




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


_lowerCamelCase	:		Dict							       =   logging.get_logger(__name__)


_lowerCamelCase	:		List[Any]							       =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
_lowerCamelCase	:		Any							       =   {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

_lowerCamelCase	:		Optional[int]							       =   {
    """RUCAIBox/mvp""": 1024,
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      ['''input_ids''', '''attention_mask''']
    UpperCAmelCase__							  =      MvpTokenizer


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Optional[Any]=None					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	str=None					, UpperCAmelCase__      :	Union[str, Any]="replace"					, UpperCAmelCase__      :	str="<s>"					, UpperCAmelCase__      :	Union[str, Any]="</s>"					, UpperCAmelCase__      :	Dict="</s>"					, UpperCAmelCase__      :	Dict="<s>"					, UpperCAmelCase__      :	Tuple="<unk>"					, UpperCAmelCase__      :	List[Any]="<pad>"					, UpperCAmelCase__      :	Dict="<mask>"					, UpperCAmelCase__      :	Optional[int]=False					, UpperCAmelCase__      :	List[Any]=True					, **UpperCAmelCase__      :	Tuple					, )						->int:





           '''simple docstring'''

           super().__init__(
               UpperCAmelCase__					, UpperCAmelCase__					, tokenizer_file=UpperCAmelCase__					, errors=UpperCAmelCase__					, bos_token=UpperCAmelCase__					, eos_token=UpperCAmelCase__					, sep_token=UpperCAmelCase__					, cls_token=UpperCAmelCase__					, unk_token=UpperCAmelCase__					, pad_token=UpperCAmelCase__					, mask_token=UpperCAmelCase__					, add_prefix_space=UpperCAmelCase__					, trim_offsets=UpperCAmelCase__					, **UpperCAmelCase__					, )

           A__       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('''add_prefix_space'''					, UpperCAmelCase__) != add_prefix_space:
                  A__       =    getattr(UpperCAmelCase__					, pre_tok_state.pop('''type'''))
                  A__       =    add_prefix_space
                  A__       =    pre_tok_class(**UpperCAmelCase__)

           A__       =    add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           A__       =    '''post_processor'''
           A__       =    getattr(self.backend_tokenizer					, UpperCAmelCase__					, UpperCAmelCase__)
           if tokenizer_component_instance:
                  A__       =    json.loads(tokenizer_component_instance.__getstate__())

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                         A__       =    tuple(state['''sep'''])
                  if "cls" in state:
                         A__       =    tuple(state['''cls'''])

                  A__       =    False

                  if state.get('''add_prefix_space'''					, UpperCAmelCase__) != add_prefix_space:
                         A__       =    add_prefix_space
                         A__       =    True

                  if state.get('''trim_offsets'''					, UpperCAmelCase__) != trim_offsets:
                         A__       =    trim_offsets
                         A__       =    True

                  if changes_to_apply:
                         A__       =    getattr(UpperCAmelCase__					, state.pop('''type'''))
                         A__       =    component_class(**UpperCAmelCase__)
                         setattr(self.backend_tokenizer					, UpperCAmelCase__					, UpperCAmelCase__)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->str:





           '''simple docstring'''

           if self._mask_token is None:
                  if self.verbose:
                         logger.error('''Using mask_token, but it is not set yet.''')
                  return None
           return str(self._mask_token)


    @mask_token.setter
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	Optional[int])						->List[str]:





           '''simple docstring'''

           A__       =    AddedToken(UpperCAmelCase__					, lstrip=UpperCAmelCase__					, rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__					, UpperCAmelCase__) else value
           A__       =    value


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, *UpperCAmelCase__      :	Optional[int]					, **UpperCAmelCase__      :	Optional[int])						->BatchEncoding:





           '''simple docstring'''

           A__       =    kwargs.get('''is_split_into_words'''					, UpperCAmelCase__)

           if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                      '''to use it with pretokenized inputs.''')

           return super()._batch_encode_plus(*UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, *UpperCAmelCase__      :	Optional[int]					, **UpperCAmelCase__      :	Dict)						->BatchEncoding:





           '''simple docstring'''

           A__       =    kwargs.get('''is_split_into_words'''					, UpperCAmelCase__)

           if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                      '''to use it with pretokenized inputs.''')

           return super()._encode_plus(*UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           A__       =    self._tokenizer.model.save(UpperCAmelCase__					, name=UpperCAmelCase__)
           return tuple(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	Tuple=None)						->List[Any]:





           '''simple docstring'''

           A__       =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                  return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	Optional[List[int]] = None)						->List[int]:





           '''simple docstring'''

           A__       =    [self.sep_token_id]
           A__       =    [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]






"""simple docstring"""






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					"""ylacombe/bark-small"""
												__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()
												__UpperCAmelCase   :						Optional[Any]      							=					"""en_speaker_1"""
												__UpperCAmelCase   :						Union[str, Any]      							=					"""This is a test string"""
												__UpperCAmelCase   :						Dict      							=					"""speaker_embeddings_path.json"""
												__UpperCAmelCase   :						Any      							=					"""speaker_embeddings"""






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												return AutoTokenizer.from_pretrained(self.checkpoint ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												processor.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(self.tmpdirname  )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer.get_vocab()  )






					@slow
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )
												processor.save_pretrained(
												    self.tmpdirname ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       speaker_embeddings_directory=self.speaker_embeddings_directory ,       )

												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""  )

												__UpperCAmelCase   :						Any      							=					BarkProcessor.from_pretrained(
												    self.tmpdirname ,       self.speaker_embeddings_dict_path ,       bos_token="""(BOS)""" ,       eos_token="""(EOS)""" ,       )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )

												__UpperCAmelCase   :						List[str]      							=					35
												__UpperCAmelCase   :						Tuple      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					8

												__UpperCAmelCase   :						Optional[Any]      							=					{
												    """semantic_prompt""": np.ones(UpperCamelCase  ),
												    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
												    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
												}

												# test providing already loaded voice_preset
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					inputs["""history_prompt"""]
												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from npz file
												__UpperCAmelCase   :						Union[str, Any]      							=					os.path.join(self.tmpdirname ,       """file.npz"""  )
												np.savez(UpperCamelCase ,       **UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					inputs["""history_prompt"""]

												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from the hub
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=self.voice_preset  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												__UpperCAmelCase   :						List[str]      							=					processor(text=self.input_string  )

												__UpperCAmelCase   :						Tuple      							=					tokenizer(
												    self.input_string ,       padding="""max_length""" ,       max_length=256 ,       add_special_tokens=UpperCamelCase ,       return_attention_mask=UpperCamelCase ,       return_token_type_ids=UpperCamelCase ,       )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key] ,       encoded_processor[key].squeeze().tolist()  )
