code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class ... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCAmelCase , ... | 680 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list[int]:
if num <= 0:
raise ValueError("Input must be a positive integer" )
__snake_case = [True] * (num + 1)
__snake_case = 2
while p * p <= num:
if primes[p]:
f... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
__snake_case = len(bin(_UpperCAmelCase )[3:] )
__snake_case = bin(abs(_UpperCAmelCase ) - (1 << binary_number_length)... | 680 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
f... | 680 |
'''simple docstring'''
from timeit import timeit
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
__snake_case = 0
while number:
number &= number - 1
result += 1
r... | 680 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
a : Tuple = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : ... | 680 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 | 1 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] ) -> str:
__snake_case = int(_UpperCAmelCase )
assert noofclusters < len(_Uppe... | 680 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transf... | 680 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a : T... | 680 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 *... | 680 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __UpperCAmelCase ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , ... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : float ) -> float:
if edge <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
d... | 680 | 1 |
'''simple docstring'''
import math
def __UpperCAmelCase ( _UpperCAmelCase : list , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 ) -> list:
__snake_case = end or len(_UpperCAmelCase )
for i in range(_UpperCAmelCase , _UpperCAmelCase ... | 680 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transfor... | 680 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from... | 680 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : Dict , a_ : Union[str, Any] , a_ : Tuple ):
"""simple docstring"""
__snake_case = name
__snake_case = value
__snak... | 680 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | 680 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( _UpperCAmelCase : str = "base_exp.txt" ) -> int:
__snake_case = 0
__snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase )... | 680 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm impor... | 680 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = log... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Union[str, Any] = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
... | 680 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],... | 680 | 1 |
'''simple docstring'''
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray ) -> np.ndarray:
return vector * sigmoid(_UpperCAmelCase )
... | 680 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format,... | 680 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=_UpperCamelCase ):
__SCREAMING_SNAKE_CASE = ["""flax""", """transformers"""]
def __init__( self : int , *a_ : List[str] , **a_ : Opt... | 680 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( _UpperCAmelCase : Dict ... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[int] = {}
try:
if not is_sentencepiece... | 680 |
'''simple docstring'''
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
a : Union[str, Any] = logging.get_logger(__name__)
... | 680 | 1 |
'''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 SCREAMING_SNAKE_CASE__ ( _UpperCamel... | 680 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 1 |
'''simple docstring'''
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
a : Union[str, Any] = logging.get_logger(__name__)
... | 680 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 680 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, 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
... | 680 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 680 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = (DDPMParallelScheduler,)
def A ( self : List... | 680 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = ... | 680 | 1 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , a_ : int , a_ : int=None , a_ : Optional[Any]=None ):
"""simple docstring"""
__snake_case = data
__snake_case = previous
... | 680 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : Optional[Any] = False
class ... | 680 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
a : str = {
# 1536-bit
5: {
... | 680 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_... | 680 | 1 |
'''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_dif... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCAmelCase , ... | 680 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
fro... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
__snake_case = len(bin(_UpperCAmelCase )[3:] )
__snake_case = bin(abs(_UpperCAmelCase ) - (1 << binary_number_length)... | 680 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 2_00_00_00 ) -> 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 ... | 680 |
'''simple docstring'''
from timeit import timeit
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
__snake_case = 0
while number:
number &= number - 1
result += 1
r... | 680 | 1 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
... | 680 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bu... | 680 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transf... | 680 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Dict = logging.get_logger(__name__)
a : List[Any] = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.... | 680 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 *... | 680 | 1 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : float ) -> float:
if edge <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
d... | 680 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorForma... | 680 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 | 1 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : int ):
"""simple docstring"""
__snake_case = n
__snake_case = [None] * self.n
__snake_case = 0 # index of the first element
... | 680 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 | 1 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( _UpperCAmelCase : Dict ... | 680 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : Dict , a_ : Union[str, Any] , a_ : Tuple ):
"""simple docstring"""
