


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 PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					"""▁"""

UpperCAmelCase    =					{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

UpperCAmelCase    =					{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

UpperCAmelCase    =					{
    """facebook/s2t-small-librispeech-asr""": 1024,
}

UpperCAmelCase    =					["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

UpperCAmelCase    =					{"""mustc""": MUSTC_LANGS}







class lowerCAmelCase_		(				lowerCamelCase__ ):

	'''simple docstring'''

	__snake_case				=						VOCAB_FILES_NAMES
	__snake_case				=						PRETRAINED_VOCAB_FILES_MAP
	__snake_case				=						MAX_MODEL_INPUT_SIZES
	__snake_case				=						["input_ids", "attention_mask"]

	__snake_case				=						[]



	def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase="<s>"	,   _UpperCAmelCase="</s>"	,   _UpperCAmelCase="<pad>"	,   _UpperCAmelCase="<unk>"	,   _UpperCAmelCase=False	,   _UpperCAmelCase=False	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase = None	,   **_UpperCAmelCase	,   ):
							snake_case_ =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_UpperCAmelCase	,   eos_token=_UpperCAmelCase	,   unk_token=_UpperCAmelCase	,   pad_token=_UpperCAmelCase	,   do_upper_case=_UpperCAmelCase	,   do_lower_case=_UpperCAmelCase	,   tgt_lang=_UpperCAmelCase	,   lang_codes=_UpperCAmelCase	,   sp_model_kwargs=self.sp_model_kwargs	,   **_UpperCAmelCase	,   )
							snake_case_ =   do_upper_case
							snake_case_ =   do_lower_case

							snake_case_ =   load_json(_UpperCAmelCase      )
							snake_case_ =   {v: k for k, v in self.encoder.items()}
							snake_case_ =   spm_file
							snake_case_ =   load_spm(_UpperCAmelCase	,   self.sp_model_kwargs      )

							if lang_codes is not None:
													snake_case_ =   lang_codes
													snake_case_ =   LANGUAGES[lang_codes]
													snake_case_ =   [F'''<lang:{lang}>''' for lang in self.langs]
													snake_case_ =   {lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''      ) for lang in self.langs}

													snake_case_ =   self.lang_tokens
													snake_case_ =   tgt_lang if tgt_lang is not None else self.langs[0]

													self.set_tgt_lang_special_tokens(self._tgt_lang      )
							else:
													snake_case_ =   {}



	@property
	def 		UpperCamelCase__  (			self      ):
							return len(self.encoder      )



	@property
	def 		UpperCamelCase__  (			self      ):
							return self._tgt_lang



	@tgt_lang.setter
	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
							snake_case_ =   new_tgt_lang
							self.set_tgt_lang_special_tokens(_UpperCAmelCase      )



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
							snake_case_ =   self.lang_code_to_id[tgt_lang]
							snake_case_ =   [lang_code_id]



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
							return self.sp_model.encode(_UpperCAmelCase	,   out_type=_UpperCAmelCase      )



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
							return self.encoder.get(_UpperCAmelCase	,   self.encoder[self.unk_token]      )



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
							return self.decoder.get(_UpperCAmelCase	,   self.unk_token      )



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
							snake_case_ =   []
							snake_case_ =   ''''''
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			snake_case_ =   self.sp_model.decode(_UpperCAmelCase      )
																			out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																			snake_case_ =   []
													else:
																			current_sub_tokens.append(_UpperCAmelCase      )
							snake_case_ =   self.sp_model.decode(_UpperCAmelCase      )
							out_string += decoded.upper() if self.do_upper_case else decoded
							return out_string.strip()



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=None      ):
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + [self.eos_token_id]
							# 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.eos_token_id]



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None	,   _UpperCAmelCase = False      ):

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

							snake_case_ =   [1] * len(self.prefix_tokens      )
							snake_case_ =   [1]
							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      ):
							snake_case_ =   self.encoder.copy()
							vocab.update(self.added_tokens_encoder      )
							return vocab



	def __getstate__(			self      ):
							snake_case_ =   self.__dict__.copy()
							snake_case_ =   None
							return state



	def __setstate__(			self	,   _UpperCAmelCase      ):
							snake_case_ =   d

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

							snake_case_ =   load_spm(self.spm_file	,   self.sp_model_kwargs      )





	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
							snake_case_ =   Path(_UpperCAmelCase      )
							assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
							snake_case_ =   save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
							)
							snake_case_ =   save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
							)

							save_json(self.encoder	,   _UpperCAmelCase      )

							if os.path.abspath(self.spm_file      ) != os.path.abspath(_UpperCAmelCase      ) and os.path.isfile(self.spm_file      ):
													copyfile(self.spm_file	,   _UpperCAmelCase      )
							elif not os.path.isfile(self.spm_file      ):
													with open(_UpperCAmelCase	,   '''wb'''      ) as fi:
																			snake_case_ =   self.sp_model.serialized_model_proto()
																			fi.write(_UpperCAmelCase      )

							return (str(_UpperCAmelCase      ), str(_UpperCAmelCase      ))






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						sentencepiece.SentencePieceProcessor:




						"""simple docstring"""

						snake_case_ =   sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE      )
						spm.Load(str(SCREAMING_SNAKE_CASE      )      )
						return spm






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Union[Dict, List]:




						"""simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						None:




						"""simple docstring"""

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



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=2	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase="cls"	,   _UpperCAmelCase=False	,   _UpperCAmelCase=True	,   **_UpperCAmelCase	,   ):
       super().__init__(pad_token_id=_UpperCAmelCase	,   bos_token_id=_UpperCAmelCase	,   eos_token_id=_UpperCAmelCase	,   **_UpperCAmelCase      )
       snake_case_ =   project_dim
       snake_case_ =   pooler_fn
       snake_case_ =   learn_encoder
       snake_case_ =   use_attention_mask







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						[R"pooler", R"logit_scale"]
 __snake_case				=						[R"position_ids", R"predictions.decoder.bias"]
 __snake_case				=						"roberta"
 __snake_case				=						RobertaSeriesConfig



 def __init__(			self	,   _UpperCAmelCase      ):
       super().__init__(_UpperCAmelCase      )
       snake_case_ =   XLMRobertaModel(_UpperCAmelCase      )
       snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
       snake_case_ =   getattr(_UpperCAmelCase	,   '''has_pre_transformation'''	,   _UpperCAmelCase      )
       if self.has_pre_transformation:
             snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
             snake_case_ =   nn.LayerNorm(config.hidden_size	,   eps=config.layer_norm_eps      )
       self.post_init()





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):
       snake_case_ =   return_dict if return_dict is not None else self.config.use_return_dict

       snake_case_ =   self.base_model(
           input_ids=_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   position_ids=_UpperCAmelCase	,   head_mask=_UpperCAmelCase	,   inputs_embeds=_UpperCAmelCase	,   encoder_hidden_states=_UpperCAmelCase	,   encoder_attention_mask=_UpperCAmelCase	,   output_attentions=_UpperCAmelCase	,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,   return_dict=_UpperCAmelCase	,   )

       if self.has_pre_transformation:
             snake_case_ =   outputs['''hidden_states'''][-2]
             snake_case_ =   self.pre_LN(_UpperCAmelCase      )
             snake_case_ =   self.transformation_pre(_UpperCAmelCase      )

             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )
       else:
             snake_case_ =   self.transformation(outputs.last_hidden_state      )
             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )