
"""simple docstring"""


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

import sentencepiece as spm

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


A_    							=					logging.get_logger(__name__)

A_    							=					"""▁"""

A_    							=					{"""vocab_file""": """sentencepiece.bpe.model"""}

A_    							=					{
    """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"""
        ),
    }
}

A_    							=					{
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
A_    							=					["""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  ( lowerCAmelCase			):
				a__:      Union[str, Any]          =    VOCAB_FILES_NAMES
				a__:      List[str]          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__:      List[Any]          =    PRETRAINED_VOCAB_FILES_MAP
				a__:      Tuple          =    ['input_ids', 'attention_mask']

				a__:      List[int]          =    []
				a__:      List[int]          =    []




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase="<s>"							,						UpperCAmelCase="</s>"							,						UpperCAmelCase="</s>"							,						UpperCAmelCase="<s>"							,						UpperCAmelCase="<unk>"							,						UpperCAmelCase="<pad>"							,						UpperCAmelCase="<mask>"							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase = None							,						UpperCAmelCase=None							,						**UpperCAmelCase							,						):
							# Mask token behave like a normal word, i.e. include the space before it
							lowerCamelCase_					=  AddedToken(UpperCAmelCase							,						lstrip=UpperCAmelCase							,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase							,						UpperCAmelCase   ) else mask_token

							lowerCamelCase_					=  {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=UpperCAmelCase							,						eos_token=UpperCAmelCase							,						unk_token=UpperCAmelCase							,						sep_token=UpperCAmelCase							,						cls_token=UpperCAmelCase							,						pad_token=UpperCAmelCase							,						mask_token=UpperCAmelCase							,						tokenizer_file=UpperCAmelCase							,						src_lang=UpperCAmelCase							,						tgt_lang=UpperCAmelCase							,						additional_special_tokens=UpperCAmelCase							,						sp_model_kwargs=self.sp_model_kwargs							,						**UpperCAmelCase							,						)

							lowerCamelCase_					=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(UpperCAmelCase   )   )
							lowerCamelCase_					=  vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							lowerCamelCase_					=  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							lowerCamelCase_					=  1

							lowerCamelCase_					=  len(self.sp_model   )
							lowerCamelCase_					=  {
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCAmelCase   )
							}
							lowerCamelCase_					=  {v: k for k, v in self.lang_code_to_id.items()}
							lowerCamelCase_					=  len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

							self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
							lowerCamelCase_					=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							lowerCamelCase_					=  list(self.lang_code_to_id.keys()   )

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

							lowerCamelCase_					=  src_lang if src_lang is not None else '''en_XX'''
							lowerCamelCase_					=  self.lang_code_to_id[self._src_lang]
							lowerCamelCase_					=  tgt_lang
							self.set_src_lang_special_tokens(self._src_lang   )




				def __getstate__(				self   ):
							lowerCamelCase_					=  self.__dict__.copy()
							lowerCamelCase_					=  None
							lowerCamelCase_					=  self.sp_model.serialized_model_proto()
							return state




				def __setstate__(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  d

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

							lowerCamelCase_					=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )




				@property
				def        UpperCAmelCase__						(				self   ):
							return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token




				@property
				def        UpperCAmelCase__						(				self   ):
							return self._src_lang




				@src_lang.setter
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = False   ):

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCAmelCase							,						token_ids_a=UpperCAmelCase							,						already_has_special_tokens=UpperCAmelCase   )

							lowerCamelCase_					=  [1] * len(self.prefix_tokens   )
							lowerCamelCase_					=  [1] * len(self.suffix_tokens   )
							if token_ids_a is None:
										return prefix_ones + ([0] * len(UpperCAmelCase   )) + suffix_ones
							return prefix_ones + ([0] * len(UpperCAmelCase   )) + ([0] * len(UpperCAmelCase   )) + suffix_ones




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							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        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							lowerCamelCase_					=  [self.sep_token_id]
							lowerCamelCase_					=  [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        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							if src_lang is None or tgt_lang is None:
										raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''   )
							lowerCamelCase_					=  src_lang
							lowerCamelCase_					=  self(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						return_tensors=UpperCAmelCase							,						**UpperCAmelCase   )
							lowerCamelCase_					=  self.convert_tokens_to_ids(UpperCAmelCase   )
							lowerCamelCase_					=  tgt_lang_id
							return inputs




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  {self.convert_ids_to_tokens(UpperCAmelCase   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return self.sp_model.encode(UpperCAmelCase							,						out_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowerCamelCase_					=  self.sp_model.PieceToId(UpperCAmelCase   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							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							,						UpperCAmelCase   ):
							lowerCamelCase_					=  ''''''.join(UpperCAmelCase   ).replace(UpperCAmelCase							,						''' '''   ).strip()
							return out_string




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							if not os.path.isdir(UpperCAmelCase   ):
										logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
										return
							lowerCamelCase_					=  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   ) and os.path.isfile(self.vocab_file   ):
										copyfile(self.vocab_file							,						UpperCAmelCase   )
							elif not os.path.isfile(self.vocab_file   ):
										with open(UpperCAmelCase							,						'''wb'''   ) as fi:
													lowerCamelCase_					=  self.sp_model.serialized_model_proto()
													fi.write(UpperCAmelCase   )

							return (out_vocab_file,)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = "en_XX"							,						UpperCAmelCase = None							,						UpperCAmelCase = "ro_RO"							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  src_lang
							lowerCamelCase_					=  tgt_lang
							return super().prepare_seqaseq_batch(UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							return self.set_src_lang_special_tokens(self.src_lang   )




				def        UpperCAmelCase__						(				self   ):
							return self.set_tgt_lang_special_tokens(self.tgt_lang   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.lang_code_to_id[src_lang]
							lowerCamelCase_					=  []
							lowerCamelCase_					=  [self.eos_token_id, self.cur_lang_code]





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.lang_code_to_id[lang]
							lowerCamelCase_					=  []
							lowerCamelCase_					=  [self.eos_token_id, self.cur_lang_code]









import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a__		     =     ['''small''', '''medium''', '''large''']

a__		     =     '''lm_head.decoder.weight'''
a__		     =     '''lm_head.weight'''





def 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
	a__		     =     parser.parse_args()
	for MODEL in DIALOGPT_MODELS:
		a__		     =     os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
		a__		     =     f"./DialoGPT-{MODEL}"
		convert_dialogpt_checkpoint(
		    checkpoint_path,
		    pytorch_dump_folder_path,
		)






