
"""simple docstring"""




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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_mbart import MBartTokenizer
else:
						A_							:			Any											= None


A_							:			Union[str, Any]											= logging.get_logger(__name__)


A_							:			Dict											= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A_							:			int											= {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

A_							:			List[Any]											= {
    'facebook/mbart-large-en-ro': 1_024,
    'facebook/mbart-large-cc25': 1_024,
}

# fmt: off
A_							:			Dict											= ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Optional[int]							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		Tuple							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		str							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		int							=							['input_ids', 'attention_mask']
			lowerCamelCase__   :		Union[str, Any]							=							MBartTokenizer

			lowerCamelCase__   :		List[int]							=							[]
			lowerCamelCase__   :		List[int]							=							[]

			def __init__(					self				:						Any    ,				__UpperCAmelCase				:						str=None    ,				__UpperCAmelCase				:						Any=None    ,				__UpperCAmelCase				:						Union[str, Any]="<s>"    ,				__UpperCAmelCase				:						str="</s>"    ,				__UpperCAmelCase				:						int="</s>"    ,				__UpperCAmelCase				:						Any="<s>"    ,				__UpperCAmelCase				:						str="<unk>"    ,				__UpperCAmelCase				:						str="<pad>"    ,				__UpperCAmelCase				:						str="<mask>"    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						Tuple=None    ,				__UpperCAmelCase				:						List[str]=None    ,				**__UpperCAmelCase				:						str    ,				)   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      AddedToken(UpperCAmelCase__    ,				lstrip=UpperCAmelCase__    ,				rstrip=UpperCAmelCase__       ) if isinstance(UpperCAmelCase__    ,				UpperCAmelCase__       ) else mask_token

						super().__init__(
						    vocab_file=UpperCAmelCase__    ,				tokenizer_file=UpperCAmelCase__    ,				bos_token=UpperCAmelCase__    ,				eos_token=UpperCAmelCase__    ,				sep_token=UpperCAmelCase__    ,				cls_token=UpperCAmelCase__    ,				unk_token=UpperCAmelCase__    ,				pad_token=UpperCAmelCase__    ,				mask_token=UpperCAmelCase__    ,				src_lang=UpperCAmelCase__    ,				tgt_lang=UpperCAmelCase__    ,				additional_special_tokens=UpperCAmelCase__    ,				**UpperCAmelCase__    ,				)

						SCREAMING_SNAKE_CASE__								=      vocab_file
						SCREAMING_SNAKE_CASE__								=      False if not self.vocab_file else True

						SCREAMING_SNAKE_CASE__								=      FAIRSEQ_LANGUAGE_CODES.copy()

						if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens]       )

						self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}       )
						SCREAMING_SNAKE_CASE__								=      {
						    lang_code: self.convert_tokens_to_ids(UpperCAmelCase__       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						SCREAMING_SNAKE_CASE__								=      src_lang if src_lang is not None else '''en_XX'''
						SCREAMING_SNAKE_CASE__								=      self.convert_tokens_to_ids(self._src_lang       )
						SCREAMING_SNAKE_CASE__								=      tgt_lang
						self.set_src_lang_special_tokens(self._src_lang       )

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    str:
						return self._src_lang

			@src_lang.setter
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      new_src_lang
						self.set_src_lang_special_tokens(self._src_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[str]:
						if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [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]

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str]    ,				__UpperCAmelCase				:						Optional[str]    ,				**__UpperCAmelCase				:						List[Any]       )   ->    Dict:
						if src_lang is None or tgt_lang is None:
									raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
						SCREAMING_SNAKE_CASE__								=      src_lang
						SCREAMING_SNAKE_CASE__								=      self(UpperCAmelCase__    ,				add_special_tokens=UpperCAmelCase__    ,				return_tensors=UpperCAmelCase__    ,				**UpperCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      self.convert_tokens_to_ids(UpperCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      tgt_lang_id
						return inputs

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						str = "en_XX"    ,				__UpperCAmelCase				:						Optional[List[str]] = None    ,				__UpperCAmelCase				:						str = "ro_RO"    ,				**__UpperCAmelCase				:						Any    ,				)   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      src_lang
						SCREAMING_SNAKE_CASE__								=      tgt_lang
						return super().prepare_seqaseq_batch(UpperCAmelCase__    ,				UpperCAmelCase__    ,				**UpperCAmelCase__       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Union[str, Any]:
						return self.set_src_lang_special_tokens(self.src_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    List[Any]:
						return self.set_tgt_lang_special_tokens(self.tgt_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Tuple       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.convert_tokens_to_ids(UpperCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      [self.eos_token_id, self.cur_lang_code]

						SCREAMING_SNAKE_CASE__								=      self.convert_ids_to_tokens(self.prefix_tokens       )
						SCREAMING_SNAKE_CASE__								=      self.convert_ids_to_tokens(self.suffix_tokens       )

						SCREAMING_SNAKE_CASE__								=      processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,				pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,				self.prefix_tokens + self.suffix_tokens       )       )    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						str       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      self.convert_tokens_to_ids(UpperCAmelCase__       )
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      [self.eos_token_id, self.cur_lang_code]

						SCREAMING_SNAKE_CASE__								=      self.convert_ids_to_tokens(self.prefix_tokens       )
						SCREAMING_SNAKE_CASE__								=      self.convert_ids_to_tokens(self.suffix_tokens       )

						SCREAMING_SNAKE_CASE__								=      processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,				pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,				self.prefix_tokens + self.suffix_tokens       )       )    ,				)





			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple:
						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
									    """tokenizer."""       )

						if not os.path.isdir(UpperCAmelCase__       ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""       )
									return
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    UpperCAmelCase__    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

						if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCAmelCase__       ):
									copyfile(self.vocab_file    ,				UpperCAmelCase__       )

						return (out_vocab_file,)





"""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,
						)