__snake_case = name
__snake_case = value
__snak... | 680 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 680 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( _UpperCAmelCase : str = "base_exp.txt" ) -> int:
__snake_case = 0
__snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase )... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a : int = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCH... | 680 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = log... | 680 | 1 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_... | 680 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],... | 680 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | 680 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format,... | 680 | 1 |
'''simple docstring'''
from math import sqrt
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
__snake_case = True
# 0 and 1 ... | 680 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( _UpperCAmelCase : Dict ... | 680 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : list[list] ) -> list[list]:
__snake_case = current_set.copy()
for row_index, row in enumerate(_UpperCAmelCase ):
__snake_case = row[0]
for column_index, column in enumerate(_UpperCAmelCase ):
... | 680 |
'''simple docstring'''
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
a : Union[str, Any] = logging.get_logger(__name__)
... | 680 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module,... | 680 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : List[str] = logging.get_logger(__name__)... | 680 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 680 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a : Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : int ... | 680 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 680 | 1 |
'''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 __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : ... | 680 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = ... | 680 | 1 |
'''simple docstring'''
from manim import *
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Any ):
"""simple docstring"""
__snake_case = Rectangle(height=0.5 , width=0.5 )
__snake_case = Re... | 680 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : Optional[Any] = False
class ... | 680 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...g... | 680 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_... | 680 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
f... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCAmelCase , ... | 680 | 1 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str , a_ : list[int] ):
"""simple docstring"""
__snake_case = len(a_ )
__snake_case = [0] * len_array
if len_array > 0:
__s... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
__snake_case = len(bin(_UpperCAmelCase )[3:] )
__snake_case = bin(abs(_UpperCAmelCase ) - (1 << binary_number_length)... | 680 | 1 |
'''simple docstring'''
# Copyright 2021 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/LI... | 680 |
'''simple docstring'''
from timeit import timeit
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
__snake_case = 0
while number:
number &= number - 1
result += 1
r... | 680 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a : int ... | 680 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 | 1 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __UpperCAmelCase ( _UpperCAmelCase : str = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def __UpperCAmelCase ( _... | 680 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transf... | 680 | 1 |
'''simple docstring'''
from __future__ import annotations
a : List[Any] = 1.6021e-19 # units = C
def __UpperCAmelCase ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , ) -> tuple[str, float]:
if (conduc... | 680 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 *... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : float ) -> float:
if edge <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
d... | 680 | 1 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
a : Union[str, Any] = datasets.logging.get_logger(__name__)
a : Optional[Any] = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinh... | 680 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 | 1 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : Optional[Any] = False
class ... | 680 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 | 1 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : Dict , a_ : Union[str, Any] , a_ : Tuple ):
"""simple docstring"""
__snake_case = name
__snake_case = value
__snak... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a : List[str] = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 680 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( _UpperCAmelCase : str = "base_exp.txt" ) -> int:
__snake_case = 0
__snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase )... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Dict = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_tor... | 680 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = log... | 680 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
a : Tuple = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
... | 680 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],... | 680 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format,... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
a : List[str] = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', ''... | 680 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( _UpperCAmelCase : Dict ... | 680 | 1 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : Dict , a_ : ... | 680 |
'''simple docstring'''
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
a : Union[str, Any] = logging.get_logger(__name__)
... | 680 | 1 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a : Optional[int] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__SCREAMING_SNAKE_CASE ... | 680 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 1 |
'''simple docstring'''
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 SCREAMING_SNAKE_CASE__ ... | 680 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 680 | 1 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format,... | 680 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 680 | 1 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
a : int = logging.get_logger(__name__)
a : List[Any] = {name: getattr(trans... | 680 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = ... | 680 | 1 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
... | 680 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : Optional[Any] = False
class ... | 680 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing imp... | 680 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_... | 680 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedL... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCAmelCase , ... | 680 | 1 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
