





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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    __lowerCamelCase	:					Optional[int]			  =					None


__lowerCamelCase	:					Tuple			  =					logging.get_logger(__name__)


__lowerCamelCase	:					Dict			  =					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase	:					Dict			  =					{
    """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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

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

# fmt: off
__lowerCamelCase	:					Any			  =					["""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__       (	__snake_case      ):
      _UpperCAmelCase     :Optional[int]						   =       VOCAB_FILES_NAMES
      _UpperCAmelCase     :int						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase     :List[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase     :Optional[Any]						   =       ['input_ids', 'attention_mask']
      _UpperCAmelCase     :Union[str, Any]						   =       MBartTokenizer

      _UpperCAmelCase     :List[int]						   =       []
      _UpperCAmelCase     :List[int]						   =       []





      def __init__(			self	,							A_=None	,							A_=None	,							A_="<s>"	,							A_="</s>"	,							A_="</s>"	,							A_="<s>"	,							A_="<unk>"	,							A_="<pad>"	,							A_="<mask>"	,							A_=None	,							A_=None	,							A_=None	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : int	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else mask_token

       super().__init__(
           vocab_file=A_	,							tokenizer_file=A_	,							bos_token=A_	,							eos_token=A_	,							sep_token=A_	,							cls_token=A_	,							unk_token=A_	,							pad_token=A_	,							mask_token=A_	,							src_lang=A_	,							tgt_lang=A_	,							additional_special_tokens=A_	,							**A_	,							)

       UpperCamelCase     : List[Any]	     =       vocab_file
       UpperCamelCase     : Any	     =       False if not self.vocab_file else True

       UpperCamelCase     : str	     =       FAIRSEQ_LANGUAGE_CODES.copy()

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

       self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}       )
       UpperCamelCase     : Union[str, Any]	     =       {
           lang_code: self.convert_tokens_to_ids(A_       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       UpperCamelCase     : int	     =       src_lang if src_lang is not None else "en_XX"
       UpperCamelCase     : List[str]	     =       self.convert_tokens_to_ids(self._src_lang       )
       UpperCamelCase     : Dict	     =       tgt_lang
       self.set_src_lang_special_tokens(self._src_lang       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._src_lang





      @src_lang.setter
      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       new_src_lang
       self.set_src_lang_special_tokens(self._src_lang       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = 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 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       [self.sep_token_id]
       UpperCamelCase     : 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	,							A_	,							A_	,							A_	,							A_	,							**A_       ):
       '''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"       )
       UpperCamelCase     : List[str]	     =       src_lang
       UpperCamelCase     : Optional[int]	     =       self(A_	,							add_special_tokens=A_	,							return_tensors=A_	,							**A_       )
       UpperCamelCase     : Optional[int]	     =       self.convert_tokens_to_ids(A_       )
       UpperCamelCase     : Optional[int]	     =       tgt_lang_id
       return inputs





      def 	__UpperCamelCase(			self	,							A_	,							A_ = "en_XX"	,							A_ = None	,							A_ = "ro_RO"	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : int	     =       src_lang
       UpperCamelCase     : int	     =       tgt_lang
       return super().prepare_seqaseq_batch(A_	,							A_	,							**A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.set_src_lang_special_tokens(self.src_lang       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.set_tgt_lang_special_tokens(self.tgt_lang       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.convert_tokens_to_ids(A_       )
       UpperCamelCase     : Optional[int]	     =       []
       UpperCamelCase     : Tuple	     =       [self.eos_token_id, self.cur_lang_code]

       UpperCamelCase     : Dict	     =       self.convert_ids_to_tokens(self.prefix_tokens       )
       UpperCamelCase     : Tuple	     =       self.convert_ids_to_tokens(self.suffix_tokens       )

       UpperCamelCase     : str	     =       processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,							pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,							self.prefix_tokens + self.suffix_tokens       )       )	,							)





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.convert_tokens_to_ids(A_       )
       UpperCamelCase     : Any	     =       []
       UpperCamelCase     : int	     =       [self.eos_token_id, self.cur_lang_code]

       UpperCamelCase     : Any	     =       self.convert_ids_to_tokens(self.prefix_tokens       )
       UpperCamelCase     : int	     =       self.convert_ids_to_tokens(self.suffix_tokens       )

       UpperCamelCase     : int	     =       processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,							pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,							self.prefix_tokens + self.suffix_tokens       )       )	,							)





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       if not self.can_save_slow_tokenizer:
        raise ValueError(
            "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
            "tokenizer."       )

       if not os.path.isdir(A_       ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""       )
        return
       UpperCamelCase     : Optional[Any]	     =       os.path.join(
           A_	,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

       if os.path.abspath(self.vocab_file       ) != os.path.abspath(A_       ):
        copyfile(self.vocab_file	,							A_       )

       return (out_vocab_file,)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
