



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__					:     str    		=   """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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			BertAbsConfig(
	    temp_dir="."  ,	finetune_bert=_SCREAMING_SNAKE_CASE  ,	large=_SCREAMING_SNAKE_CASE  ,	share_emb=_SCREAMING_SNAKE_CASE  ,	use_bert_emb=_SCREAMING_SNAKE_CASE  ,	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  ,	)
	__lowercase		       =			torch.load(_SCREAMING_SNAKE_CASE  ,	lambda _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   : storage   )
	__lowercase		       =			AbsSummarizer(_SCREAMING_SNAKE_CASE  ,	torch.device("cpu"   )  ,	_SCREAMING_SNAKE_CASE   )
	original.eval()

	__lowercase		       =			BertAbsSummarizer(_SCREAMING_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"   )
	__lowercase		       =			BertTokenizer.from_pretrained("bert-base-uncased"   )

	# prepare the model inputs
	__lowercase		       =			tokenizer.encode("This is sample éàalj'-."   )
	encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_SCREAMING_SNAKE_CASE   ))   )
	__lowercase		       =			torch.tensor(_SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
	__lowercase		       =			tokenizer.encode("This is sample 3 éàalj'-."   )
	decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_SCREAMING_SNAKE_CASE   ))   )
	__lowercase		       =			torch.tensor(_SCREAMING_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
	__lowercase		       =			encoder_input_ids
	__lowercase		       =			decoder_input_ids
	__lowercase		       =			__lowercase		       =			None
	__lowercase		       =			None
	__lowercase		       =			__lowercase		       =			None
	__lowercase		       =			__lowercase		       =			None
	__lowercase		       =			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
	__lowercase		       =			original(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )[0]
	__lowercase		       =			original.generator(_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			new_model(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )[0]
	__lowercase		       =			new_model.generator(_SCREAMING_SNAKE_CASE   )

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

	__lowercase		       =			torch.allclose(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_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__":
	snake_case__					:     str    		=   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__					:     Tuple    		=   parser.parse_args()

	convert_bertabs_checkpoints(
	    args.bertabs_checkpoint_path,
	    args.pytorch_dump_folder_path,
	)








from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

snake_case__					:     Union[str, Any]    		=   TypeVar("""T""")
snake_case__					:     Optional[int]    		=   TypeVar("""U""")

class   _A   ( Generic[T, U]	):


						'''simple docstring'''





						def __init__(	self	:							Optional[int]							,      lowerCamelCase	:							T | None							,      lowerCamelCase	:							U | None	):






							'''simple docstring'''



							__lowercase		       =			key
							__lowercase		       =			val
							__lowercase		       =			None
							__lowercase		       =			None



						def __repr__(	self	:							Any	):






							'''simple docstring'''



							return (
							    f"""Node: key: {self.key}, val: {self.val}, """
							    f"""has next: {bool(self.next	)}, has prev: {bool(self.prev	)}"""
							)

class   _A   ( Generic[T, U]	):


						'''simple docstring'''





						def __init__(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			DoubleLinkedListNode(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			DoubleLinkedListNode(lowerCamelCase							,      lowerCamelCase	)
							__lowercase       ,    __lowercase		       =			self.rear, self.head



						def __repr__(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			["DoubleLinkedList"]
							__lowercase		       =			self.head
							while node.next is not None:
								rep.append(str(lowerCamelCase	)	)
								__lowercase		       =			node.next
							rep.append(str(self.rear	)	)
							return ",\n    ".join(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							DoubleLinkedListNode[T, U]	):






							'''simple docstring'''



							__lowercase		       =			self.rear.prev

							# All nodes other than self.head are guaranteed to have non-None previous
							assert previous is not None

							__lowercase		       =			node
							__lowercase		       =			previous
							__lowercase		       =			node
							__lowercase		       =			self.rear



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							DoubleLinkedListNode[T, U]	):






							'''simple docstring'''



							if node.prev is None or node.next is None:
								return None

							__lowercase		       =			node.next
							__lowercase		       =			node.prev
							__lowercase		       =			None
							__lowercase		       =			None
							return node

class   _A   ( Generic[T, U]	):


						'''simple docstring'''





						_snake_case			:     dict[Callable[[T], U], LRUCache[T, U]]      							=      {}



						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			DoubleLinkedList()
							__lowercase		       =			capacity
							__lowercase		       =			0
							__lowercase		       =			0
							__lowercase		       =			0
							__lowercase		       =			{}



						def __repr__(	self	:							Optional[Any]	):






							'''simple docstring'''



							return (
							    f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
							    f"""capacity={self.capacity}, current size={self.num_keys})"""
							)



						def __contains__(	self	:							Dict							,      lowerCamelCase	:							T	):






							'''simple docstring'''



							return key in self.cache



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							T	):






							'''simple docstring'''



							if key in self.cache:
								self.hits += 1
								__lowercase		       =			self.cache[key]
								__lowercase		       =			self.list.remove(self.cache[key]	)
								assert node == value_node

								# node is guaranteed not None because it is in self.cache
								assert node is not None
								self.list.add(lowerCamelCase	)
								return node.val
							self.miss += 1
							return None



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							T							,      lowerCamelCase	:							U	):






							'''simple docstring'''



							if key not in self.cache:
								if self.num_keys >= self.capacity:
									# delete first node (oldest) when over capacity
									__lowercase		       =			self.list.head.next

									# guaranteed to have a non-None first node when num_keys > 0
									# explain to type checker via assertions
									assert first_node is not None
									assert first_node.key is not None
									assert (
									    self.list.remove(lowerCamelCase	) is not None
									)  # node guaranteed to be in list assert node.key is not None

									del self.cache[first_node.key]
									self.num_keys -= 1
								__lowercase		       =			DoubleLinkedListNode(lowerCamelCase							,      lowerCamelCase	)
								self.list.add(self.cache[key]	)
								self.num_keys += 1

							else:
								# bump node to the end of the list, update value
								__lowercase		       =			self.list.remove(self.cache[key]	)
								assert node is not None  # node guaranteed to be in list
								__lowercase		       =			value
								self.list.add(lowerCamelCase	)



						@classmethod
						def     _snake_case	(	cls	:							Union[str, Any]							,      lowerCamelCase	:							int = 128	):






							'''simple docstring'''



							def cache_decorator_inner(lowerCamelCase	:							Callable[[T], U]	) -> Callable[..., U]:
								def cache_decorator_wrapper(*lowerCamelCase	:							T	) -> U:
									if func not in cls.decorator_function_to_instance_map:
										__lowercase		       =			LRUCache(lowerCamelCase	)

									__lowercase		       =			cls.decorator_function_to_instance_map[func].get(args[0]	)
									if result is None:
										__lowercase		       =			func(*lowerCamelCase	)
										cls.decorator_function_to_instance_map[func].put(args[0]							,      lowerCamelCase	)
									return result

								def cache_info() -> LRUCache[T, U]:
									return cls.decorator_function_to_instance_map[func]

								setattr(lowerCamelCase							,      "cache_info"							,      lowerCamelCase	)  # noqa: B010

								return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()