__snake_case = len(bin(_UpperCAmelCase )[3:] )
__snake_case = bin(abs(_UpperCAmelCase ) - (1 << binary_number_length)... | 680 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )... | 680 |
'''simple docstring'''
from timeit import timeit
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
__snake_case = 0
while number:
number &= number - 1
result += 1
r... | 680 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
a : Dict = False
class SCREAMING_SNAKE_CASE__ ( unittest.Te... | 680 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 | 1 |
'''simple docstring'''
from PIL import Image
def __UpperCAmelCase ( _UpperCAmelCase : Image , _UpperCAmelCase : int ) -> Image:
__snake_case = (2_59 * (level + 2_55)) / (2_55 * (2_59 - level))
def contrast(_UpperCAmelCase : int ) -> int:
return i... | 680 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transf... | 680 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : str = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json'''... | 680 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 *... | 680 | 1 |
'''simple docstring'''
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, ... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : float ) -> float:
if edge <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
d... | 680 | 1 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
a : List[str] = ''''''
a : int = ''''''
a : List[Any] = ''''''
a : Optional[int] = ''''''
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> None:
... | 680 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 | 1 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ... | 680 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 680 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : Dict , a_ : Union[str, Any] , a_ : Tuple ):
"""simple docstring"""
__snake_case = name
__snake_case = value
__snak... | 680 | 1 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set... | 680 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( _UpperCAmelCase : str = "base_exp.txt" ) -> int:
__snake_case = 0
__snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase )... | 680 | 1 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
fr... | 680 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = log... | 680 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Toke... | 680 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],... | 680 | 1 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple... | 680 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format,... | 680 | 1 |
'''simple docstring'''
from string import ascii_uppercase
a : List[str] = {char: i for i, char in enumerate(ascii_uppercase)}
a : str = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> ... | 680 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( _UpperCAmelCase : Dict ... | 680 | 1 |
'''simple docstring'''
import argparse
import copy
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> List[Any]:
__snake_case = {}
with open(_UpperCAmelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__snake_case ... | 680 |
'''simple docstring'''
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
a : Union[str, Any] = logging.get_logger(__name__)
... | 680 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CA... | 680 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 1 |
'''simple docstring'''
import os
import sys
import unittest
a : List[str] = 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:... | 680 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 680 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a : Any = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a : Tuple ... | 680 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 680 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 680 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = ... | 680 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : int = {
'''facebook/x... | 680 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : Optional[Any] = False
class ... | 680 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a : Any = ''''''
a : List[str] = ''''''
a : List[Any] = ''''''
a : List[Any] = 1 # (0 is vertical, 1 is horizontal)
def __Uppe... | 680 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_... | 680 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@da... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCAmelCase , ... | 680 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
__snake_case = len(bin(_UpperCAmelCase )[3:] )
__snake_case = bin(abs(_UpperCAmelCase ) - (1 << binary_number_length)... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
__snake_case = len(bin(_UpperCAmelCase )[3:] )
__snake_case = bin(abs(_UpperCAmelCase ) - (1 << binary_number_length)... | 680 | 1 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 |
'''simple docstring'''
from timeit import timeit
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
__snake_case = 0
while number:
number &= number - 1
result += 1
r... | 680 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : Dict = '''docs/source/en/_toctree.yml'''
def __UpperCAmelCase ( _UpperCAmelCase : Optional[int] ) -> Tuple:
__snake_case = defaultdict(_UpperCAmelCase )
__snake_... | 680 |
'''simple docstring'''
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : Dict = '''... | 680 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a : Dict = logging.get_logger... | 680 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transf... | 680 | 1 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
a : Any = '''<<<<<<< This should probably be modified because it mentions: '''
... | 680 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 *... | 680 | 1 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesC... | 680 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : float ) -> float:
if edge <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
d... | 680 | 1 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 680 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...u... | 680 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a : Union[str, Any] = [
'''Prosecutor: "No videos were used in the crash investigation" German ... | 680 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : Dict , a_ : Union[str, Any] , a_ : Tuple ):
"""simple docstring"""
__snake_case = name
__snake_case = value
__snak... | 680 | 1 |
'''simple docstring'''
# Copyright 2021 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/LI... | 680 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( _UpperCAmelCase : str = "base_exp.txt" ) -> int:
__snake_case = 0
__snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase )... | 680 | 1 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _... | 680 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = log... | 680 | 1 |
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