




'''simple docstring'''

import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
a  :				Optional[int]									=  logging.getLogger(__name__)


a  :				Optional[Any]									=  'Hello world! cécé herlolip'


a  :				List[str]									=  namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				BertAbsConfig(
					    temp_dir='''.''',      finetune_bert=__UpperCAmelCase,      large=__UpperCAmelCase,      share_emb=__UpperCAmelCase,      use_bert_emb=__UpperCAmelCase,      encoder='''bert''',      max_pos=512,      enc_layers=6,      enc_hidden_size=512,      enc_heads=8,      enc_ff_size=512,      enc_dropout=0.2,      dec_layers=6,      dec_hidden_size=768,      dec_heads=8,      dec_ff_size=2048,      dec_dropout=0.2,      )
					snake_case_									=				torch.load(__UpperCAmelCase,      lambda __UpperCAmelCase,      __UpperCAmelCase	: storage	)
					snake_case_									=				AbsSummarizer(__UpperCAmelCase,      torch.device('''cpu'''	),      __UpperCAmelCase	)
					original.eval()

					snake_case_									=				BertAbsSummarizer(__UpperCAmelCase,      torch.device('''cpu'''	)	)
					new_model.eval()

					# -------------------
					# Convert the weights
					# -------------------

					logging.info('''convert the model'''	)
					new_model.bert.load_state_dict(original.bert.state_dict()	)
					new_model.decoder.load_state_dict(original.decoder.state_dict()	)
					new_model.generator.load_state_dict(original.generator.state_dict()	)

					# ----------------------------------
					# Make sure the outpus are identical
					# ----------------------------------

					logging.info('''Make sure that the models\' outputs are identical'''	)
					snake_case_									=				BertTokenizer.from_pretrained('''bert-base-uncased'''	)

					# prepare the model inputs
					snake_case_									=				tokenizer.encode('''This is sample éàalj\'-.'''	)
					encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCAmelCase	))	)
					snake_case_									=				torch.tensor(__UpperCAmelCase	).unsqueeze(0	)
					snake_case_									=				tokenizer.encode('''This is sample 3 éàalj\'-.'''	)
					decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCAmelCase	))	)
					snake_case_									=				torch.tensor(__UpperCAmelCase	).unsqueeze(0	)

					# failsafe to make sure the weights reset does not affect the
					# loaded weights.
					assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight	)	) == 0

					# forward pass
					snake_case_									=				encoder_input_ids
					snake_case_									=				decoder_input_ids
					snake_case_									=				snake_case_									=				None
					snake_case_									=				None
					snake_case_									=				snake_case_									=				None
					snake_case_									=				snake_case_									=				None
					snake_case_									=				None

					# The original model does not apply the geneator layer immediatly but rather in
					# the beam search (where it combines softmax + linear layer). Since we already
					# apply the softmax in our generation process we only apply the linear layer here.
					# We make sure that the outputs of the full stack are identical
					snake_case_									=				original(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)[0]
					snake_case_									=				original.generator(__UpperCAmelCase	)

					snake_case_									=				new_model(
					    __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)[0]
					snake_case_									=				new_model.generator(__UpperCAmelCase	)

					snake_case_									=				torch.max(torch.abs(output_converted_model - output_original_model	)	).item()
					print('''Maximum absolute difference beween weights: {:.2f}'''.format(__UpperCAmelCase	)	)
					snake_case_									=				torch.max(torch.abs(output_converted_generator - output_original_generator	)	).item()
					print('''Maximum absolute difference beween weights: {:.2f}'''.format(__UpperCAmelCase	)	)

					snake_case_									=				torch.allclose(__UpperCAmelCase,      __UpperCAmelCase,      atol=1e-3	)
					if are_identical:
										logging.info('''all weights are equal up to 1e-3'''	)
					else:
										raise ValueError('''the weights are different. The new model is likely different from the original one.'''	)

					# The model has been saved with torch.save(model) and this is bound to the exact
					# directory structure. We save the state_dict instead.
					logging.info('''saving the model\'s state dictionary'''	)
					torch.save(
					    new_model.state_dict(),      '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'''	)


if __name__ == "__main__":
							a  :				Union[str, Any]									=  argparse.ArgumentParser()
							parser.add_argument(
							    '--bertabs_checkpoint_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path the official PyTorch dump.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to the output PyTorch model.',
							)
							a  :				Dict									=  parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)







'''simple docstring'''

# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a  :				int									=  abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					config.addinivalue_line(
					    '''markers''',      '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'''	)
					config.addinivalue_line(
					    '''markers''',      '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'''	)
					config.addinivalue_line('''markers''',      '''is_pipeline_test: mark test to run only when pipelines are tested'''	)
					config.addinivalue_line('''markers''',      '''is_staging_test: mark test to run only in the staging environment'''	)
					config.addinivalue_line('''markers''',      '''accelerate_tests: mark test that require accelerate'''	)
					config.addinivalue_line('''markers''',      '''tool_tests: mark the tool tests that are run on their specific schedule'''	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					from transformers.testing_utils import pytest_terminal_summary_main

					snake_case_									=				terminalreporter.config.getoption('''--make-reports'''	)
					if make_reports:
										pytest_terminal_summary_main(__UpperCAmelCase,      id=__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					if exitstatus == 5:
										snake_case_									=				0


# Doctest custom flag to ignore output.
a  :				Union[str, Any]									=  doctest.register_optionflag('IGNORE_RESULT')

a  :				Optional[int]									=  doctest.OutputChecker






class 						a				(   _lowerCamelCase     ):


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					int       ,				lowercase_    :					Tuple       ,				lowercase_    :					Optional[int]					):
									if IGNORE_RESULT & optionflags:
														return True
									return OutputChecker.check_output(self       ,				lowercase_       ,				lowercase_       ,				lowercase_					)







a  :				List[Any]									=  CustomOutputChecker
a  :				Optional[int]									=  HfDoctestModule

a  :				Tuple									=  HfDocTestParser

