





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


_lowerCAmelCase				:   str						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   Optional[int]						       =						'''▁'''

_lowerCAmelCase				:   Dict						       =						{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCAmelCase				:   str						       =						{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowerCAmelCase				:   str						       =						{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_024,
}

# fmt: off
_lowerCAmelCase				:   Union[str, 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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']


class        __magic_name__	(				a_				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']

     __UpperCamelCase            =  []
     __UpperCamelCase            =  []



     def __init__(				self							:Optional[int]      ,   snake_case							:Union[str, Any]      ,   snake_case							:str=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:Optional[int]="</s>"      ,   snake_case							:Optional[Any]="</s>"      ,   snake_case							:Dict="<s>"      ,   snake_case							:Dict="<unk>"      ,   snake_case							:Union[str, Any]="<pad>"      ,   snake_case							:Optional[int]="<mask>"      ,   snake_case							:Optional[Dict[str, Any]] = None      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        A_       :					Dict        =      AddedToken(lowercase_      ,   lstrip=lowercase_      ,   rstrip=lowercase_	) if isinstance(lowercase_      ,   lowercase_	) else mask_token

        A_       :					Any        =      {} if sp_model_kwargs is None else sp_model_kwargs

        A_       :					Dict        =      kwargs.get("additional_special_tokens"      ,   []	)
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=lowercase_      ,   tgt_lang=lowercase_      ,   eos_token=lowercase_      ,   unk_token=lowercase_      ,   sep_token=lowercase_      ,   cls_token=lowercase_      ,   pad_token=lowercase_      ,   mask_token=lowercase_      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **lowercase_      ,   )

        A_       :					int        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(lowercase_	)	)
        A_       :					List[str]        =      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
        A_       :					Union[str, 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
        A_       :					Optional[int]        =      1

        A_       :					Optional[Any]        =      len(self.sp_model	)
        A_       :					int        =      {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase_	)
        }
        A_       :					Union[str, Any]        =      {v: k for k, v in self.lang_code_to_id.items()}
        A_       :					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	)
        A_       :					Optional[Any]        =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

        A_       :					str        =      src_lang if src_lang is not None else '''en_XX'''
        A_       :					Dict        =      self.lang_code_to_id[self._src_lang]
        A_       :					List[str]        =      tgt_lang
        self.set_src_lang_special_tokens(self._src_lang	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self._src_lang



     @src_lang.setter
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:str	):





        '''simple docstring'''




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



     def __getstate__(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					List[str]        =      self.__dict__.copy()
        A_       :					Dict        =      None
        return state



     def __setstate__(				self							:str      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Any        =      d

        # for backward compatibility
        if not hasattr(self      ,   "sp_model_kwargs"	):
           A_       :					List[Any]        =      {}

        A_       :					Union[str, Any]        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(self.vocab_file	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_       :					List[Any]        =      {self.convert_ids_to_tokens(lowercase_	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:str	):





        '''simple docstring'''




        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        A_       :					List[Any]        =      self.sp_model.PieceToId(lowercase_	)

        # 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     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:int	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:int	):





        '''simple docstring'''




        A_       :					Dict        =      []
        A_       :					Any        =      ''''''
        A_       :					Tuple        =      False
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              if not prev_is_special:
                 out_string += " "
              out_string += self.sp_model.decode(lowercase_	) + token
              A_       :					Optional[int]        =      True
              A_       :					str        =      []
           else:
              current_sub_tokens.append(lowercase_	)
              A_       :					Optional[Any]        =      False
        out_string += self.sp_model.decode(lowercase_	)
        return out_string.strip()



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




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

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

        return (out_vocab_file,)



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None      ,   snake_case							:bool = False	):





        '''simple docstring'''




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

        A_       :					List[str]        =      [1] * len(self.prefix_tokens	)
        A_       :					int        =      [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     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = 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     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:str      ,   snake_case							:str      ,   snake_case							:Optional[str]      ,   snake_case							:Optional[str]      ,   **snake_case							:Dict	):





