

"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class    lowerCamelCase						:

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Any       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      data
						SCREAMING_SNAKE_CASE__								=      None





class    lowerCamelCase						:

			def __init__(					self				:						str       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      None
						SCREAMING_SNAKE_CASE__								=      None

			def __iter__(					self				:						Union[str, Any]       )   ->    Iterator[Any]:
						SCREAMING_SNAKE_CASE__								=      self.head
						while self.head:
									yield node.data
									SCREAMING_SNAKE_CASE__								=      node.next
									if node == self.head:
												break

			def __len__(					self				:						Any       )   ->    int:
						return sum(1 for _ in self       )

			def __repr__(					self				:						Tuple       )   ->    List[Any]:
						return "->".join(str(__UpperCAmelCase       ) for item in iter(self       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Any       )   ->    None:
						self.insert_nth(len(self       )    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Any       )   ->    None:
						self.insert_nth(0    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Any       )   ->    None:
						if index < 0 or index > len(self       ):
									raise IndexError("""list index out of range."""       )
						SCREAMING_SNAKE_CASE__								=      Node(__UpperCAmelCase       )
						if self.head is None:
									SCREAMING_SNAKE_CASE__								=      new_node  # first node points itself
									SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      new_node
						elif index == 0:  # insert at head
									SCREAMING_SNAKE_CASE__								=      self.head
									SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      new_node
						else:
									SCREAMING_SNAKE_CASE__								=      self.head
									for _ in range(index - 1       ):
												SCREAMING_SNAKE_CASE__								=      temp.next
									SCREAMING_SNAKE_CASE__								=      temp.next
									SCREAMING_SNAKE_CASE__								=      new_node
									if index == len(self       ) - 1:  # insert at tail
												SCREAMING_SNAKE_CASE__								=      new_node

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Union[str, Any]:
						return self.delete_nth(0       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Any:
						return self.delete_nth(len(self       ) - 1       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						int = 0       )   ->    Any:
						if not 0 <= index < len(self       ):
									raise IndexError("""list index out of range."""       )
						SCREAMING_SNAKE_CASE__								=      self.head
						if self.head == self.tail:  # just one node
									SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
						elif index == 0:  # delete head node
									SCREAMING_SNAKE_CASE__								=      self.tail.next.next
									SCREAMING_SNAKE_CASE__								=      self.head.next
						else:
									SCREAMING_SNAKE_CASE__								=      self.head
									for _ in range(index - 1       ):
												SCREAMING_SNAKE_CASE__								=      temp.next
									SCREAMING_SNAKE_CASE__								=      temp.next
									SCREAMING_SNAKE_CASE__								=      temp.next.next
									if index == len(self       ) - 1:  # delete at tail
												SCREAMING_SNAKE_CASE__								=      temp
						return delete_node.data





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    bool:
						return len(self       ) == 0

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      CircularLinkedList()
			assert len(snake_case__    ) == 0
			assert circular_linked_list.is_empty() is True
			assert str(snake_case__    ) == ""

			try:
						circular_linked_list.delete_front()
						raise AssertionError  # This should not happen
			except IndexError:
						assert True  # This should happen

			try:
						circular_linked_list.delete_tail()
						raise AssertionError  # This should not happen
			except IndexError:
						assert True  # This should happen

			try:
						circular_linked_list.delete_nth(-1    )
						raise AssertionError
			except IndexError:
						assert True

			try:
						circular_linked_list.delete_nth(0    )
						raise AssertionError
			except IndexError:
						assert True

			assert circular_linked_list.is_empty() is True
			for i in range(5    ):
						assert len(snake_case__    ) == i
						circular_linked_list.insert_nth(snake_case__		,				i + 1    )
			assert str(snake_case__    ) == "->".join(str(snake_case__    ) for i in range(1		,				6    )    )

			circular_linked_list.insert_tail(6    )
			assert str(snake_case__    ) == "->".join(str(snake_case__    ) for i in range(1		,				7    )    )
			circular_linked_list.insert_head(0    )
			assert str(snake_case__    ) == "->".join(str(snake_case__    ) for i in range(0		,				7    )    )

			assert circular_linked_list.delete_front() == 0
			assert circular_linked_list.delete_tail() == 6
			assert str(snake_case__    ) == "->".join(str(snake_case__    ) for i in range(1		,				6    )    )
			assert circular_linked_list.delete_nth(2    ) == 3

			circular_linked_list.insert_nth(2		,				3    )
			assert str(snake_case__    ) == "->".join(str(snake_case__    ) for i in range(1		,				6    )    )

			assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""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_							:			Tuple											= logging.getLogger(__name__)


A_							:			Tuple											= "Hello world! cécé herlolip"


A_							:			Dict											= 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       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      BertAbsConfig(
			    temp_dir="""."""		,				finetune_bert=snake_case__		,				large=snake_case__		,				share_emb=snake_case__		,				use_bert_emb=snake_case__		,				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		,				)
			SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__		,				lambda snake_case__		,				snake_case__    : storage    )
			SCREAMING_SNAKE_CASE__								=      AbsSummarizer(snake_case__		,				torch.device("""cpu"""    )		,				snake_case__    )
			original.eval()

			SCREAMING_SNAKE_CASE__								=      BertAbsSummarizer(snake_case__		,				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"""    )
			SCREAMING_SNAKE_CASE__								=      BertTokenizer.from_pretrained("""bert-base-uncased"""    )

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

			SCREAMING_SNAKE_CASE__								=      new_model(
			    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      new_model.generator(snake_case__    )

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

			SCREAMING_SNAKE_CASE__								=      torch.allclose(snake_case__		,				snake_case__		,				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_							:			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.",
						)
						A_							:			Tuple											= parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


