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


__SCREAMING_SNAKE_CASE						:   Tuple         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			"▁"

__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

__SCREAMING_SNAKE_CASE						:   List[Any]         =			{
    "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",
    },
}

__SCREAMING_SNAKE_CASE						:   Tuple         =			{
    "facebook/m2m100_418M": 1_0_2_4,
}

# fmt: off
__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			{
    "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        lowercase_   (					_a		):
       _lowerCamelCase						=		VOCAB_FILES_NAMES
       _lowerCamelCase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowerCamelCase						=		PRETRAINED_VOCAB_FILES_MAP
       _lowerCamelCase						=		["""input_ids""", """attention_mask"""]

       _lowerCamelCase						=		[]
       _lowerCamelCase						=		[]



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

             _snake_case						:					Union[str, Any]    =   language_codes
             _snake_case						:					Tuple    =   FAIRSEQ_LANGUAGE_CODES[language_codes]
             _snake_case						:					Tuple    =   {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

             _snake_case						:					Dict    =   kwargs.get("additional_special_tokens"				,    []    )
             kwargs["additional_special_tokens"] += [
                 self.get_lang_token(lowercase_    )
                 for lang_code in fairseq_language_code
                 if self.get_lang_token(lowercase_    ) not in kwargs["additional_special_tokens"]
             ]

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

             _snake_case						:					Optional[int]    =   vocab_file
             _snake_case						:					List[str]    =   load_json(lowercase_    )
             _snake_case						:					Dict    =   {v: k for k, v in self.encoder.items()}
             _snake_case						:					List[str]    =   spm_file
             _snake_case						:					int    =   load_spm(lowercase_				,    self.sp_model_kwargs    )

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

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

             _snake_case						:					Optional[int]    =   src_lang if src_lang is not None else 'en'
             _snake_case						:					Any    =   tgt_lang
             _snake_case						:					Union[str, Any]    =   self.get_lang_id(self._src_lang    )
             self.set_src_lang_special_tokens(self._src_lang    )

             _snake_case						:					Union[str, Any]    =   num_madeup_words



       @property
       def 	UpperCamelCase	(						self    ):
             return len(self.encoder    ) + len(self.lang_token_to_id    )



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



       @src_lang.setter
       def 	UpperCamelCase	(						self				,    lowercase_    ):
             _snake_case						:					Union[str, Any]    =   new_src_lang
             self.set_src_lang_special_tokens(self._src_lang    )



       def 	UpperCamelCase	(						self				,    lowercase_    ):
             return self.sp_model.encode(lowercase_				,    out_type=lowercase_    )



       def 	UpperCamelCase	(						self				,    lowercase_    ):

             if token in self.lang_token_to_id:
                   return self.lang_token_to_id[token]
             return self.encoder.get(lowercase_				,    self.encoder[self.unk_token]    )



       def 	UpperCamelCase	(						self				,    lowercase_    ):

             if index in self.id_to_lang_token:
                   return self.id_to_lang_token[index]
             return self.decoder.get(lowercase_				,    self.unk_token    )



       def 	UpperCamelCase	(						self				,    lowercase_    ):
             _snake_case						:					Optional[Any]    =   []
             _snake_case						:					List[str]    =   ''
             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(lowercase_    ) + token
                         _snake_case						:					Tuple    =   []
                   else:
                         current_sub_tokens.append(lowercase_    )
             out_string += self.sp_model.decode(lowercase_    )
             return out_string.strip()



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = False    ):

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

             _snake_case						:					Tuple    =   [1] * len(self.prefix_tokens    )
             _snake_case						:					List[str]    =   [1] * len(self.suffix_tokens    )
             if token_ids_a is None:
                   return prefix_ones + ([0] * len(lowercase_    )) + suffix_ones
             return prefix_ones + ([0] * len(lowercase_    )) + ([0] * len(lowercase_    )) + suffix_ones



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = 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    ):
             _snake_case						:					str    =   {self.convert_ids_to_tokens(lowercase_    ): i for i in range(self.vocab_size    )}
             vocab.update(self.added_tokens_encoder    )
             return vocab



       def __getstate__(						self    ):
             _snake_case						:					Any    =   self.__dict__.copy()
             _snake_case						:					List[Any]    =   None
             return state



       def __setstate__(						self				,    lowercase_    ):
             _snake_case						:					Optional[int]    =   d

