



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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"ylacombe/bark-small"
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	tempfile.mkdtemp()
      __SCREAMING_SNAKE_CASE       :    str       =	"en_speaker_1"
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"This is a test string"
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"speaker_embeddings_path.json"
      __SCREAMING_SNAKE_CASE       :    Any       =	"speaker_embeddings"





     def 	__snake_case    (       self :							Optional[Any]     ,  **UpperCamelCase :							Union[str, Any]				)->Tuple:
      return AutoTokenizer.from_pretrained(self.checkpoint     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							List[str]				)->str:
      shutil.rmtree(self.tmpdirname				)





     def 	__snake_case    (       self :							Optional[Any]				)->str:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_tokenizer()

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

      processor.save_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	BarkProcessor.from_pretrained(self.tmpdirname				)

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





     @slow
     def 	__snake_case    (       self :							str				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    str       =	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       :    int       =	self.get_tokenizer(bos_token="(BOS)"     ,  eos_token="(EOS)"				)

      __SCREAMING_SNAKE_CASE       :    int       =	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 :							List[str]				)->int:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint     ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,  )

      __SCREAMING_SNAKE_CASE       :    Dict       =	3_5
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	2
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	8

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{
          "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
      __SCREAMING_SNAKE_CASE       :    Dict       =	processor(text=self.input_string     ,  voice_preset=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    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
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	os.path.join(self.tmpdirname     ,  "file.npz"				)
      np.savez(UpperCamelCase     ,  **UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	processor(text=self.input_string     ,  voice_preset=UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	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
      __SCREAMING_SNAKE_CASE       :    Dict       =	processor(text=self.input_string     ,  voice_preset=self.voice_preset				)






     def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    str       =	BarkProcessor(tokenizer=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	processor(text=self.input_string				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	tokenizer(
          self.input_string     ,  padding="max_length"     ,  max_length=2_5_6     ,  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()				)











import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_lowerCamelCase						    =  logging.get_logger(__name__)

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							int     ,  *UpperCamelCase :							Optional[int]     ,  **UpperCamelCase :							Optional[int]				)->None:
      warnings.warn(
          "The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use LayoutLMv2ImageProcessor instead."     ,  UpperCamelCase     ,  )
      super().__init__(*UpperCamelCase     ,  **UpperCamelCase				)






