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


SCREAMING_SNAKE_CASE							:int							 =				'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE							: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 						UpperCAmelCase			(	a_		,   a_						)		->	Dict:







				"""simple docstring"""



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

				__A   			= 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"						)
				__A   			= BertTokenizer.from_pretrained("bert-base-uncased"						)

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

				__A   			= new_model(
				    a_		,   a_		,   a_		,   a_		,   a_						)[0]
				__A   			= new_model.generator(a_						)

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

				__A   			= 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__":
					SCREAMING_SNAKE_CASE							:int							 =				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.',
					)
					SCREAMING_SNAKE_CASE							:List[str]							 =				parser.parse_args()

					convert_bertabs_checkpoints(
					    args.bertabs_checkpoint_path,
					    args.pytorch_dump_folder_path,
					)


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							:Optional[int]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[int]							 =				{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE							:List[str]							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Optional[Any]							,A					: List[str]							,A					: List[Any]="<s>"							,A					: Tuple="</s>"							,A					: Any="</s>"							,A					: Optional[Any]="<s>"							,A					: Tuple="<unk>"							,A					: str="<pad>"							,A					: int="<mask>"							,A					: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]							,A					: Optional[Dict[str, Any]] = None							,**A					: Optional[Any]							,):
					# Mask token behave like a normal word, i.e. include the space before it
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else mask_token

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,mask_token=A							,additional_special_tokens=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )
					__A   			= vocab_file
					# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
					# sentencepiece vocabulary (this is the case for <s> and </s>
					__A   			= {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
					__A   			= len(self.fairseq_tokens_to_ids )
					__A   			= len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
					__A   			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def 				UpperCamelCase_  (       self					: int							,A					: List[int]							,A					: Optional[List[int]] = None ):

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A   			= [self.cls_token_id]
					__A   			= [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A )

					if token_ids_a is None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(A ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Tuple ):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Dict ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def __getstate__(       self					: Dict ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Union[str, Any]							,A					: Any ):
					__A   			= d

					# for backward compatibility
					if not hasattr(self							,"sp_model_kwargs" ):
									__A   			= {}

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )







	def 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file							,A )
					elif not os.path.isfile(self.vocab_file ):
									with open(A							,"wb" ) as fi:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)
