

import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      =CustomConfig




							def __init__(			self       ,    _A				):
												super().__init__(_A				)
												__lowerCAmelCase       =				torch.nn.Linear(config.hidden_size       ,    config.hidden_size				)




							def 							A__	(			self       ,    _A				):
												return self.linear(_A				)




							def 							A__	(			self       ,    _A				):
												pass





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      =NoSuperInitConfig




							def __init__(			self       ,    _A				):
												super().__init__(_A				)
												__lowerCAmelCase       =				torch.nn.Linear(config.attribute       ,    config.attribute				)




							def 							A__	(			self       ,    _A				):
												return self.linear(_A				)




							def 							A__	(			self       ,    _A				):
												pass









# 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 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					if name != "training_function" and name != "main":
										raise ValueError(F"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'"""   )
					__lowerCAmelCase,						__lowerCAmelCase       =				[], False
					for line in lines:
										if not found_start and F"""def {name}""" in line:
															__lowerCAmelCase       =				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 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					return [line for line in lines if not line.lstrip().startswith('#'   ) and line != "\n"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__ = None   ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(os.path.abspath(os.path.join('examples'		,       'nlp_example.py'   )   )		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					if secondary_filename is not None:
										with open(UpperCAmelCase__		,       'r'   ) as f:
															__lowerCAmelCase       =				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:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
					else:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(
															    get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )

					__lowerCAmelCase       =				'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
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # We keep track of the idxs just in case it's a repeated statement
					__lowerCAmelCase       =				iter(UpperCAmelCase__   )
					for i in range(len(UpperCAmelCase__   ) - 1   ):
										if i not in passed_idxs:
															__lowerCAmelCase       =				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
																									__lowerCAmelCase       =				next(UpperCAmelCase__   )

    # Extract out just the new parts from the full_file_training_func
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # 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
					__lowerCAmelCase       =				[line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										__lowerCAmelCase       =				[line for line in full_file_contents if line not in secondary_file_func]
										__lowerCAmelCase       =				[line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example






