





'''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     :    Union[str, Any]      						=					logging.getLogger(__name__)


a     :    str      						=					"""Hello world! cécé herlolip"""


a     :    Any      						=					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						(      _lowercase			,       _lowercase	)   ->    int:
							UpperCAmelCase  :	Tuple							  =	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			,       )
							UpperCAmelCase  :	int							  =	torch.load(lowerCamelCase_			,       lambda _lowercase			,       _lowercase	: storage	)
							UpperCAmelCase  :	List[str]							  =	AbsSummarizer(lowerCamelCase_			,       torch.device("""cpu"""	)			,       lowerCamelCase_	)
							original.eval()

							UpperCAmelCase  :	Optional[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"""	)
							UpperCAmelCase  :	int							  =	BertTokenizer.from_pretrained("""bert-base-uncased"""	)

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

							UpperCAmelCase  :	List[Any]							  =	new_model(
							    lowerCamelCase_			,       lowerCamelCase_			,       lowerCamelCase_			,       lowerCamelCase_			,       lowerCamelCase_	)[0]
							UpperCAmelCase  :	str							  =	new_model.generator(lowerCamelCase_	)

							UpperCAmelCase  :	int							  =	torch.max(torch.abs(output_converted_model - output_original_model	)	).item()
							print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_	)	)
							UpperCAmelCase  :	Optional[int]							  =	torch.max(torch.abs(output_converted_generator - output_original_generator	)	).item()
							print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_	)	)

							UpperCAmelCase  :	Any							  =	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__":
							a     :    Tuple      						=					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     :    Any      						=					parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)






'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str | Literal[False]:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	str							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count += 1
																					UpperCAmelCase  :	Optional[Any]							  =	"""_"""
							if count > 1:
														return False
							else:
														return "".join(_lowercase	)



def    __lowerCamelCase						(      _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	List[str]							  =	[]
							while True:
														UpperCAmelCase  :	Optional[int]							  =	["""$"""] * len(_lowercase	)
														UpperCAmelCase  :	int							  =	[]
														for i in range(len(_lowercase	)	):
																					for j in range(i + 1			,       len(_lowercase	)	):
																												UpperCAmelCase  :	str							  =	compare_string(binary[i]			,       binary[j]	)
																												if k is False:
																																			UpperCAmelCase  :	Union[str, Any]							  =	"""*"""
																																			UpperCAmelCase  :	Optional[Any]							  =	"""*"""
																																			temp.append("""X"""	)
														for i in range(len(_lowercase	)	):
																					if checka[i] == "$":
																												pi.append(binary[i]	)
														if len(_lowercase	) == 0:
																					return pi
														UpperCAmelCase  :	List[Any]							  =	list(set(_lowercase	)	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Dict							  =	[]
							for minterm in minterms:
														UpperCAmelCase  :	List[str]							  =	""""""
														for _ in range(_lowercase	):
																					UpperCAmelCase  :	Dict							  =	str(minterm % 2	) + string
																					minterm //= 2
														temp.append(_lowercase	)
							return temp



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    bool:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count_n += 1
							return count_n == count



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Tuple							  =	[]
							UpperCAmelCase  :	Optional[int]							  =	[0] * len(_lowercase	)
							for i in range(len(chart[0]	)	):
														UpperCAmelCase  :	Any							  =	0
														UpperCAmelCase  :	Optional[Any]							  =	-1
														for j in range(len(_lowercase	)	):
																					if chart[j][i] == 1:
																												count += 1
																												UpperCAmelCase  :	str							  =	j
														if count == 1:
																					UpperCAmelCase  :	Optional[int]							  =	1
							for i in range(len(_lowercase	)	):
														if select[i] == 1:
																					for j in range(len(chart[0]	)	):
																												if chart[i][j] == 1:
																																			for k in range(len(_lowercase	)	):
																																										UpperCAmelCase  :	List[str]							  =	0
																					temp.append(prime_implicants[i]	)
							while True:
														UpperCAmelCase  :	int							  =	0
														UpperCAmelCase  :	Tuple							  =	-1
														UpperCAmelCase  :	Union[str, Any]							  =	0
														for i in range(len(_lowercase	)	):
																					UpperCAmelCase  :	Optional[Any]							  =	chart[i].count(1	)
																					if count_n > max_n:
																												UpperCAmelCase  :	Union[str, Any]							  =	count_n
																												UpperCAmelCase  :	Optional[Any]							  =	i

														if max_n == 0:
																					return temp

														temp.append(prime_implicants[rem]	)

														for i in range(len(chart[0]	)	):
																					if chart[rem][i] == 1:
																												for j in range(len(_lowercase	)	):
																																			UpperCAmelCase  :	Optional[Any]							  =	0



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[list[int]]:
							UpperCAmelCase  :	Optional[int]							  =	[[0 for x in range(len(_lowercase	)	)] for x in range(len(_lowercase	)	)]
							for i in range(len(_lowercase	)	):
														UpperCAmelCase  :	Tuple							  =	prime_implicants[i].count("""_"""	)
														for j in range(len(_lowercase	)	):
																					if is_for_table(prime_implicants[i]			,       binary[j]			,       _lowercase	):
																												UpperCAmelCase  :	List[Any]							  =	1

							return chart



def    __lowerCamelCase						(      )   ->    None:
							UpperCAmelCase  :	str							  =	int(input("""Enter the no. of variables\n"""	)	)
							UpperCAmelCase  :	List[Any]							  =	[
							    float(_lowercase	)
							    for x in input(
							        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""	).split()
							]
							UpperCAmelCase  :	str							  =	decimal_to_binary(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	check(_lowercase	)
							print("""Prime Implicants are:"""	)
							print(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	prime_implicant_chart(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	selection(_lowercase			,       _lowercase	)
							print("""Essential Prime Implicants are:"""	)
							print(_lowercase	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