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



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:List[str]      ,   snake_case							:str = "en_XX"      ,   snake_case							:Optional[List[str]] = None      ,   snake_case							:str = "ro_RO"      ,   **snake_case							:List[Any]      ,   ):





        '''simple docstring'''




        A_       :					Any        =      src_lang
        A_       :					Dict        =      tgt_lang
        return super().prepare_seqaseq_batch(lowercase_      ,   lowercase_      ,   **lowercase_	)



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        return self.set_src_lang_special_tokens(self.src_lang	)



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        return self.set_tgt_lang_special_tokens(self.tgt_lang	)



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					int        =      self.lang_code_to_id[src_lang]
        A_       :					Optional[int]        =      [self.cur_lang_code_id]
        A_       :					Dict        =      [self.eos_token_id]





     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					str        =      self.lang_code_to_id[tgt_lang]
        A_       :					Union[str, Any]        =      [self.cur_lang_code_id]
        A_       :					str        =      [self.eos_token_id]



"""simple docstring"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Any				 =						{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"t5"
					lowercase__												=			["past_key_values"]
					lowercase__												=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







					def __init__(			self     :							Tuple  ,lowercase_     :							Tuple=3_2_1_2_8  ,lowercase_     :							Union[str, Any]=5_1_2  ,lowercase_     :							Dict=6_4  ,lowercase_     :							Any=2_0_4_8  ,lowercase_     :							Dict=6  ,lowercase_     :							Dict=None  ,lowercase_     :							Dict=8  ,lowercase_     :							Optional[int]=3_2  ,lowercase_     :							List[Any]=1_2_8  ,lowercase_     :							Any=0.1  ,lowercase_     :							Optional[Any]=1E-6  ,lowercase_     :							Any=1.0  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Tuple=True  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Tuple=0  ,lowercase_     :							Optional[Any]=1  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Tuple    							=      vocab_size
						lowerCAmelCase__      :		List[str]    							=      d_model
						lowerCAmelCase__      :		Union[str, Any]    							=      d_kv
						lowerCAmelCase__      :		List[Any]    							=      d_ff
						lowerCAmelCase__      :		int    							=      num_layers
						lowerCAmelCase__      :		Union[str, Any]    							=      (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						lowerCAmelCase__      :		Any    							=      num_heads
						lowerCAmelCase__      :		Optional[int]    							=      relative_attention_num_buckets
						lowerCAmelCase__      :		Union[str, Any]    							=      relative_attention_max_distance
						lowerCAmelCase__      :		List[Any]    							=      dropout_rate
						lowerCAmelCase__      :		str    							=      layer_norm_epsilon
						lowerCAmelCase__      :		int    							=      initializer_factor
						lowerCAmelCase__      :		Dict    							=      feed_forward_proj
						lowerCAmelCase__      :		int    							=      use_cache

						lowerCAmelCase__      :		int    							=      self.feed_forward_proj.split('''-'''						)
						lowerCAmelCase__      :		str    							=      act_info[-1]
						lowerCAmelCase__      :		Tuple    							=      act_info[0] == '''gated'''

						if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
							raise ValueError(
							    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
							    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
							    '''\'gated-gelu\' or \'relu\''''						)

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__      :		Tuple    							=      '''gelu_new'''

						super().__init__(
						    pad_token_id=lowercase_  ,eos_token_id=lowercase_  ,is_encoder_decoder=lowercase_  ,**lowercase_  ,)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		int    							=      {
						    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
						    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
						}
						if self.use_past:
							lowerCAmelCase__      :		Union[str, Any]    							=      '''past_encoder_sequence + sequence'''
							lowerCAmelCase__      :		List[Any]    							=      {0: '''batch'''}
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''decoder_sequence'''}
							lowerCAmelCase__      :		Any    							=      {0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
							self.fill_with_past_key_values_(lowercase_  ,direction='''inputs'''						)

						return common_inputs







					@property
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						return 1_3




