
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


A     :	Any    = logging.get_logger(__name__)

A     :	Optional[Any]    = '▁'

A     :	List[str]    = {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

A     :	Union[str, Any]    = {
    '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'
        )
    },
}

A     :	Tuple    = {
    'facebook/s2t-small-librispeech-asr': 1_0_2_4,
}

A     :	Union[str, Any]    = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

A     :	str    = {'mustc': MUSTC_LANGS}







class 			__A( SCREAMING_SNAKE_CASE_			):
				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		,     _snake_case		,     _snake_case		,     _snake_case="<s>"		,     _snake_case="</s>"		,     _snake_case="<pad>"		,     _snake_case="<unk>"		,     _snake_case=False		,     _snake_case=False		,     _snake_case=None		,     _snake_case=None		,     _snake_case = None		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								__a										=    {} 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		,     )
								__a										=    do_upper_case
								__a										=    do_lower_case

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

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

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

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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return len(self.encoder				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								return self._tgt_lang




				@tgt_lang.setter
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    new_tgt_lang
								self.set_tgt_lang_special_tokens(__UpperCAmelCase				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    self.lang_code_to_id[tgt_lang]
								__a										=    [lang_code_id]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								return self.encoder.get(__UpperCAmelCase		,     self.encoder[self.unk_token]				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								return self.decoder.get(__UpperCAmelCase		,     self.unk_token				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=None				)    ->      List[int]:


								'''simple docstring'''




								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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = False				)    ->      List[int]:


								'''simple docstring'''




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

								__a										=    [1] * len(self.prefix_tokens				)
								__a										=    [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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.encoder.copy()
								vocab.update(self.added_tokens_encoder				)
								return vocab




				def __getstate__(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.__dict__.copy()
								__a										=    None
								return state




				def __setstate__(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    d

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Tuple[str]:


								'''simple docstring'''




								__a										=    Path(__UpperCAmelCase				)
								assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
								__a										=    save_dir / (
								    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
								)
								__a										=    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:
																__a										=    self.sp_model.serialized_model_proto()
																fi.write(__UpperCAmelCase				)

								return (str(__UpperCAmelCase				), str(__UpperCAmelCase				))



def     __lowerCAmelCase			(			a__						,    a__  )       ->  Any:
				__a										=    sentencepiece.SentencePieceProcessor(**a__  )
				spm.Load(str(a__  )  )
				return spm


def     __lowerCAmelCase			(			a__  )       ->  Optional[Any]:





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






def     __lowerCAmelCase			(			a__						,    a__  )       ->  Optional[Any]:





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




'''simple docstring'''





import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
 import tensorflow as tf

if is_torch_available():
 import torch


_lowerCAmelCase      						=	logging.get_logger(__name__)






class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None      )    ->		str:
      if not conversation_id:
          lowerCAmelCase__ :  List[str]							      =					uuid.uuida()
      if past_user_inputs is None:
          lowerCAmelCase__ :  List[Any]							      =					[]
      if generated_responses is None:
          lowerCAmelCase__ :  str							      =					[]

      lowerCAmelCase__ :  uuid.UUID							      =					conversation_id
      lowerCAmelCase__ :  List[str]							      =					past_user_inputs
      lowerCAmelCase__ :  List[str]							      =					generated_responses
      lowerCAmelCase__ :  Optional[str]							      =					text




  def __eq__(    self  ,__UpperCAmelCase      )    ->		Dict:
      if not isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          return False
      if self.uuid == other.uuid:
          return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = False      )    ->		Optional[Any]:
      if self.new_user_input:
          if overwrite:
              logger.warning(
                  F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
                  F"""with: \"{text}\"."""      )
              lowerCAmelCase__ :  Optional[int]							      =					text
          else:
              logger.warning(
                  F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
                  F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""      )
      else:
          lowerCAmelCase__ :  Optional[Any]							      =					text




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      if self.new_user_input:
          self.past_user_inputs.append(self.new_user_input      )
      lowerCAmelCase__ :  Union[str, Any]							      =					None




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      self.generated_responses.append(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      for user_input, generated_response in zip(self.past_user_inputs  ,self.generated_responses      ):
          yield True, user_input
          yield False, generated_response
      if self.new_user_input:
          yield True, self.new_user_input






  def __repr__(    self      )    ->		Tuple:
      lowerCAmelCase__ :  Tuple							      =					F"""Conversation id: {self.uuid} \n"""
      for is_user, text in self.iter_texts():
          lowerCAmelCase__ :  Any							      =					"""user""" if is_user else """bot"""
          output += F"""{name} >> {text} \n"""
      return output