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

             _snake_case						:					Optional[Any]    =   load_spm(self.spm_file				,    self.sp_model_kwargs    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
             _snake_case						:					List[Any]    =   Path(lowercase_    )
             if not save_dir.is_dir():
                   raise OSError(f"""{save_directory} should be a directory"""    )
             _snake_case						:					List[Any]    =   save_dir / (
                 (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
             )
             _snake_case						:					str    =   save_dir / (
                 (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
             )

             save_json(self.encoder				,    lowercase_    )

             if os.path.abspath(self.spm_file    ) != os.path.abspath(lowercase_    ) and os.path.isfile(self.spm_file    ):
                   copyfile(self.spm_file				,    lowercase_    )
             elif not os.path.isfile(self.spm_file    ):
                   with open(lowercase_				,    "wb"    ) as fi:
                         _snake_case						:					Tuple    =   self.sp_model.serialized_model_proto()
                         fi.write(lowercase_    )

             return (str(lowercase_    ), str(lowercase_    ))



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = "en"				,    lowercase_ = None				,    lowercase_ = "ro"				,    **lowercase_				,    ):
             _snake_case						:					int    =   src_lang
             _snake_case						:					List[Any]    =   tgt_lang
             self.set_src_lang_special_tokens(self.src_lang    )
             return super().prepare_seqaseq_batch(lowercase_				,    lowercase_				,    **lowercase_    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    **lowercase_    ):

             if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"    )
             _snake_case						:					Optional[int]    =   src_lang
             _snake_case						:					List[Any]    =   self(lowercase_				,    add_special_tokens=lowercase_				,    **lowercase_    )
             _snake_case						:					List[Any]    =   self.get_lang_id(lowercase_    )
             _snake_case						:					Tuple    =   tgt_lang_id
             return inputs



       def 	UpperCamelCase	(						self    ):
             self.set_src_lang_special_tokens(self.src_lang    )



       def 	UpperCamelCase	(						self    ):
             self.set_tgt_lang_special_tokens(self.tgt_lang    )



       def 	UpperCamelCase	(						self				,    lowercase_    ):
             _snake_case						:					List[Any]    =   self.get_lang_token(lowercase_    )
             _snake_case						:					Optional[Any]    =   self.lang_token_to_id[lang_token]
             _snake_case						:					List[str]    =   [self.cur_lang_id]
             _snake_case						:					Optional[Any]    =   [self.eos_token_id]



       def 	UpperCamelCase	(						self				,    lowercase_    ):
             _snake_case						:					str    =   self.get_lang_token(lowercase_    )
             _snake_case						:					Any    =   self.lang_token_to_id[lang_token]
             _snake_case						:					List[str]    =   [self.cur_lang_id]
             _snake_case						:					List[str]    =   [self.eos_token_id]



       def 	UpperCamelCase	(						self				,    lowercase_    ):
             return self.lang_code_to_token[lang]



       def 	UpperCamelCase	(						self				,    lowercase_    ):
             _snake_case						:					Tuple    =   self.get_lang_token(lowercase_    )
             return self.lang_token_to_id[lang_token]




def  snake_case						(__lowercase ,      __lowercase						)	->	sentencepiece.SentencePieceProcessor:

      '''simple docstring'''



      _snake_case						:					str    =   sentencepiece.SentencePieceProcessor(**lowerCamelCase_						)
      spm.Load(str(lowerCamelCase_						)						)
      return spm
def  snake_case						(__lowercase						)	->	Union[Dict, List]:

      '''simple docstring'''



      with open(lowerCamelCase_ ,      "r"						) as f:
            return json.load(lowerCamelCase_						)

def  snake_case						(__lowercase ,      __lowercase						)	->	None:

      '''simple docstring'''



      with open(lowerCamelCase_ ,      "w"						) as f:
            json.dump(lowerCamelCase_ ,      lowerCamelCase_ ,      indent=2						)
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)