


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


_snake_case					:		Optional[int]      =    "Hello world! cécé herlolip"


_snake_case					:		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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      str										=       BertAbsConfig(
							    temp_dir="."							,     finetune_bert=__lowerCamelCase							,     large=__lowerCamelCase							,     share_emb=__lowerCamelCase							,     use_bert_emb=__lowerCamelCase							,     encoder="bert"							,     max_pos=5_1_2							,     enc_layers=6							,     enc_hidden_size=5_1_2							,     enc_heads=8							,     enc_ff_size=5_1_2							,     enc_dropout=0.2							,     dec_layers=6							,     dec_hidden_size=7_6_8							,     dec_heads=8							,     dec_ff_size=2_0_4_8							,     dec_dropout=0.2							,     )
							__snake_case		:      Optional[Any]										=       torch.load(__lowerCamelCase							,     lambda __lowerCamelCase							,     __lowerCamelCase     : storage     )
							__snake_case		:      Optional[Any]										=       AbsSummarizer(__lowerCamelCase							,     torch.device("cpu"     )							,     __lowerCamelCase     )
							original.eval()

							__snake_case		:      int										=       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"     )
							__snake_case		:      Union[str, Any]										=       BertTokenizer.from_pretrained("bert-base-uncased"     )

							# prepare the model inputs
							__snake_case		:      int										=       tokenizer.encode("This is sample éàalj'-."     )
							encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__lowerCamelCase     ))     )
							__snake_case		:      List[Any]										=       torch.tensor(__lowerCamelCase     ).unsqueeze(0     )
							__snake_case		:      Optional[Any]										=       tokenizer.encode("This is sample 3 éàalj'-."     )
							decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__lowerCamelCase     ))     )
							__snake_case		:      int										=       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
							__snake_case		:      Union[str, Any]										=       encoder_input_ids
							__snake_case		:      Optional[int]										=       decoder_input_ids
							__snake_case		:      Tuple										=       None
							__snake_case		:      Union[str, Any]										=       None
							__snake_case		:      str										=       None
							__snake_case		:      Optional[int]										=       None
							__snake_case		:      int										=       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		:      Tuple										=       original(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )[0]
							__snake_case		:      List[Any]										=       original.generator(__lowerCamelCase     )

							__snake_case		:      Union[str, Any]										=       new_model(
							    __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )[0]
							__snake_case		:      Any										=       new_model.generator(__lowerCamelCase     )

							__snake_case		:      Union[str, Any]										=       torch.max(torch.abs(output_converted_model - output_original_model     )     ).item()
							print("Maximum absolute difference beween weights: {:.2f}".format(__lowerCamelCase     )     )
							__snake_case		:      List[Any]										=       torch.max(torch.abs(output_converted_generator - output_original_generator     )     ).item()
							print("Maximum absolute difference beween weights: {:.2f}".format(__lowerCamelCase     )     )

							__snake_case		:      Dict										=       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__":
			_snake_case					:		Dict      =    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.",
			)
			_snake_case					:		List[str]      =    parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)





def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							if index == r:
														for j in range(__lowerCamelCase     ):
																					print(data[j]							,     end=" "     )
														print(" "     )
														return
							#  When no more elements are there to put in data[]
							if i >= n:
														return
							# current is included, put next at next location
							__snake_case		:      Union[str, Any]										=       arr[i]
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     index + 1							,     __lowerCamelCase							,     i + 1     )
							# current is excluded, replace it with
							# next (Note that i+1 is passed, but
							# index is not changed)
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     i + 1     )
							# The main function that prints all combinations
							# of size r in arr[] of size n. This function
							# mainly uses combinationUtil()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# A temporary array to store all combination one by one
							__snake_case		:      Union[str, Any]										=       [0] * r
							# Print all combination using temporary array 'data[]'
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     0							,     __lowerCamelCase							,     0     )


if __name__ == "__main__":
			# Driver code to check the function above
			_snake_case					:		List[str]      =    [10, 20, 30, 40, 50]
			print_combination(arr, len(arr), 3)
			# This code is contributed by Ambuj sahu

