"""simple docstring"""





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_UpperCamelCase  :		Dict           =       logging.get_logger(__name__)

_UpperCamelCase  :		Any           =       "▁"

_UpperCamelCase  :		Any           =       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCamelCase  :		str           =       {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

_UpperCamelCase  :		Optional[int]           =       {
    "facebook/m2m100_418M": 10_24,
}

# fmt: off
_UpperCamelCase  :		str           =       {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}


class 	UpperCAmelCase_      (       A_):
      lowerCamelCase__						:       int					    =    VOCAB_FILES_NAMES
      lowerCamelCase__						:       str					    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__						:       str					    =    PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__						:       Optional[int]					    =    ["input_ids", "attention_mask"]

      lowerCamelCase__						:       List[str]					    =    []
      lowerCamelCase__						:       Tuple					    =    []


      def __init__(  self						,     a						,     a						,     a=None						,     a=None						,     a="<s>"						,     a="</s>"						,     a="</s>"						,     a="<pad>"						,     a="<unk>"						,     a="m2m100"						,     a = None						,     a=8						,     **a						,     )			->   None:
             lowercase__     :						List[Any]         =      {} if sp_model_kwargs is None else sp_model_kwargs

             lowercase__     :						List[Any]         =      language_codes
             lowercase__     :						List[Any]         =      FAIRSEQ_LANGUAGE_CODES[language_codes]
             lowercase__     :						str         =      {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

             lowercase__     :						Optional[Any]         =      kwargs.get('additional_special_tokens'						,     []				)
             kwargs["additional_special_tokens"] += [
                 self.get_lang_token(a				)
                 for lang_code in fairseq_language_code
                 if self.get_lang_token(a				) not in kwargs["additional_special_tokens"]
             ]

             super().__init__(
                 src_lang=a						,     tgt_lang=a						,     bos_token=a						,     eos_token=a						,     sep_token=a						,     unk_token=a						,     pad_token=a						,     language_codes=a						,     sp_model_kwargs=self.sp_model_kwargs						,     num_madeup_words=a						,     **a						,     )

             lowercase__     :						Union[str, Any]         =      vocab_file
             lowercase__     :						str         =      load_json(a				)
             lowercase__     :						List[Any]         =      {v: k for k, v in self.encoder.items()}
             lowercase__     :						Optional[int]         =      spm_file
             lowercase__     :						Union[str, Any]         =      load_spm(a						,     self.sp_model_kwargs				)

             lowercase__     :						Optional[int]         =      len(self.encoder				)

             lowercase__     :						Dict         =      {
                 self.get_lang_token(a				): self.encoder_size + i for i, lang_code in enumerate(a				)
             }
             lowercase__     :						List[Any]         =      {lang_code: self.encoder_size + i for i, lang_code in enumerate(a				)}
             lowercase__     :						str         =      {v: k for k, v in self.lang_token_to_id.items()}

             lowercase__     :						List[str]         =      src_lang if src_lang is not None else """en"""
             lowercase__     :						List[str]         =      tgt_lang
             lowercase__     :						List[str]         =      self.get_lang_id(self._src_lang				)
             self.set_src_lang_special_tokens(self._src_lang				)

             lowercase__     :						Dict         =      num_madeup_words


      @property
      def 						_UpperCAmelCase			(  self				)			->   int:
             return len(self.encoder				) + len(self.lang_token_to_id				)


      @property
      def 						_UpperCAmelCase			(  self				)			->   str:
             return self._src_lang


      @src_lang.setter
      def 						_UpperCAmelCase			(  self						,     a				)			->   None:
             lowercase__     :						Dict         =      new_src_lang
             self.set_src_lang_special_tokens(self._src_lang				)


      def 						_UpperCAmelCase			(  self						,     a				)			->   List[str]:
             return self.sp_model.encode(a						,     out_type=a				)


      def 						_UpperCAmelCase			(  self						,     a				)			->   List[str]:
             if token in self.lang_token_to_id:
                    return self.lang_token_to_id[token]
             return self.encoder.get(a						,     self.encoder[self.unk_token]				)


      def 						_UpperCAmelCase			(  self						,     a				)			->   str:
             if index in self.id_to_lang_token:
                    return self.id_to_lang_token[index]
             return self.decoder.get(a						,     self.unk_token				)


      def 						_UpperCAmelCase			(  self						,     a				)			->   Dict:
             lowercase__     :						Tuple         =      []
             lowercase__     :						Optional[int]         =      """"""
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           out_string += self.sp_model.decode(a				) + token
                           lowercase__     :						Tuple         =      []
                    else:
                           current_sub_tokens.append(a				)
             out_string += self.sp_model.decode(a				)
             return out_string.strip()


      def 						_UpperCAmelCase			(  self						,     a						,     a = None						,     a = False				)			->   List[int]:

