... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> str:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=0 ) -> Dict:
return sorted(_UpperCAmelCase , key... | 45 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vo... | 45 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 45 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : U... | 45 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_con... | 45 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 45 | 1 |
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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils imp... | 45 |
from bisect import bisect
from itertools import accumulate
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
lowerCamelCase__ : Optional[int] = sorted(zip(_UpperCAmelCase , _UpperCAmelCase ... | 45 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vo... | 45 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 45 | 1 |
from __future__ import annotations
import requests
_UpperCAmelCase : List[Any] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categ... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase__ : str = len(bin(_UpperCAmelCase )[3:] )
lowerCamelCase__ : Dict = bin(abs(_UpperCAmelCase ) - (1 << binary_number_... | 45 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase : str = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""":... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ) , _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 45 | 1 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 45 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTran... | 45 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Any ... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : int = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
""... | 45 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is eq... | 45 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 45 | 1 |
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
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 multiples of 3 are not primes
return False... | 45 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, Res... | 45 | 1 |
from math import factorial, pi
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase = 30 ) -> float:
if not isinstance(_UpperCAmelCase , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> float:
return 10 - x * x
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_UpperCAmelCase ) * equation(_Up... | 45 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = (DDPMParallelScheduler,)
def A_ ( self : List[Any] , **UpperCAmelCase : int )... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[Any] = get_failure_array(_UpperCAmelCase )
# 2) Step through text searching for pattern
lowerCamelCase__ , lowerCamelCase__ ... | 45 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_UpperCAmelCase : List[str] = datasets.util... | 45 |
from collections import deque
class lowerCAmelCase :
def __init__( self : str , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : int ) -> None:
lowerCamelCase__ : Optional[int] = process_name # process name
... | 45 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 45 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 45 | 1 |
_UpperCAmelCase : List[Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ... | 45 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCAmelCase : Any = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig... | 45 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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_tens... | 45 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def A_ ( self : List[str] , UpperCAmelCase : Dict ) -> Optional[Any]:
lowerCamelCase__ : List[A... | 45 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase ( unittest.TestCase ):
def A_ ( self : Tuple ) -> Union[str, Any]:
lowerCamelCase__ : Optional[Any] = [10, 20, 30, 40, 50, 60]
lowerCamelCase__ : Any = [2, ... | 45 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Tuple = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MA... | 45 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=__UpperCamelCase ):
UpperCAmelCase__ = ["""sentencepiece"""]
def __init__( self : Optional[Any] , *UpperCAmelCase : Optional[Any] , **UpperCAmelCase : List[str] ... | 45 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCAmelCase ( pl.LightningModule ):
def __init__( self : List[str] , UpperCAmelCase : Optional[Any] ) ... | 45 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCAmelCase ( __UpperCamelCase ):
def A_ ( self : List[str] ) -> Union[str, Any]:
return [
{"col_1": 3, "col_2": "a"},
{"c... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"... | 45 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.js... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is eq... | 45 | 1 |
from copy import deepcopy
class lowerCAmelCase :
def __init__( self : List[Any] , UpperCAmelCase : list[int] | None = None , UpperCAmelCase : int | None = None ) -> None:
if arr is None and size is not None:
lowerCamelCase__ : Option... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_UpperCAmelCase ) == 0:
raise ValueError('Input list must be a non empt... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> list[int]:
lowerCamelCase__ : Union[str, Any] = [0 for i in range(len(_UpperCAmelCase ) )]
# initialize interval's left pointer and right pointer
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = 0, 0
... | 45 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vo... | 45 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 45 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : U... | 45 | 1 |
from manim import *
class lowerCAmelCase ( __UpperCamelCase ):
def A_ ( self : int ) -> List[Any]:
lowerCamelCase__ : str = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase__ : List[Any] = Rectangle(height=0.2_5 , ... | 45 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 45 | 1 |
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100 ) -> int:
lowerCamelCase__ : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowerCamelCase__ : Optional[Any] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return ... | 45 |
from bisect import bisect
from itertools import accumulate
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
lowerCamelCase__ : Optional[int] = sorted(zip(_UpperCAmelCase , _UpperCAmelCase ... | 45 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_UpperCAmelCase : str = get_logger(__name__)
class lowerCAmelCase ( enum.Enum ):
UpperCAmelCase__ = """all_checks"""
UpperCAmelCa... | 45 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 45 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acc... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase__ : str = len(bin(_UpperCAmelCase )[3:] )
lowerCamelCase__ : Dict = bin(abs(_UpperCAmelCase ) - (1 << binary_number_... | 45 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Dict:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowerCAmelCase ( __U... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ) , _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 45 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1337 , num_examples=42 , dataset_name=... | 45 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTran... | 45 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class lowerCAmelCase ( __UpperCamelCase ):
def __init__( self : Optional[int] , UpperCAmelCase : str=None , **Up... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : int = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
""... | 45 | 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... | 45 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 45 | 1 |
from __future__ import annotations
_UpperCAmelCase : Union[str, Any] = 1.6021E-19 # units = C
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0... | 45 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, Res... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 6008_5147_5143 ) -> int:
try:
lowerCamelCase__ : Optional[int] = int(_UpperCAmelCase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise Valu... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> float:
return 10 - x * x
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_UpperCAmelCase ) * equation(_Up... | 45 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[Any] = get_failure_array(_UpperCAmelCase )
# 2) Step through text searching for pattern
lowerCamelCase__ , lowerCamelCase__ ... | 45 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"""roberta-base"... | 45 |
from collections import deque
class lowerCAmelCase :
def __init__( self : str , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : int ) -> None:
lowerCamelCase__ : Optional[int] = process_name # process name
... | 45 | 1 |
import torch
from torch import nn
class lowerCAmelCase ( nn.Module ):
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : ... | 45 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 45 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowerCAmelCase ( unittest.TestCase ):
def A_ ( self : Dict ) -> Any:
debug_launcher(test_script.main )
de... | 45 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCAmelCase : Any = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig... | 45 | 1 |
from collections.abc import Generator
def SCREAMING_SNAKE_CASE ( ) -> Generator[int, None, None]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = 0, 1
while True:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = b, a + b
yield... | 45 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def A_ ( self : List[str] , UpperCAmelCase : Dict ) -> Optional[Any]:
lowerCamelCase__ : List[A... | 45 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
fr... | 45 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Tuple = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MA... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
lowerCamelCase__ : List[str] = gray_code_sequenc... | 45 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCAmelCase ( pl.LightningModule ):
def __init__( self : List[str] , UpperCAmelCase : Optional[Any] ) ... | 45 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm i... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"... | 45 | 1 |
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> tuple[float | int, list[tuple[int, int]]]:
lowerCamelCase__ , lowerCamelCase__ : Any ... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is eq... | 45 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_UpperCAmelCase ) == 0:
raise ValueError('Input list must be a non empt... | 45 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : List[Any] = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Un... | 45 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vo... | 45 | 1 |
import math
import unittest
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True... | 45 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : U... | 45 | 1 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 45 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 45 | 1 |
from collections import deque
class lowerCAmelCase :
def __init__( self : str , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : int ) -> None:
lowerCamelCase__ : Optional[int] = process_name # process name
... | 45 |
from bisect import bisect
from itertools import accumulate
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
lowerCamelCase__ : Optional[int] = sorted(zip(_UpperCAmelCase , _UpperCAmelCase ... | 45 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 45 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 45 | 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_tenso... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase__ : str = len(bin(_UpperCAmelCase )[3:] )
lowerCamelCase__ : Dict = bin(abs(_UpperCAmelCase ) - (1 << binary_number_... | 45 | 1 |
import pprint
import requests
_UpperCAmelCase : Dict = """https://zenquotes.io/api"""
def SCREAMING_SNAKE_CASE ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def SCREAMING_SNAKE_CASE ( ) -> list:
return requests.get(API_ENDPOINT_URL... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ) , _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 45 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_co... | 45 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTran... | 45 | 1 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCAmelCase : Any = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : int = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
""... | 45 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_UpperCAmelCase : Tupl... | 45 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 45 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokeni... | 45 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, Res... | 45 | 1 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_S... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> float:
return 10 - x * x
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_UpperCAmelCase ) * equation(_Up... | 45 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : U... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[Any] = get_failure_array(_UpperCAmelCase )
# 2) Step through text searching for pattern
lowerCamelCase__ , lowerCamelCase__ ... | 45 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import t... | 45 |
from collections import deque
class lowerCAmelCase :
def __init__( self : str , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : int ) -> None:
lowerCamelCase__ : Optional[int] = process_name # process name
