



'''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)
lowerCAmelCase	:		Optional[Any]				=   logging.getLogger(__name__)


lowerCAmelCase	:		List[Any]				=   '''Hello world! cécé herlolip'''


lowerCAmelCase	:		Tuple				=   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   __lowerCAmelCase					(							lowerCamelCase   :						Dict				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  BertAbsConfig(
	    temp_dir="."				,		finetune_bert=lowerCamelCase				,		large=lowerCamelCase				,		share_emb=lowerCamelCase				,		use_bert_emb=lowerCamelCase				,		encoder="bert"				,		max_pos=5_12				,		enc_layers=6				,		enc_hidden_size=5_12				,		enc_heads=8				,		enc_ff_size=5_12				,		enc_dropout=0.2				,		dec_layers=6				,		dec_hidden_size=7_68				,		dec_heads=8				,		dec_ff_size=20_48				,		dec_dropout=0.2				,		)
	__lowerCAmelCase												=  torch.load(lowerCamelCase				,		lambda lowerCamelCase				,		lowerCamelCase					: storage					)
	__lowerCAmelCase												=  AbsSummarizer(lowerCamelCase				,		torch.device("cpu"					)				,		lowerCamelCase					)
	original.eval()

	__lowerCAmelCase												=  BertAbsSummarizer(lowerCamelCase				,		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"					)
	__lowerCAmelCase												=  BertTokenizer.from_pretrained("bert-base-uncased"					)

	# prepare the model inputs
	__lowerCAmelCase												=  tokenizer.encode("This is sample éàalj'-."					)
	encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase					))					)
	__lowerCAmelCase												=  torch.tensor(lowerCamelCase					).unsqueeze(0					)
	__lowerCAmelCase												=  tokenizer.encode("This is sample 3 éàalj'-."					)
	decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase					))					)
	__lowerCAmelCase												=  torch.tensor(lowerCamelCase					).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
	__lowerCAmelCase												=  encoder_input_ids
	__lowerCAmelCase												=  decoder_input_ids
	__lowerCAmelCase												=  __lowerCAmelCase												=  None
	__lowerCAmelCase												=  None
	__lowerCAmelCase												=  __lowerCAmelCase												=  None
	__lowerCAmelCase												=  __lowerCAmelCase												=  None
	__lowerCAmelCase												=  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
	__lowerCAmelCase												=  original(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)[0]
	__lowerCAmelCase												=  original.generator(lowerCamelCase					)

	__lowerCAmelCase												=  new_model(
	    lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)[0]
	__lowerCAmelCase												=  new_model.generator(lowerCamelCase					)

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

	__lowerCAmelCase												=  torch.allclose(lowerCamelCase				,		lowerCamelCase				,		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__":
	lowerCAmelCase	:		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.''',
	)
	lowerCAmelCase	:		Optional[int]				=   parser.parse_args()

	convert_bertabs_checkpoints(
	    args.bertabs_checkpoint_path,
	    args.pytorch_dump_folder_path,
	)




'''simple docstring'''



import os
import sys
import unittest


lowerCAmelCase	:		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: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')






class 					UpperCAmelCase__	(		unittest.TestCase ):



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {"BertModelTest": "BertModelTester"}

						__lowerCAmelCase												=  {
						    "BlipModelTest": "BlipModelTester",
						    "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
						    "BlipTextModelTest": "BlipTextModelTester",
						    "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
						    "BlipVQAModelTest": "BlipVQAModelTester",
						    "BlipVisionModelTest": "BlipVisionModelTester",
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTest"],
						    "BertForMultipleChoice": ["BertModelTest"],
						    "BertForNextSentencePrediction": ["BertModelTest"],
						    "BertForPreTraining": ["BertModelTest"],
						    "BertForQuestionAnswering": ["BertModelTest"],
						    "BertForSequenceClassification": ["BertModelTest"],
						    "BertForTokenClassification": ["BertModelTest"],
						    "BertLMHeadModel": ["BertModelTest"],
						    "BertModel": ["BertModelTest"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTest"],
						    "BlipModel": ["BlipModelTest"],
						    "BlipTextModel": ["BlipTextModelTest"],
						    "BlipVisionModel": ["BlipVisionModelTest"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTester"],
						    "BertForMultipleChoice": ["BertModelTester"],
						    "BertForNextSentencePrediction": ["BertModelTester"],
						    "BertForPreTraining": ["BertModelTester"],
						    "BertForQuestionAnswering": ["BertModelTester"],
						    "BertForSequenceClassification": ["BertModelTester"],
						    "BertForTokenClassification": ["BertModelTester"],
						    "BertLMHeadModel": ["BertModelTester"],
						    "BertModel": ["BertModelTester"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTester"],
						    "BlipModel": ["BlipModelTester"],
						    "BlipTextModel": ["BlipTextModelTester"],
						    "BlipVisionModel": ["BlipVisionModelTester"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )