




from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 		_A   :






  def __init__(    self						:      int ,		_A						:      Collection[float] | None = None )						->       None:






       """simple docstring"""







       if components is None:
            lowercase   :  str        =					[]
       lowercase   :  Tuple        =					list(_A )






  def __len__(    self						:      Optional[int] )						->       int:






       """simple docstring"""







       return len(self.__components )






  def __str__(    self						:      str )						->       str:






       """simple docstring"""







       return "(" + ",".join(map(_A ,		self.__components ) ) + ")"






  def __add__(    self						:      Union[str, Any] ,		_A						:      Vector )						->       Vector:






       """simple docstring"""







       lowercase   :  Any        =					len(self )
       if size == len(_A ):
            lowercase   :  int        =					[self.__components[i] + other.component(_A ) for i in range(_A )]
            return Vector(_A )
       else:
            raise Exception('''must have the same size''' )






  def __sub__(    self						:      List[str] ,		_A						:      Vector )						->       Vector:






       """simple docstring"""







       lowercase   :  Optional[Any]        =					len(self )
       if size == len(_A ):
            lowercase   :  str        =					[self.__components[i] - other.component(_A ) for i in range(_A )]
            return Vector(_A )
       else:  # error case
            raise Exception('''must have the same size''' )






  @overload
  def __mul__(    self						:      List[str] ,		_A						:      float )						->       Vector:






       """simple docstring"""







       ...






  @overload
  def __mul__(    self						:      List[Any] ,		_A						:      Vector )						->       float:






       """simple docstring"""







       ...






  def __mul__(    self						:      Optional[int] ,		_A						:      float | Vector )						->       float | Vector:






       """simple docstring"""







       if isinstance(_A ,		(float, int) ):
            lowercase   :  Union[str, Any]        =					[c * other for c in self.__components]
            return Vector(_A )
       elif isinstance(_A ,		_A ) and len(self ) == len(_A ):
            lowercase   :  List[Any]        =					len(self )
            lowercase   :  str        =					[self.__components[i] * other.component(_A ) for i in range(_A )]
            return sum(_A )
       else:  # error case
            raise Exception('''invalid operand!''' )






  def    __a     (    self						:      Tuple )						->       Vector:






       """simple docstring"""







       return Vector(self.__components )






  def    __a     (    self						:      Union[str, Any] ,		_A						:      int )						->       float:






       """simple docstring"""







       if isinstance(_A ,		_A ) and -len(self.__components ) <= i < len(self.__components ):
            return self.__components[i]
       else:
            raise Exception('''index out of range''' )






  def    __a     (    self						:      str ,		_A						:      int ,		_A						:      float )						->       None:






       """simple docstring"""







       assert -len(self.__components ) <= pos < len(self.__components )
       lowercase   :  Union[str, Any]        =					value






  def    __a     (    self						:      str )						->       float:






       """simple docstring"""







       if len(self.__components ) == 0:
            raise Exception('''Vector is empty''' )
       lowercase   :  Optional[int]        =					[c**2 for c in self.__components]
       return math.sqrt(sum(_A ) )






  def    __a     (    self						:      int ,		_A						:      Vector ,		_A						:      bool = False )						->       float:






       """simple docstring"""







       lowercase   :  Union[str, Any]        =					self * other
       lowercase   :  Tuple        =					self.euclidean_length() * other.euclidean_length()
       if deg:
            return math.degrees(math.acos(num / den ) )
       else:
            return math.acos(num / den )






def    snake_case(							__magic_name__       )					->				str:





     '''simple docstring'''


     assert isinstance(_a					,						_a       )
     return Vector([0] * dimension       )






def    snake_case(							__magic_name__					,						__magic_name__       )					->				Optional[int]:





     '''simple docstring'''


     assert isinstance(_a					,						_a       ) and (isinstance(_a					,						_a       ))
     lowercase   :  Union[str, Any]        =					[0] * dimension
     lowercase   :  Any        =					1
     return Vector(_a       )






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__       )					->				int:





     '''simple docstring'''


     assert (
         isinstance(_a					,						_a       )
         and isinstance(_a					,						_a       )
         and (isinstance(_a					,						(int, float)       ))
     )
     return x * scalar + y






