code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase = None , lowercase = None ) -> Optional[Any]:
"""simple docstring"""
if start is None:
lowercase_ : Dict = 0
if end is None:
... | 458 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 1
while repunit:
SCREAMING_SNAKE_CASE_ = (10 * repunit + 1) % divisor
repunit_index += 1
return repuni... | 626 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class lowerCAmelCase ( _... | 655 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( __a, __a = None ):
SCREAMING_SNAKE_CASE_ = word_bank or []
# create a table
SCREAMING_SNAKE_CASE_ = len(__a ) + 1
SCREAMING_SNAKE_CASE_ = []
for _ in range(__a ):
tabl... | 626 | 0 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttent... | 388 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNL... | 626 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
@dataclass
class ... | 301 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def _lowerCamelCase ( __a, __a, __a, __a = 100, ):
SCREAMING_SNAKE_CASE_ = x_start
SCREAMING_SNAKE_CASE_ = fnc(__a )
SCREAMING_SNAKE_CASE_ = 0.0
for _ in ran... | 626 | 0 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase_ = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def _UpperCAmelCase ( ) -> List[Any]:
_lowerCAmelCase : str = os.path.dirname(os.path.realpath(__a ) )
_l... | 384 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision... | 626 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __UpperCAmelCase :
def __init__( self , _lowerCamelCase ):
lowerCamelCase__ =data
lowerCamelCase__ =None
class __UpperCAmelCase :
de... | 530 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( __a = "isbn/0140328726" ):
SCREAMING_SNAKE_CASE_ = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if n... | 626 | 0 |
import os
def _lowerCAmelCase ( __lowerCAmelCase = "matrix.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(__a ) , __a ) ) as in_file:
snake_case__ : Union[str, Any] = in_file.read()
snake_case__ : Union[s... | 252 |
"""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_cp
from torch.di... | 626 | 0 |
def UpperCamelCase ( __lowerCamelCase : List[Any] , __lowerCamelCase : Dict ):
snake_case : int = ""
for word_or_phrase in separated:
if not isinstance(__a , __a ):
raise Exception("join() accepts only ... | 204 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case ( __lowercase , ... | 626 | 0 |
'''simple docstring'''
from typing import Any
def snake_case ( a_ : int , a_ : Dict , a_ : Tuple , a_ : int , a_ : Optional[int] , ) -> int:
"""simple docstring"""
_validation(
__a , __a , __a , __a ... | 208 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def _lowerCamelCase ( __a ):
return input_array.reshape((input_array.size, 1) )
def _lowerCamelCase ( __a, __... | 626 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__ver... | 300 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class snake_case :
def __init__(self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE_ = [1, 2, 3, 4]
def... | 626 | 0 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( A__ , A__ , A__ , A__ = 100 , ) -> Any:
a__ : List[str] = x_start
a__ : int = fnc(__a )
a__ : str = 0.0
for _... | 302 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import pat... | 626 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def __magic_name__ ( lowercase ) -> int:
"""simple docstring"""
return input_array.reshape((input_array.size, ... | 458 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 626 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {
"""configuration_vision_text_dual_encoder""": ["""VisionTextDualEncoderConfig"""],
"""processi... | 655 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCAmelCase__ = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the m... | 626 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 388 |
"""simple docstring"""
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 (
... | 626 | 0 |
'''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_d... | 301 |
"""simple docstring"""
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 transfo... | 626 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCamelCase_ = """src/trans... | 384 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttentio... | 626 | 0 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib im... | 530 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowercase ):
UpperCAmelCase__ = (UnCLIPScheduler,)
def _lowercase (self , **SCREAMING_SNAKE_CASE_ ):
""... | 626 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderb... | 252 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqa... | 626 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( __lowercase ):
A__ : List[Any] = (UnCLIPScheduler,)
def _SCREAMING_SNAKE_CASE (self : Dict , **snake_case__ : ... | 204 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowerCamelCase ( __a ):
if num <= 0:
SCREAMING_SNAKE_CASE_ = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(__a )
SCREAMING_SNAKE_CASE_ = [True] * (num + 1)
... | 626 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
UpperCamelCase =logging.get_logger(__name__) # pylint: disable=invalid-name
class A ( _... | 208 |
"""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/LICENSE-2.0
#
# ... | 626 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 300 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=__lowercase ):
UpperCAmelCase__ = ['''note_seq''']
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
"""simple docstri... | 626 | 0 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase : List[str] = """src/transformers"""
# This is to make su... | 302 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 626 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 458 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 1
while repunit:
SCREAMING_SNAKE_CASE_ = (10 * repunit + 1) % divisor
repunit_index += 1
return repuni... | 626 | 0 |
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... | 655 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( __a, __a = None ):
SCREAMING_SNAKE_CASE_ = word_bank or []
# create a table
SCREAMING_SNAKE_CASE_ = len(__a ) + 1
SCREAMING_SNAKE_CASE_ = []
for _ in range(__a ):
tabl... | 626 | 0 |
"""simple docstring"""
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa:... | 388 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNL... | 626 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=__lowercase ):
