





"""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__   	=	logging.getLogger(__name__)


lowerCAmelCase__   	=	'''Hello world! cécé herlolip'''


lowerCAmelCase__   	=	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 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		BertAbsConfig(
		    temp_dir="."     ,		finetune_bert=__lowerCamelCase     ,		large=__lowerCamelCase     ,		share_emb=__lowerCamelCase     ,		use_bert_emb=__lowerCamelCase     ,		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=2_048     ,		dec_dropout=0.2     ,		)
		UpperCamelCase      =		torch.load(__lowerCamelCase     ,		lambda _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE			: storage			)
		UpperCamelCase      =		AbsSummarizer(__lowerCamelCase     ,		torch.device("cpu"			)     ,		__lowerCamelCase			)
		original.eval()

		UpperCamelCase      =		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"			)
		UpperCamelCase      =		BertTokenizer.from_pretrained("bert-base-uncased"			)

		# prepare the model inputs
		UpperCamelCase      =		tokenizer.encode("This is sample éàalj'-."			)
		encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase			))			)
		UpperCamelCase      =		torch.tensor(__lowerCamelCase			).unsqueeze(0			)
		UpperCamelCase      =		tokenizer.encode("This is sample 3 éàalj'-."			)
		decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase			))			)
		UpperCamelCase      =		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
		UpperCamelCase      =		encoder_input_ids
		UpperCamelCase      =		decoder_input_ids
		UpperCamelCase      =		None
		UpperCamelCase      =		None
		UpperCamelCase      =		None
		UpperCamelCase      =		None
		UpperCamelCase      =		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
		UpperCamelCase      =		original(__lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase			)[0]
		UpperCamelCase      =		original.generator(__lowerCamelCase			)

		UpperCamelCase      =		new_model(
		    __lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase     ,		__lowerCamelCase			)[0]
		UpperCamelCase      =		new_model.generator(__lowerCamelCase			)

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

		UpperCamelCase      =		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__   	=	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__   	=	parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)








"""simple docstring"""





def 	__a     (		__lowerCamelCase = 3,		__lowerCamelCase = 7,		__lowerCamelCase = 100_0000  ):
   UpperCAmelCase_					:					Dict			     =    0
   UpperCAmelCase_					:					List[Any]			     =    1

   for current_denominator in range(1,		limit + 1  ):
      UpperCAmelCase_					:					Dict			     =    current_denominator * numerator // denominator
      if current_denominator % denominator == 0:
         current_numerator -= 1
      if current_numerator * max_denominator > current_denominator * max_numerator:
         UpperCAmelCase_					:					List[Any]			     =    current_numerator
         UpperCAmelCase_					:					Optional[int]			     =    current_denominator
   return max_numerator


if __name__ == "__main__":
    print(solution(numerator=3, denominator=7, limit=1_000_000))


