






'''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    a__				(					self     :     List[Any]     )							->							int:

     '''simple docstring'''




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

    def    a__				(					self     :     Optional[Any]				,     **__lowerCamelCase     :     Optional[Any]     )							->							Tuple:

     '''simple docstring'''




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

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

     '''simple docstring'''




     shutil.rmtree(self.tmpdirname     )

    def    a__				(					self     :     Dict     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     self.get_tokenizer()

     lowerCamelCase__  =     BarkProcessor(tokenizer=__lowerCamelCase     )

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

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

    @slow
    def    a__				(					self     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     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				,     )

     lowerCamelCase__  =     self.get_tokenizer(bos_token="(BOS)"				,     eos_token="(EOS)"     )

     lowerCamelCase__  =     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     :     Union[str, Any]     )							->							Any:

     '''simple docstring'''




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

     lowerCamelCase__  =     35
     lowerCamelCase__  =     2
     lowerCamelCase__  =     8

     lowerCamelCase__  =     {
         "semantic_prompt": np.ones(__lowerCamelCase     ),
         "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)     ),
         "fine_prompt": np.ones((nb_codebooks_total, seq_len)     ),
     }

     # test providing already loaded voice_preset
     lowerCamelCase__  =     processor(text=self.input_string				,     voice_preset=__lowerCamelCase     )

     lowerCamelCase__  =     inputs["history_prompt"]
     for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(__lowerCamelCase				,     np.array([]     )     ).tolist()     )

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

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

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




    def    a__				(					self     :     Dict     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     self.get_tokenizer()

     lowerCamelCase__  =     BarkProcessor(tokenizer=__lowerCamelCase     )

     lowerCamelCase__  =     processor(text=self.input_string     )

     lowerCamelCase__  =     tokenizer(
         self.input_string				,     padding="max_length"				,     max_length=256				,     add_special_tokens=__lowerCamelCase				,     return_attention_mask=__lowerCamelCase				,     return_token_type_ids=__lowerCamelCase				,     )

     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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A							:							Union[str, Any]  							=		{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Optional[Any]  							=		["""LayoutLMv2TokenizerFast"""]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							List[Any]  							=		["""LayoutLMv2FeatureExtractor"""]
 __A							:							List[Any]  							=		["""LayoutLMv2ImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Any  							=		[
     """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """LayoutLMv2ForQuestionAnswering""",
     """LayoutLMv2ForSequenceClassification""",
     """LayoutLMv2ForTokenClassification""",
     """LayoutLMv2Layer""",
     """LayoutLMv2Model""",
     """LayoutLMv2PreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 __A							:							Tuple  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

