

'''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


_snake_case     =  logging.get_logger(__name__)

_snake_case     =  '▁'

_snake_case     =  {'vocab_file': 'sentencepiece.bpe.model'}

_snake_case     =  {
    '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'
        ),
    }
}

_snake_case     =  {
    'facebook/mbart-large-en-ro': 1_024,
    'facebook/mbart-large-cc25': 1_024,
}

# fmt: off
_snake_case     =  ['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    a__  (							A__  ):
   _SCREAMING_SNAKE_CASE    :     Optional[Any]	       =							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE    :     Optional[int]	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE    :     Tuple	       =							PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE    :     str	       =							['input_ids', 'attention_mask']

   _SCREAMING_SNAKE_CASE    :     Any	       =							[]
   _SCREAMING_SNAKE_CASE    :     Optional[Any]	       =							[]


   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			,				):





      """simple docstring"""





      _lowercase			:      Dict			     =   AddedToken(UpperCamelCase_			,				lstrip=UpperCamelCase_			,				rstrip=UpperCamelCase_  ) if isinstance(UpperCamelCase_			,				UpperCamelCase_  ) else mask_token

      _lowercase			:      int			     =   {} 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_			,				)

      _lowercase			:      int			     =   spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(str(UpperCamelCase_  )  )
      _lowercase			:      Optional[int]			     =   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
      _lowercase			:      Dict			     =   {'''<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
      _lowercase			:      Tuple			     =   1

      _lowercase			:      int			     =   len(self.sp_model  )
      _lowercase			:      Dict			     =   {
          code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_  )
      }
      _lowercase			:      List[Any]			     =   {v: k for k, v in self.lang_code_to_id.items()}
      _lowercase			:      Any			     =   len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

      self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
      _lowercase			:      Union[str, Any]			     =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
      _lowercase			:      Union[str, Any]			     =   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]  )

      _lowercase			:      Any			     =   src_lang if src_lang is not None else '''en_XX'''
      _lowercase			:      Optional[Any]			     =   self.lang_code_to_id[self._src_lang]
      _lowercase			:      Union[str, Any]			     =   tgt_lang
      self.set_src_lang_special_tokens(self._src_lang  )


   def __getstate__( self  ):





      """simple docstring"""





      _lowercase			:      Dict			     =   self.__dict__.copy()
      _lowercase			:      int			     =   None
      _lowercase			:      Dict			     =   self.sp_model.serialized_model_proto()
      return state


   def __setstate__( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Tuple			     =   d

      # for backward compatibility
      if not hasattr(self			,				"sp_model_kwargs"  ):
         _lowercase			:      Optional[int]			     =   {}

      _lowercase			:      Optional[Any]			     =   spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )


   @property
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token


   @property
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      return self._src_lang


   @src_lang.setter
   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[Any]			     =   new_src_lang
      self.set_src_lang_special_tokens(self._src_lang  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase = None			,				_UpperCamelCase = False  ):





      """simple docstring"""





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

      _lowercase			:      List[str]			     =   [1] * len(self.prefix_tokens  )
      _lowercase			:      int			     =   [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 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase = None  ):





      """simple docstring"""





      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 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase = None  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   [self.sep_token_id]
      _lowercase			:      List[Any]			     =   [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 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      if src_lang is None or tgt_lang is None:
         raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"  )
      _lowercase			:      Tuple			     =   src_lang
      _lowercase			:      Optional[Any]			     =   self(UpperCamelCase_			,				add_special_tokens=UpperCamelCase_			,				return_tensors=UpperCamelCase_			,				**UpperCamelCase_  )
      _lowercase			:      List[str]			     =   self.convert_tokens_to_ids(UpperCamelCase_  )
      _lowercase			:      Dict			     =   tgt_lang_id
      return inputs


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Tuple			     =   {self.convert_ids_to_tokens(UpperCamelCase_  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      return self.sp_model.encode(UpperCamelCase_			,				out_type=UpperCamelCase_  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _lowercase			:      Any			     =   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 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      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 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   ''''''.join(UpperCamelCase_  ).replace(UpperCamelCase_			,				" "  ).strip()
      return out_string


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase = None  ):





      """simple docstring"""





      if not os.path.isdir(UpperCamelCase_  ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
         return
      _lowercase			:      int			     =   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:
            _lowercase			:      Any			     =   self.sp_model.serialized_model_proto()
            fi.write(UpperCamelCase_  )

      return (out_vocab_file,)


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase = "en_XX"			,				_UpperCamelCase = None			,				_UpperCamelCase = "ro_RO"			,				**_UpperCamelCase			,				):





      """simple docstring"""





      _lowercase			:      Optional[Any]			     =   src_lang
      _lowercase			:      Optional[Any]			     =   tgt_lang
      return super().prepare_seqaseq_batch(UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      return self.set_src_lang_special_tokens(self.src_lang  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      return self.set_tgt_lang_special_tokens(self.tgt_lang  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Any			     =   self.lang_code_to_id[src_lang]
      _lowercase			:      int			     =   []
      _lowercase			:      Union[str, Any]			     =   [self.eos_token_id, self.cur_lang_code]






   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Dict			     =   self.lang_code_to_id[lang]
      _lowercase			:      Optional[Any]			     =   []
      _lowercase			:      Tuple			     =   [self.eos_token_id, self.cur_lang_code]










'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					for param in module.parameters():
										UpperCamelCase__   :Dict												= False


def 			a      (			)  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= '''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										UpperCamelCase__   :Optional[int]												= '''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''   )
					return device


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= plt.imshow(__a   )
					fig.axes.get_xaxis().set_visible(__a   )
					fig.axes.get_yaxis().set_visible(__a   )
					plt.show()







def 			a      (			)  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= datetime.now()
					UpperCamelCase__   :str												= current_time.strftime('''%H:%M:%S'''   )
					return timestamp