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__       )					->				Dict:





     '''simple docstring'''


     random.seed(_a       )
     lowercase   :  Union[str, Any]        =					[random.randint(_a					,						_a       ) for _ in range(_a       )]
     return Vector(_a       )
class 		_A   :






  def __init__(    self						:      int ,		_A						:      list[list[float]] ,		_A						:      int ,		_A						:      int )						->       None:






       """simple docstring"""







       lowercase   :  List[str]        =					matrix
       lowercase   :  Optional[int]        =					w
       lowercase   :  Optional[Any]        =					h






  def __str__(    self						:      Any )						->       str:






       """simple docstring"""







       lowercase   :  Dict        =					""
       for i in range(self.__height ):
            ans += "|"
            for j in range(self.__width ):
                 if j < self.__width - 1:
                      ans += str(self.__matrix[i][j] ) + ","
                 else:
                      ans += str(self.__matrix[i][j] ) + "|\n"
       return ans






  def __add__(    self						:      List[Any] ,		_A						:      Matrix )						->       Matrix:






       """simple docstring"""







       if self.__width == other.width() and self.__height == other.height():
            lowercase   :  Dict        =					[]
            for i in range(self.__height ):
                 lowercase   :  Any        =					[
                     self.__matrix[i][j] + other.component(_A ,		_A )
                     for j in range(self.__width )
                 ]
                 matrix.append(_A )
            return Matrix(_A ,		self.__width ,		self.__height )
       else:
            raise Exception('''matrix must have the same dimension!''' )






  def __sub__(    self						:      Optional[Any] ,		_A						:      Matrix )						->       Matrix:






       """simple docstring"""







       if self.__width == other.width() and self.__height == other.height():
            lowercase   :  Union[str, Any]        =					[]
            for i in range(self.__height ):
                 lowercase   :  Union[str, Any]        =					[
                     self.__matrix[i][j] - other.component(_A ,		_A )
                     for j in range(self.__width )
                 ]
                 matrix.append(_A )
            return Matrix(_A ,		self.__width ,		self.__height )
       else:
            raise Exception('''matrices must have the same dimension!''' )






  @overload
  def __mul__(    self						:      List[str] ,		_A						:      float )						->       Matrix:






       """simple docstring"""







       ...






  @overload
  def __mul__(    self						:      Any ,		_A						:      Vector )						->       Vector:






       """simple docstring"""







       ...






  def __mul__(    self						:      str ,		_A						:      float | Vector )						->       Vector | Matrix:






       """simple docstring"""







       if isinstance(_A ,		_A ):  # matrix-vector
            if len(_A ) == self.__width:
                 lowercase   :  Union[str, Any]        =					zero_vector(self.__height )
                 for i in range(self.__height ):
                      lowercase   :  Optional[int]        =					[
                          self.__matrix[i][j] * other.component(_A )
                          for j in range(self.__width )
                      ]
                      ans.change_component(_A ,		sum(_A ) )
                 return ans
            else:
                 raise Exception(
                     '''vector must have the same size as the '''
                     '''number of columns of the matrix!''' )
       elif isinstance(_A ,		(int, float) ):  # matrix-scalar
            lowercase   :  str        =					[
                [self.__matrix[i][j] * other for j in range(self.__width )]
                for i in range(self.__height )
            ]
            return Matrix(_A ,		self.__width ,		self.__height )
       return None






  def    __a     (    self						:      Optional[int] )						->       int:






       """simple docstring"""







       return self.__height






  def    __a     (    self						:      Tuple )						->       int:






       """simple docstring"""







       return self.__width






  def    __a     (    self						:      Any ,		_A						:      int ,		_A						:      int )						->       float:






       """simple docstring"""







       if 0 <= x < self.__height and 0 <= y < self.__width:
            return self.__matrix[x][y]
       else:
            raise Exception('''change_component: indices out of bounds''' )






  def    __a     (    self						:      Tuple ,		_A						:      int ,		_A						:      int ,		_A						:      float )						->       None:






       """simple docstring"""







       if 0 <= x < self.__height and 0 <= y < self.__width:
            lowercase   :  str        =					value
       else:
            raise Exception('''change_component: indices out of bounds''' )






  def    __a     (    self						:      int ,		_A						:      int ,		_A						:      int )						->       float:






       """simple docstring"""







       if self.__height != self.__width:
            raise Exception('''Matrix is not square''' )
       lowercase   :  List[str]        =					self.__matrix[:x] + self.__matrix[x + 1 :]
       for i in range(len(_A ) ):
            lowercase   :  Optional[int]        =					minor[i][:y] + minor[i][y + 1 :]
       return Matrix(_A ,		self.__width - 1 ,		self.__height - 1 ).determinant()






  def    __a     (    self						:      int ,		_A						:      int ,		_A						:      int )						->       float:






       """simple docstring"""







       if self.__height != self.__width:
            raise Exception('''Matrix is not square''' )
       if 0 <= x < self.__height and 0 <= y < self.__width:
            return (-1) ** (x + y) * self.minor(_A ,		_A )
       else:
            raise Exception('''Indices out of bounds''' )






  def    __a     (    self						:      Dict )						->       float:






       """simple docstring"""







       if self.__height != self.__width:
            raise Exception('''Matrix is not square''' )
       if self.__height < 1:
            raise Exception('''Matrix has no element''' )
       elif self.__height == 1:
            return self.__matrix[0][0]
       elif self.__height == 2:
            return (
                self.__matrix[0][0] * self.__matrix[1][1]
                - self.__matrix[0][1] * self.__matrix[1][0]
            )
       else:
            lowercase   :  List[Any]        =					[
                self.__matrix[0][y] * self.cofactor(0 ,		_A ) for y in range(self.__width )
            ]
            return sum(_A )






def    snake_case(							__magic_name__       )					->				str:





     '''simple docstring'''


     lowercase   :  list[list[float]]        =					[[0] * n for _ in range(_a       )]
     return Matrix(_a					,						_a					,						_a       )






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__       )					->				List[str]:





     '''simple docstring'''


     random.seed(_a       )
     lowercase   :  list[list[float]]        =					[
         [random.randint(_a					,						_a       ) for _ in range(_a       )] for _ in range(_a       )
     ]
     return Matrix(_a					,						_a					,						_a       )


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_      =     logging.getLogger(__name__)


a_      =     'Hello world! cécé herlolip'


a_      =     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__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							BertAbsConfig(
	    temp_dir="."				,      finetune_bert=_a				,      large=_a				,      share_emb=_a				,      use_bert_emb=_a				,      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=2048				,      dec_dropout=0.2				,      )
	SCREAMING_SNAKE_CASE :   Dict					  =							torch.load(_a				,      lambda _a				,      _a: storage)
	SCREAMING_SNAKE_CASE :   str					  =							AbsSummarizer(_a				,      torch.device("cpu")				,      _a)
	original.eval()

	SCREAMING_SNAKE_CASE :   List[str]					  =							BertAbsSummarizer(_a				,      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 :   List[str]					  =							BertTokenizer.from_pretrained("bert-base-uncased")

	# prepare the model inputs
	SCREAMING_SNAKE_CASE :   List[str]					  =							tokenizer.encode("This is sample éàalj'-.")
	encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).unsqueeze(0)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							tokenizer.encode("This is sample 3 éàalj'-.")
	decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).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 :   List[Any]					  =							encoder_input_ids
	SCREAMING_SNAKE_CASE :   List[Any]					  =							decoder_input_ids
	SCREAMING_SNAKE_CASE :   Dict					  =							None
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							None
	SCREAMING_SNAKE_CASE :   List[Any]					  =							None
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							None
	SCREAMING_SNAKE_CASE :   Dict					  =							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 :   Optional[int]					  =							original(_a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Dict					  =							original.generator(_a)

	SCREAMING_SNAKE_CASE :   Any					  =							new_model(
	    _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Tuple					  =							new_model.generator(_a)

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

	SCREAMING_SNAKE_CASE :   int					  =							torch.allclose(_a				,      _a				,      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_      =     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_      =     parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)