


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 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							"""ylacombe/bark-small"""
      _a			      =							tempfile.mkdtemp()
      _a			      =							"""en_speaker_1"""
      _a			      =							"""This is a test string"""
      _a			      =							"""speaker_embeddings_path.json"""
      _a			      =							"""speaker_embeddings"""
 def 							__UpperCAmelCase		( self      ,				**__magic_name__						)					->							Optional[Any]:
      return AutoTokenizer.from_pretrained(self.checkpoint      ,				**__a						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      shutil.rmtree(self.tmpdirname						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							self.get_tokenizer()

      _a			      =							BarkProcessor(tokenizer=__a						)

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

      self.assertEqual(processor.tokenizer.get_vocab()      ,				tokenizer.get_vocab()						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							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      ,				)

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

      _a			      =							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 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint      ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,				)

      _a			      =							35
      _a			      =							2
      _a			      =							8

      _a			      =							{
          """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
      _a			      =							processor(text=self.input_string      ,				voice_preset=__a						)

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




 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							self.get_tokenizer()

      _a			      =							BarkProcessor(tokenizer=__a						)

      _a			      =							processor(text=self.input_string						)

      _a			      =							tokenizer(
          self.input_string      ,				padding='max_length'      ,				max_length=2_56      ,				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_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_					:    int          =    {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[Any]          =    ["ViTFeatureExtractor"]
 a_					:    Optional[int]          =    ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Optional[int]          =    [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Any          =    [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Dict          =    [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 a_					:    List[str]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