lowercase = ["""note_seq"""]
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"... | 301 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def _lowerCamelCase ( __a, __a, __a, __a = 100, ):
SCREAMING_SNAKE_CASE_ = x_start
SCREAMING_SNAKE_CASE_ = fnc(__a )
SCREAMING_SNAKE_CASE_ = 0.0
for _ in ran... | 626 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a_ (__lowercase ):
__lowerCAmelC... | 384 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision... | 626 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a =logging.get_logger(__name__)
class __UpperCAmelCase ( __lowercase ):
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ):
... | 530 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( __a = "isbn/0140328726" ):
SCREAMING_SNAKE_CASE_ = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if n... | 626 | 0 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
A__ = importlib.util.find_spec('''s3fs''') is not None
if _has_safs:
from .safilesystem impor... | 252 |
"""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_cp
from torch.di... | 626 | 0 |
import math
__lowerCamelCase = 10
__lowerCamelCase = 7
__lowerCamelCase = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( __lowerCamelCase : List[Any] = 20 ):
snake_case : Any = math.comb(__a , __a )
snake_case : Option... | 204 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case ( __lowercase , ... | 626 | 0 |
'''simple docstring'''
from math import sqrt
def snake_case ( a_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
assert isinstance(__a , __a ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase_ ... | 208 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def _lowerCamelCase ( __a ):
return input_array.reshape((input_array.size, 1) )
def _lowerCamelCase ( __a, __... | 626 | 0 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
SCREAMING_SNAKE_CASE_ = namedtuple(
'_TestCommandArgs',
[
'dataset... | 300 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class snake_case :
def __init__(self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE_ = [1, 2, 3, 4]
def... | 626 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A__ ( __lowercase , unittest.TestCase ):
... | 302 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import pat... | 626 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 458 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 626 | 0 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
fro... | 655 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCAmelCase__ = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the m... | 626 | 0 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
Channel... | 388 |
"""simple docstring"""
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 (
... | 626 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class a_ ( __lowercase ):
lowercase = """SpeechT5FeatureExtractor"""
lowercase = """SpeechT5Tokenizer"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_... | 301 |
"""simple docstring"""
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 transfo... | 626 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
... | 384 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttentio... | 626 | 0 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Opt... | 530 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowercase ):
UpperCAmelCase__ = (UnCLIPScheduler,)
def _lowercase (self , **SCREAMING_SNAKE_CASE_ ):
""... | 626 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 252 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqa... | 626 | 0 |
def UpperCamelCase ( __lowerCamelCase : Union[str, Any] ):
def merge(__lowerCamelCase : int , __lowerCamelCase : List[Any] ) -> list:
def _merge():
while left and right:
yield (left if left[0] <=... | 204 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowerCamelCase ( __a ):
if num <= 0:
SCREAMING_SNAKE_CASE_ = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(__a )
SCREAMING_SNAKE_CASE_ = [True] * (num + 1)
... | 626 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identi... | 208 |
"""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/LICENSE-2.0
#
# ... | 626 | 0 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
SCREAMING_SNAK... | 300 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=__lowercase ):
UpperCAmelCase__ = ['''note_seq''']
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
"""simple docstri... | 626 | 0 |
from __future__ import annotations
from collections import deque
class A__ :
"""simple docstring"""
def __init__( self , lowercase) -> int:
'''simple docstring'''
a__ : Dict = []
self.adlist.append(
{'val... | 302 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 626 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 458 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 1
while repunit:
SCREAMING_SNAKE_CASE_ = (10 * repunit + 1) % divisor
repunit_index += 1
return repuni... | 626 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 655 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( __a, __a = None ):
SCREAMING_SNAKE_CASE_ = word_bank or []
# create a table
SCREAMING_SNAKE_CASE_ = len(__a ) + 1
SCREAMING_SNAKE_CASE_ = []
for _ in range(__a ):
tabl... | 626 | 0 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_t... | 388 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNL... | 626 | 0 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, ... | 301 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def _lowerCamelCase ( __a, __a, __a, __a = 100, ):
SCREAMING_SNAKE_CASE_ = x_start
SCREAMING_SNAKE_CASE_ = fnc(__a )
SCREAMING_SNAKE_CASE_ = 0.0
for _ in ran... | 626 | 0 |
'''simple docstring'''
from math import factorial
UpperCamelCase_ = {str(digit): factorial(digit) for digit in range(10)}
def _UpperCAmelCase ( _lowerCamelCase : Dict ) -> Any:
if not isinstance(__a , __a ):
raise TypeError("""Parameter number must be int""" )
i... | 384 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision... | 626 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:... | 530 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( __a = "isbn/0140328726" ):
SCREAMING_SNAKE_CASE_ = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if n... | 626 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : Optional[int] = [[0 for _ in range(__a )] for _ in range(m + 1 )]
