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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								="▁"

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

__SCREAMING_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"
        ),
    }
}

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

# fmt: off
__SCREAMING_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  UpperCamelCase	(    _UpperCamelCase						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						['input_ids', 'attention_mask']

   lowercase							=						[]
   lowercase							=						[]
   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  ,)			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			AddedToken(_UpperCAmelCase  ,lstrip=_UpperCAmelCase  ,rstrip=_UpperCAmelCase    ) if isinstance(_UpperCAmelCase  ,_UpperCAmelCase    ) else mask_token

        lowercase_			:       Tuple									=			{} 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_			:       List[str]									=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(_UpperCAmelCase    )    )
        lowercase_			:       Tuple									=			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_			:       Optional[Any]									=			{'''<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_			:       List[Any]									=			1

        lowercase_			:       Dict									=			len(self.sp_model    )
        lowercase_			:       Any									=			{
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCAmelCase    )
        }
        lowercase_			:       Optional[int]									=			{v: k for k, v in self.lang_code_to_id.items()}
        lowercase_			:       Optional[int]									=			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_			:       List[Any]									=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
        lowercase_			:       List[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_			:       List[Any]									=			self.lang_code_to_id[self._src_lang]
        lowercase_			:       str									=			tgt_lang
        self.set_src_lang_special_tokens(self._src_lang    )
   def __getstate__(    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Dict									=			self.__dict__.copy()
        lowercase_			:       str									=			None
        lowercase_			:       Tuple									=			self.sp_model.serialized_model_proto()
        return state
   def __setstate__(    self  ,__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			d

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

        lowercase_			:       List[str]									=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )
   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''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     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return self._src_lang
   @src_lang.setter
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       str									=			new_src_lang
        self.set_src_lang_special_tokens(self._src_lang    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = False    )			->   Tuple:


        '''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_			:       Any									=			[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    )			->   Optional[int]:


        '''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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			[self.sep_token_id]
        lowercase_			:       List[str]									=			[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    )			->   Optional[int]:


        '''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_			:       Optional[int]									=			src_lang
        lowercase_			:       List[Any]									=			self(_UpperCAmelCase  ,add_special_tokens=_UpperCAmelCase  ,return_tensors=_UpperCAmelCase  ,**_UpperCAmelCase    )
        lowercase_			:       Any									=			self.convert_tokens_to_ids(_UpperCAmelCase    )
        lowercase_			:       Any									=			tgt_lang_id
        return inputs
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       int									=			{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    )			->   Optional[Any]:


        '''simple docstring'''


        return self.sp_model.encode(_UpperCAmelCase  ,out_type=_UpperCAmelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[int]:


        '''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     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   List[str]:


        '''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     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Dict									=			''''''.join(_UpperCAmelCase    ).replace(_UpperCAmelCase  ,' '    ).strip()
        return out_string
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   int:


        '''simple docstring'''


        if not os.path.isdir(_UpperCAmelCase    ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
             return
        lowercase_			:       List[Any]									=			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_			:       str									=			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  ,)			->   Dict:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			src_lang
        lowercase_			:       Union[str, Any]									=			tgt_lang
        return super().prepare_seqaseq_batch(_UpperCAmelCase  ,_UpperCAmelCase  ,**_UpperCAmelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        return self.set_src_lang_special_tokens(self.src_lang    )
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        return self.set_tgt_lang_special_tokens(self.tgt_lang    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


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







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


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


"""simple docstring"""







from random import randint, random



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  int = 5  ,    ):
     lowercase_			:       Union[str, Any]									=			[[-1] * number_of_cells]  # Create a highway without any car
     lowercase_			:       str									=			0
     lowercase_			:       List[Any]									=			max(__SCREAMING_SNAKE_CASE  ,    0   )
     while i < number_of_cells:
          lowercase_			:       Union[str, Any]									=			(
              randint(0  ,    __SCREAMING_SNAKE_CASE   ) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1  ,    max_speed * 2   ) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			0
     lowercase_			:       Dict									=			highway_now[car_index + 1 :]
     for cell in range(len(__SCREAMING_SNAKE_CASE   )   ):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(__SCREAMING_SNAKE_CASE  ,    -1   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   )
     # Beforce calculations, the highway is empty
     lowercase_			:       Dict									=			[-1] * number_of_cells

     for car_index in range(__SCREAMING_SNAKE_CASE   ):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               lowercase_			:       Any									=			min(highway_now[car_index] + 1  ,    __SCREAMING_SNAKE_CASE   )
               # Number of empty cell before the next car
               lowercase_			:       List[Any]									=			get_distance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) - 1
               # We can't have the car causing an accident
               lowercase_			:       Dict									=			min(next_highway[car_index]  ,    __SCREAMING_SNAKE_CASE   )
               if random() < probability:
                    # Randomly, a driver will slow down
                    lowercase_			:       Optional[Any]									=			max(next_highway[car_index] - 1  ,    0   )
     return next_highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			len(highway[0]   )

     for i in range(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Optional[Any]									=			update(highway[i]  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
          lowercase_			:       List[str]									=			[-1] * number_of_cells

          for car_index in range(__SCREAMING_SNAKE_CASE   ):
               lowercase_			:       Optional[Any]									=			next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    lowercase_			:       Any									=			(car_index + speed) % number_of_cells
                    # Commit the change of position
                    lowercase_			:       Optional[Any]									=			speed
          highway.append(__SCREAMING_SNAKE_CASE   )

     return highway


if __name__ == "__main__":
       import doctest

       doctest.testmod()

