

'''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 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     List[str]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='ylacombe/bark-small'
       _SCREAMING_SNAKE_CASE							   =tempfile.mkdtemp()
       _SCREAMING_SNAKE_CASE							   ='en_speaker_1'
       _SCREAMING_SNAKE_CASE							   ='This is a test string'
       _SCREAMING_SNAKE_CASE							   ='speaker_embeddings_path.json'
       _SCREAMING_SNAKE_CASE							   ='speaker_embeddings'





  def 						A  (		self				:     Union[str, Any]							, **_a				:     int			)     ->   Tuple:





       '''simple docstring'''






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





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       shutil.rmtree(self.tmpdirname			)





  def 						A  (		self				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.get_tokenizer()

       _SCREAMING_SNAKE_CASE							   =BarkProcessor(tokenizer=_a			)

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

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





  @slow
  def 						A  (		self				:     int			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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							, )

       _SCREAMING_SNAKE_CASE							   =self.get_tokenizer(bos_token='(BOS)'							, eos_token='(EOS)'			)

       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     int			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							, )

       _SCREAMING_SNAKE_CASE							   =35
       _SCREAMING_SNAKE_CASE							   =2
       _SCREAMING_SNAKE_CASE							   =8

       _SCREAMING_SNAKE_CASE							   ={
           '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
       _SCREAMING_SNAKE_CASE							   =processor(text=self.input_string							, voice_preset=_a			)

       _SCREAMING_SNAKE_CASE							   =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
       _SCREAMING_SNAKE_CASE							   =os.path.join(self.tmpdirname							, 'file.npz'			)
       np.savez(_a							, **_a			)
       _SCREAMING_SNAKE_CASE							   =processor(text=self.input_string							, voice_preset=_a			)
       _SCREAMING_SNAKE_CASE							   =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
       _SCREAMING_SNAKE_CASE							   =processor(text=self.input_string							, voice_preset=self.voice_preset			)





  def 						A  (		self				:     str			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.get_tokenizer()

       _SCREAMING_SNAKE_CASE							   =BarkProcessor(tokenizer=_a			)

       _SCREAMING_SNAKE_CASE							   =processor(text=self.input_string			)

       _SCREAMING_SNAKE_CASE							   =tokenizer(
           self.input_string							, padding='max_length'							, max_length=256							, 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()			)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