for i in range(m + 1 ):
snake_case__ : Dict = 1
for n in range(m + 1 ):
... | 252 |
"""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_cp
from torch.di... | 626 | 0 |
from math import isclose, sqrt
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : Dict , __lowerCamelCase : Dict ):
snake_case : Tuple = point_y / 4 / point_x
snake_case : int = 2 * normal_gradient / (1 + nor... | 204 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case ( __lowercase , ... | 626 | 0 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict impor... | 208 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def _lowerCamelCase ( __a ):
return input_array.reshape((input_array.size, 1) )
def _lowerCamelCase ( __a, __... | 626 | 0 |
import inspect
import unittest
class a ( unittest.TestCase ):
def _UpperCAmelCase ( self ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def ... | 300 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class snake_case :
def __init__(self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE_ = [1, 2, 3, 4]
def... | 626 | 0 |
import requests
from bsa import BeautifulSoup
def A_ ( A__ , A__ ) -> List[Any]:
a__ : str = BeautifulSoup(requests.get(__a , params=__a ).content , 'html.parser' )
a__ : Optional[Any] = soup.find('div' , attr... | 302 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import pat... | 626 | 0 |
def __magic_name__ ( lowercase , lowercase ) -> str:
"""simple docstring"""
lowercase_ : str = """"""
for i in table:
res += inp[i - 1]
return res
def __magic_name__ ( lowercase ) -> Optional[int]:
... | 458 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 626 | 0 |
_snake_case = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""": """ABAAB"""... | 655 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCAmelCase__ = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the m... | 626 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accele... | 388 |
"""simple docstring"""
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 (
... | 626 | 0 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Any:
return int((input_a, input_a).count(0 ) == 0 )
def lowercase__ ( )-> List[Any]:
assert and_gate(0 , 0 ) == 0
assert... | 301 |
"""simple docstring"""
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 transfo... | 626 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seq... | 384 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttentio... | 626 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configu... | 530 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowercase ):
UpperCAmelCase__ = (UnCLIPScheduler,)
def _lowercase (self , **SCREAMING_SNAKE_CASE_ ):
""... | 626 | 0 |
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__ = logging.get_logger(__name__)
A__ = ... | 252 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqa... | 626 | 0 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] , __lowerCamelCase : str , __lowerCamelCase : Union[str, An... | 204 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowerCamelCase ( __a ):
if num <= 0:
SCREAMING_SNAKE_CASE_ = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(__a )
SCREAMING_SNAKE_CASE_ = [True] * (num + 1)
... | 626 | 0 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
... | 208 |
"""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/LICENSE-2.0
#
# ... | 626 | 0 |
class a :
def __init__( self ):
'''simple docstring'''
_UpperCAmelCase : int = 0
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Any = {}
def _UpperCAmelCase ( self , A_... | 300 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=__lowercase ):
UpperCAmelCase__ = ['''note_seq''']
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
"""simple docstri... | 626 | 0 |
import numpy as np
def A_ ( A__ ) -> Dict:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 302 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfor... | 626 | 0 |
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... | 458 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 1
while repunit:
SCREAMING_SNAKE_CASE_ = (10 * repunit + 1) % divisor
repunit_index += 1
return repuni... | 626 | 0 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if ... | 655 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( __a, __a = None ):
SCREAMING_SNAKE_CASE_ = word_bank or []
# create a table
SCREAMING_SNAKE_CASE_ = len(__a ) + 1
SCREAMING_SNAKE_CASE_ = []
for _ in range(__a ):
tabl... | 626 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_a... | 388 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNL... | 626 | 0 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a_ ( ... | 301 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def _lowerCamelCase ( __a, __a, __a, __a = 100, ):
SCREAMING_SNAKE_CASE_ = x_start
SCREAMING_SNAKE_CASE_ = fnc(__a )
SCREAMING_SNAKE_CASE_ = 0.0
for _ in ran... | 626 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Auto... | 384 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision... | 626 | 0 |
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a =version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
a ='\\n@inproceedings{b... | 530 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( __a = "isbn/0140328726" ):
SCREAMING_SNAKE_CASE_ = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if n... | 626 | 0 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.trai... | 252 |
"""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_cp
from torch.di... | 626 | 0 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__lowerCamelCase = {
"""tiny.en""": """https://openaipublic.azureedge.net/main/whisper/model... | 204 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case ( __lowercase , ... | 626 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class A :
"""simple docstring"""
__a : Dict = f... | 208 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def _lowerCamelCase ( __a ):
return input_array.reshape((input_array.size, 1) )
def _lowerCamelCase ( __a, __... | 626 | 0 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[int] ) -> str:
if len(__a ) < 2:
return collection
def circle_sort_util(lowerCAmelCase: Optional[int] , lowerCAmelCase: List[Any] , lowerCAmelCase: str ) -> bool:
_UpperCAmelCase : Dict = False
... | 300 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class snake_case :
