


'''simple docstring'''



# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     int  ):






					if name != "training_function" and name != "main":
										raise ValueError(F"""Incorrect function name passed: {name}, choose either \'main\' or \'training_function\'"""  )
					__a					,       __a    : List[Any]      					=						[], False
					for line in lines:
										if not found_start and F"""def {name}""" in line:
															__a    : Any      					=						True
															good_lines.append(UpperCamelCase_  )
															continue
										if found_start:
															if name == "training_function" and "def main" in line:
																				return good_lines
															if name == "main" and "if __name__" in line:
																				return good_lines
															good_lines.append(UpperCamelCase_  )






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]  ):
					return [line for line in lines if not line.lstrip().startswith('#'  ) and line != "\n"]






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Any = None  ):






					with open(UpperCamelCase_		,   'r'  ) as f:
										__a    : Any      					=						f.readlines()
					with open(os.path.abspath(os.path.join('examples'		,   'nlp_example.py'  )  )		,   'r'  ) as f:
										__a    : List[Any]      					=						f.readlines()
					with open(UpperCamelCase_		,   'r'  ) as f:
										__a    : List[Any]      					=						f.readlines()
					if secondary_filename is not None:
										with open(UpperCamelCase_		,   'r'  ) as f:
															__a    : List[Any]      					=						f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
					if parser_only:
										__a    : Any      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
										__a    : Union[str, Any]      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
										__a    : Dict      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
										if secondary_filename is not None:
															__a    : Dict      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
					else:
										__a    : int      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )
										__a    : Union[str, Any]      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )
										__a    : Optional[Any]      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )
										if secondary_filename is not None:
															__a    : Union[str, Any]      					=						clean_lines(
															    get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )

					__a    : Tuple      					=						'train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'

					# Specific code in our script that differs from the full version, aka what is new
					__a    : int      					=						[]
					__a    : Union[str, Any]      					=						[]  # We keep track of the idxs just in case it's a repeated statement
					__a    : Tuple      					=						iter(UpperCamelCase_  )
					for i in range(len(UpperCamelCase_  ) - 1  ):
										if i not in passed_idxs:
															__a    : Any      					=						next(UpperCamelCase_  )
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(UpperCamelCase_  )
																									passed_idxs.append(UpperCamelCase_  )
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									__a    : Dict      					=						next(UpperCamelCase_  )

    # Extract out just the new parts from the full_file_training_func
					__a    : Union[str, Any]      					=						[]
					__a    : int      					=						[]  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(UpperCamelCase_  ):
										if i not in passed_idxs:
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_full_example_parts.append(UpperCamelCase_  )
																									passed_idxs.append(UpperCamelCase_  )

    # Finally, get the overall diff
					__a    : Union[str, Any]      					=						[line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										__a    : str      					=						[line for line in full_file_contents if line not in secondary_file_func]
										__a    : int      					=						[line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase			:		Optional[int] =			[
	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VisualBertForMultipleChoice',
	    'VisualBertForPreTraining',
	    'VisualBertForQuestionAnswering',
	    'VisualBertForRegionToPhraseAlignment',
	    'VisualBertForVisualReasoning',
	    'VisualBertLayer',
	    'VisualBertModel',
	    'VisualBertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_visual_bert import (
		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VisualBertForMultipleChoice,
		    VisualBertForPreTraining,
		    VisualBertForQuestionAnswering,
		    VisualBertForRegionToPhraseAlignment,
		    VisualBertForVisualReasoning,
		    VisualBertLayer,
		    VisualBertModel,
		    VisualBertPreTrainedModel,
		)


else:
	import sys

	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



