
'''simple docstring'''






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCAmelCase_							: str =							logging.get_logger(__name__)


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

UpperCAmelCase_							: Union[str, Any] =							{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_							: List[str] =							{
    'allenai/led-base-16384': 1_6384,
}

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		VOCAB_FILES_NAMES
    lowerCAmelCase_					     =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_					     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_					     =		LEDTokenizer
    lowerCAmelCase_					     =		['input_ids', 'attention_mask']



    def __init__(						self   :							Union[str, Any],__A   :							List[Any]=None,__A   :							str=None,__A   :							str=None,__A   :							Optional[int]="replace",__A   :							Union[str, Any]="<s>",__A   :							Union[str, Any]="</s>",__A   :							Any="</s>",__A   :							Optional[int]="<s>",__A   :							List[str]="<unk>",__A   :							str="<pad>",__A   :							Tuple="<mask>",__A   :							Union[str, Any]=False,__A   :							Optional[int]=True,**__A   :							Optional[int],):
          super().__init__(
              __A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)

          _lowerCamelCase					: List[str]		    = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
          if pre_tok_state.get("add_prefix_space",__A  ) != add_prefix_space:
                _lowerCamelCase					: str		    = getattr(__A,pre_tok_state.pop("type"  )  )
                _lowerCamelCase					: List[Any]		    = add_prefix_space
                _lowerCamelCase					: Tuple		    = pre_tok_class(**__A  )

          _lowerCamelCase					: Optional[int]		    = add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _lowerCamelCase					: List[str]		    = "post_processor"
          _lowerCamelCase					: int		    = getattr(self.backend_tokenizer,__A,__A  )
          if tokenizer_component_instance:
                _lowerCamelCase					: Tuple		    = 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:
                      _lowerCamelCase					: str		    = tuple(state["sep"]  )
                if "cls" in state:
                      _lowerCamelCase					: List[str]		    = tuple(state["cls"]  )

                _lowerCamelCase					: Dict		    = False

                if state.get("add_prefix_space",__A  ) != add_prefix_space:
                      _lowerCamelCase					: List[str]		    = add_prefix_space
                      _lowerCamelCase					: List[Any]		    = True

                if state.get("trim_offsets",__A  ) != trim_offsets:
                      _lowerCamelCase					: List[str]		    = trim_offsets
                      _lowerCamelCase					: List[str]		    = True

                if changes_to_apply:
                      _lowerCamelCase					: Tuple		    = getattr(__A,state.pop("type"  )  )
                      _lowerCamelCase					: Any		    = component_class(**__A  )
                      setattr(self.backend_tokenizer,__A,__A  )



    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def 	lowerCamelCase_	(						self   :							str  ):
          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 	lowerCamelCase_	(						self   :							List[str],__A   :							str  ):
          _lowerCamelCase					: Optional[Any]		    = AddedToken(__A,lstrip=__A,rstrip=__A  ) if isinstance(__A,__A  ) else value
          _lowerCamelCase					: str		    = value



    def 	lowerCamelCase_	(						self   :							List[str],*__A   :							List[Any],**__A   :							int  ):
          _lowerCamelCase					: List[str]		    = kwargs.get("is_split_into_words",__A  )

          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(*__A,**__A  )



    def 	lowerCamelCase_	(						self   :							Optional[int],*__A   :							Optional[Any],**__A   :							Union[str, Any]  ):
          _lowerCamelCase					: List[Any]		    = kwargs.get("is_split_into_words",__A  )

          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(*__A,**__A  )



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							str,__A   :							Optional[str] = None  ):
          _lowerCamelCase					: List[str]		    = self._tokenizer.model.save(__A,name=__A  )
          return tuple(__A  )



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							Optional[Any],__A   :							List[str]=None  ):
          _lowerCamelCase					: Optional[Any]		    = [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 	lowerCamelCase_	(						self   :							Dict,__A   :							List[int],__A   :							Optional[List[int]] = None  ):
          _lowerCamelCase					: Tuple		    = [self.sep_token_id]
          _lowerCamelCase					: Union[str, Any]		    = [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 	lowerCamelCase_	(						self   :							Any,__A   :							Union[Dict[str, EncodedInput], BatchEncoding],__A   :							Optional[int] = None,__A   :							PaddingStrategy = PaddingStrategy.DO_NOT_PAD,__A   :							Optional[int] = None,__A   :							Optional[bool] = None,):
          _lowerCamelCase					: List[str]		    = super()._pad(
              encoded_inputs=__A,max_length=__A,padding_strategy=__A,pad_to_multiple_of=__A,return_attention_mask=__A,)

          # Load from model defaults
          if return_attention_mask is None:
                _lowerCamelCase					: Any		    = "attention_mask" in self.model_input_names

          if return_attention_mask and "global_attention_mask" in encoded_inputs:
                _lowerCamelCase					: Union[str, Any]		    = encoded_inputs[self.model_input_names[0]]
                # `global_attention_mask` need to have the same length as other (sequential) inputs.
                _lowerCamelCase					: Optional[Any]		    = len(encoded_inputs["global_attention_mask"]  ) != len(__A  )

                if needs_to_be_padded:
                      _lowerCamelCase					: str		    = len(__A  ) - len(encoded_inputs["global_attention_mask"]  )

                      if self.padding_side == "right":
                            # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                            _lowerCamelCase					: Tuple		    = (
                                encoded_inputs["global_attention_mask"] + [-1] * difference
                            )
                      elif self.padding_side == "left":
                            _lowerCamelCase					: int		    = [-1] * difference + encoded_inputs[
                                "global_attention_mask"
                            ]
                      else:
                            raise ValueError("Invalid padding strategy:" + str(self.padding_side  )  )

          return encoded_inputs


'''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 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						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    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        '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    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

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

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





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

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