def __init__(self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE_ = [1, 2, 3, 4]
def... | 626 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from trans... | 302 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import pat... | 626 | 0 |
import gc
import threading
import time
import psutil
import torch
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ) -> str:
"""simple docstring"""
lowercase_ : List[str] = psutil.Process()... | 458 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 626 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class lowerCAmelCase ( __lowercase , unittest.TestCase ):
__lowerCamelCase = DownBlockaD # noqa F405
__l... | 655 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCAmelCase__ = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the m... | 626 | 0 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 388 |
"""simple docstring"""
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 (
... | 626 | 0 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lower... | 627 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
fro... | 627 | 1 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def a__ ( a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(a__ )
assert noofclusters < len(a__ )
# Find out the dimensionality
__SCREAMING_S... | 627 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 627 | 1 |
'''simple docstring'''
import os
def a__ ( a__ = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(a__ ) , a__ ) ) as input_file:
__SCREAMING_SNAKE_CASE = [
[int(a__ ) for element in line.split(""",""" )]
... | 627 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCAmelCase__ ( a , a ):
"""simple docstring"""
@register_to_config
de... | 627 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCAmelCase__ ( a , a ):
"""simple docstring"""
@register_to_config
de... | 627 |
'''simple docstring'''
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,
)... | 627 | 1 |
'''simple docstring'''
from itertools import count
def a__ ( a__ = 50 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [1] * min_block_length
for n in count(a__ ):
fill_count_functions.append(1 )
for block_length in range(a__ , n + 1 ):
... | 627 |
'''simple docstring'''
import math
def a__ ( a__ ):
"""simple docstring"""
return math.sqrt(a__ ) * math.sqrt(a__ ) == num
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = n
while left <= ... | 627 | 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
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperC... | 627 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase : Any = 'src/transformers'
# This is to make sure the transfo... | 627 | 1 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def a__ ( a__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(a__ , 0 , a__ , args=(a__) )[0]
def a__ ... | 627 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
__SCREAM... | 627 | 1 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def a__ ( a__ , a__ = 0.0 , a__ = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 627 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 627 | 1 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def a__ ( a__ , a__=10_00 ):
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__SCREAMING_SNAKE_CASE = n - 1
... | 627 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def a__ ( a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = math.sqrt(a__ )
__SCREAMING_SNAKE_CASE = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img /... | 627 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase__ ( ... | 627 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if v... | 627 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils impo... | 627 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfi... | 627 | 1 |
'''simple docstring'''
def a__ ( a__ ):
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
__SCREAMING_SNAKE_CASE = sum(a__ ) / len(a__ ) # Calculate the average
return sum(abs(x - average ) for ... | 627 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Any = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main... | 627 | 1 |
'''simple docstring'''
import numpy
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : numpy.ndarray , __SCREAMING_SNAKE_CASE : numpy.ndarray ) -> None:
"""simple docstring"""
__SC... | 627 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittes... | 627 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( a ):
"""simple docstring"""
lowerCAmelCase__ = ["image_processor", "tokenizer"]
lowerCAmelCase__ = "CLIPImageProcesso... | 627 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase__ ( ... | 627 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAmelCase : Dict = {
'robert... | 627 |
'''simple docstring'''
UpperCAmelCase : Tuple = range(2, 2_0 + 1)
UpperCAmelCase : int = [1_0**k for k in range(ks[-1] + 1)]
UpperCAmelCase : dict[int, dict[int, list[list[int]]]] = {}
def a__ ( a__ , a__ , a__ , a__ ):
"""simple docstri... | 627 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = u
for i in range(1 , a__ ):
__SCREAMING_SNAKE_CASE = temp * (u - i)
return temp
def a__ ... | 627 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 627 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Tuple ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
... | 627 |
'''simple docstring'''
from math import isqrt
def a__ ( a__ ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(a__ ) + 1 ) )
def a__ ( a__ = 10**6 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
... | 627 | 1 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a__ ( a__ , a__ , a__ ):
"""simple docstring"""
__SCREAMING_... | 627 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : Any = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class lowerCAmelCase__ ( a ):
... | 627 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase__ ( a ):
"""simple docstring"""
def __init__( ... | 627 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common impor... | 627 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase : Optional[int] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase__ :
"""simple docstr... | 627 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( a ):
"""simple docstring"""
lowerCAmelCase__ = ["image_processor", "tokenizer"]
lowerCAmelCase__ = "CLIPImageProcesso... | 627 | 1 |
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