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

             lowercase__     :						Union[str, Any]         =      [1] * len(self.prefix_tokens				)
             lowercase__     :						Dict         =      [1] * len(self.suffix_tokens				)
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(a				)) + suffix_ones
             return prefix_ones + ([0] * len(a				)) + ([0] * len(a				)) + suffix_ones


      def 						_UpperCAmelCase			(  self						,     a						,     a = None				)			->   List[int]:
             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				)			->   Dict:
             lowercase__     :						Dict         =      {self.convert_ids_to_tokens(a				): i for i in range(self.vocab_size				)}
             vocab.update(self.added_tokens_encoder				)
             return vocab


      def __getstate__(  self				)			->   Dict:
             lowercase__     :						str         =      self.__dict__.copy()
             lowercase__     :						List[Any]         =      None
             return state


      def __setstate__(  self						,     a				)			->   None:
             lowercase__     :						Any         =      d

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

             lowercase__     :						Any         =      load_spm(self.spm_file						,     self.sp_model_kwargs				)


      def 						_UpperCAmelCase			(  self						,     a						,     a = None				)			->   Tuple[str]:
             lowercase__     :						Optional[int]         =      Path(a				)
             if not save_dir.is_dir():
                    raise OSError(f"""{save_directory} should be a directory"""				)
             lowercase__     :						Any         =      save_dir / (
                 (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
             )
             lowercase__     :						Optional[Any]         =      save_dir / (
                 (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
             )

             save_json(self.encoder						,     a				)

             if os.path.abspath(self.spm_file				) != os.path.abspath(a				) and os.path.isfile(self.spm_file				):
                    copyfile(self.spm_file						,     a				)
             elif not os.path.isfile(self.spm_file				):
                    with open(a						,     'wb'				) as fi:
                           lowercase__     :						Union[str, Any]         =      self.sp_model.serialized_model_proto()
                           fi.write(a				)

             return (str(a				), str(a				))


      def 						_UpperCAmelCase			(  self						,     a						,     a = "en"						,     a = None						,     a = "ro"						,     **a						,     )			->   BatchEncoding:
             lowercase__     :						List[Any]         =      src_lang
             lowercase__     :						Union[str, Any]         =      tgt_lang
             self.set_src_lang_special_tokens(self.src_lang				)
             return super().prepare_seqaseq_batch(a						,     a						,     **a				)


      def 						_UpperCAmelCase			(  self						,     a						,     a						,     a						,     **a				)			->   List[str]:
             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__     :						List[str]         =      src_lang
             lowercase__     :						str         =      self(a						,     add_special_tokens=a						,     **a				)
             lowercase__     :						List[Any]         =      self.get_lang_id(a				)
             lowercase__     :						str         =      tgt_lang_id
             return inputs


      def 						_UpperCAmelCase			(  self				)			->   Any:
             self.set_src_lang_special_tokens(self.src_lang				)


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             self.set_tgt_lang_special_tokens(self.tgt_lang				)


      def 						_UpperCAmelCase			(  self						,     a				)			->   None:
             lowercase__     :						List[Any]         =      self.get_lang_token(a				)
             lowercase__     :						List[str]         =      self.lang_token_to_id[lang_token]
             lowercase__     :						str         =      [self.cur_lang_id]
             lowercase__     :						int         =      [self.eos_token_id]


      def 						_UpperCAmelCase			(  self						,     a				)			->   None:
             lowercase__     :						int         =      self.get_lang_token(a				)
             lowercase__     :						Optional[Any]         =      self.lang_token_to_id[lang_token]
             lowercase__     :						List[Any]         =      [self.cur_lang_id]
             lowercase__     :						Tuple         =      [self.eos_token_id]


      def 						_UpperCAmelCase			(  self						,     a				)			->   str:
             return self.lang_code_to_token[lang]


      def 						_UpperCAmelCase			(  self						,     a				)			->   int:
             lowercase__     :						int         =      self.get_lang_token(a				)
             return self.lang_token_to_id[lang_token]





def 							a_	( _lowerCAmelCase    :					str  ,					_lowerCAmelCase    :					Dict[str, Any]	):





       '''simple docstring'''

       lowercase__     :						List[str]         =      sentencepiece.SentencePieceProcessor(**__snake_case	)
       spm.Load(str(__snake_case	)	)
       return spm





def 							a_	( _lowerCAmelCase    :					str	):





       '''simple docstring'''

       with open(__snake_case  ,					'r'	) as f:
              return json.load(__snake_case	)






def 							a_	( _lowerCAmelCase    :					List[str]  ,					_lowerCAmelCase    :					str	):





       '''simple docstring'''

       with open(__snake_case  ,					'w'	) as f:
              json.dump(__snake_case  ,					__snake_case  ,					indent=2	)





"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch

def 				_snake_case      (   __snake_case							:      List[str]   ):




   """simple docstring"""







   for param in module.parameters():
      _lowerCamelCase	:					Optional[Any]   =		False

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		"""cuda""" if torch.cuda.is_available() else """cpu"""
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      _lowerCamelCase	:					Any   =		"""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 				_snake_case      (   __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		plt.imshow(__snake_case   )
   fig.axes.get_xaxis().set_visible(__snake_case   )
   fig.axes.get_yaxis().set_visible(__snake_case   )
   plt.show()





def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		datetime.now()
   _lowerCamelCase	:					Optional[Any]   =		current_time.strftime("""%H:%M:%S"""   )
   return timestamp