... | 45 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import In... | 45 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 45 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[Any] = {"""configuration_xglm""... | 45 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCAmelCase : Any = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig... | 45 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torc... | 45 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def A_ ( self : List[str] , UpperCAmelCase : Dict ) -> Optional[Any]:
lowerCamelCase__ : List[A... | 45 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ... | 45 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Tuple = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MA... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> list[int]:
if length <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(_UpperCAmelCase )]
if __name__ == "__main... | 45 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCAmelCase ( pl.LightningModule ):
def __init__( self : List[str] , UpperCAmelCase : Optional[Any] ) ... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_UpperCAmelCase ) == 0:
raise ValueError('Input list must be a non empt... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"... | 45 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase ( __UpperCamelCase ... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is eq... | 45 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCAmelCase : Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.c... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_UpperCAmelCase ) == 0:
raise ValueError('Input list must be a non empt... | 45 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowerCAmelCase ( unittest.TestCase ):
def A_ ... | 45 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vo... | 45 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance <... | 45 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : U... | 45 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 45 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 45 | 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
fro... | 45 |
from bisect import bisect
from itertools import accumulate
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
lowerCamelCase__ : Optional[int] = sorted(zip(_UpperCAmelCase , _UpperCAmelCase ... | 45 | 1 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 45 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 45 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slo... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase__ : str = len(bin(_UpperCAmelCase )[3:] )
lowerCamelCase__ : Dict = bin(abs(_UpperCAmelCase ) - (1 << binary_number_... | 45 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import Fla... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ) , _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 45 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , ... | 45 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTran... | 45 | 1 |
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 150_0000 ) -> int:
lowerCamelCase__ : defaultdict = defaultdict(_UpperCAmelCase )
lowerCamelCase__ : Tuple = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : int = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
""... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase__ : str = len(bin(_UpperCAmelCase )[3:] )
lowerCamelCase__ : Dict = bin(abs(_UpperCAmelCase ) - (1 << binary_number_... | 45 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 45 | 1 |
import random
class lowerCAmelCase :
@staticmethod
def A_ ( UpperCAmelCase : str ) -> tuple[list[int], list[int]]:
lowerCamelCase__ : Optional[Any] = [ord(UpperCAmelCase ) for i in text]
lowerCamelCase__ : Optional[int] = []
lowerCa... | 45 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, Res... | 45 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> float:
return 10 - x * x
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_UpperCAmelCase ) * equation(_Up... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
lowerCamelCase__ : Dict = [int(_UpperCAmelCase ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(_UpperCAmelCase ) == 4 and all(0 <= int(_UpperCAmelCase ) <= 254 for octet in octets )
if __name__ == "__... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[Any] = get_failure_array(_UpperCAmelCase )
# 2) Step through text searching for pattern
lowerCamelCase__ , lowerCamelCase__ ... | 45 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 45 |
from collections import deque
class lowerCAmelCase :
def __init__( self : str , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : int ) -> None:
lowerCamelCase__ : Optional[int] = process_name # process name
... | 45 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.config... | 45 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 45 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def A_ ( self : List[str] , UpperCAmelCase : Dict ) -> Optional[Any]:
lowerCamelCase__ : List[A... | 45 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_UpperCAmelCase : Any = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig... | 45 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 45 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def A_ ( self : List[str] , UpperCAmelCase : Dict ) -> Optional[Any]:
lowerCamelCase__ : List[A... | 45 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class ... | 45 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Tuple = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MA... | 45 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
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 multiples of 3... | 45 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCAmelCase ( pl.LightningModule ):
def __init__( self : List[str] , UpperCAmelCase : Optional[Any] ) ... | 45 | 1 |
import os
def SCREAMING_SNAKE_CASE ( ) -> Dict:
lowerCamelCase__ : int = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
lowerCamelCase__ : Optional[int] = os.path.join(_UpperCAmelCase , 'triangle.txt' )
with open(_UpperCAmelCase ) as f:
... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"... | 45 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> str:
lowerCamelCase__ : Union[str, Any] = ''
for word_or_phrase in separated:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise Exception('join() accepts only string... | 45 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is eq... | 45 | 1 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class lowerCAmelCase ( __UpperCamelCase ):
def __init__( self : Any , *UpperCAmelCase : Optional[A... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_UpperCAmelCase ) == 0:
raise ValueError('Input list must be a non empt... | 45 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_Upp... | 45 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vo... | 45 | 1 |
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