@add_end_docstrings(
    SCREAMING_SNAKE_CASE_						, R'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''						, )
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,*__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		Tuple:
      super().__init__(*__UpperCAmelCase  ,**__UpperCAmelCase      )
      if self.tokenizer.pad_token_id is None:
          lowerCAmelCase__ :  Tuple							      =					self.tokenizer.eos_token




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[Any]							      =					{}
      lowerCAmelCase__ :  Optional[int]							      =					{}
      lowerCAmelCase__ :  List[str]							      =					{}

      if min_length_for_response is not None:
          lowerCAmelCase__ :  Any							      =					min_length_for_response
      if minimum_tokens is not None:
          lowerCAmelCase__ :  Optional[int]							      =					minimum_tokens

      if "max_length" in generate_kwargs:
          lowerCAmelCase__ :  Optional[Any]							      =					generate_kwargs["""max_length"""]
          # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
          lowerCAmelCase__ :  int							      =					clean_up_tokenization_spaces

      if generate_kwargs:
          forward_params.update(__UpperCAmelCase      )
      return preprocess_params, forward_params, postprocess_params




  def __call__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=0  ,**__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					super().__call__(__UpperCAmelCase  ,num_workers=__UpperCAmelCase  ,**__UpperCAmelCase      )
      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) and len(__UpperCAmelCase      ) == 1:
          return outputs[0]
      return outputs




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=32      )    ->		Dict[str, Any]:
      if not isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""      )
      if conversation.new_user_input is None:
          raise ValueError(
              F"""Conversation with UUID {type(conversation.uuid      )} does not contain new user input to process. """
              """Add user inputs with the conversation's `add_user_input` method"""      )
      if hasattr(self.tokenizer  ,"""_build_conversation_input_ids"""      ):
          lowerCAmelCase__ :  str							      =					self.tokenizer._build_conversation_input_ids(__UpperCAmelCase      )
      else:
          # If the tokenizer cannot handle conversations, we default to only the old version
          lowerCAmelCase__ :  List[Any]							      =					self._legacy_parse_and_tokenize(__UpperCAmelCase      )

      if self.framework == "pt":
          lowerCAmelCase__ :  List[Any]							      =					torch.LongTensor([input_ids]      )
      elif self.framework == "tf":
          lowerCAmelCase__ :  Dict							      =					tf.constant([input_ids]      )
      return {"input_ids": input_ids, "conversation": conversation}




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=10  ,**__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Optional[Any]							      =					generate_kwargs.get("""max_length"""  ,self.model.config.max_length      )

      lowerCAmelCase__ :  Optional[Any]							      =					model_inputs["""input_ids"""].shape[1]
      if max_length - minimum_tokens < n:
          logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""      )
          lowerCAmelCase__ :  str							      =					max_length - minimum_tokens
          lowerCAmelCase__ :  Union[str, Any]							      =					model_inputs["""input_ids"""][:, -trim:]
          if "attention_mask" in model_inputs:
              lowerCAmelCase__ :  Tuple							      =					model_inputs["""attention_mask"""][:, -trim:]
      lowerCAmelCase__ :  str							      =					model_inputs.pop("""conversation"""      )
      lowerCAmelCase__ :  Union[str, Any]							      =					max_length
      lowerCAmelCase__ :  Any							      =					self.model.generate(**__UpperCAmelCase  ,**__UpperCAmelCase      )
      if self.model.config.is_encoder_decoder:
          lowerCAmelCase__ :  int							      =					1
      else:
          lowerCAmelCase__ :  int							      =					n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=True      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					model_outputs["""output_ids"""]
      lowerCAmelCase__ :  Tuple							      =					self.tokenizer.decode(
          output_ids[0]  ,skip_special_tokens=__UpperCAmelCase  ,clean_up_tokenization_spaces=__UpperCAmelCase  ,)
      lowerCAmelCase__ :  Union[str, Any]							      =					model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__UpperCAmelCase      )
      return conversation






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Dict							      =					self.tokenizer.eos_token_id
      lowerCAmelCase__ :  int							      =					[]
      for is_user, text in conversation.iter_texts():
          if eos_token_id is not None:
              input_ids.extend(self.tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      ) + [eos_token_id]      )
          else:
              input_ids.extend(self.tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      )      )

      if len(__UpperCAmelCase      ) > self.tokenizer.model_max_length:
          lowerCAmelCase__ :  Optional[Any]							      =					input_ids[-self.tokenizer.model_max_length :]
      return input_ids


