from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase					      =							{
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase					      =							[
         'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RobertaPreLayerNormForCausalLM',
         'RobertaPreLayerNormForMaskedLM',
         'RobertaPreLayerNormForMultipleChoice',
         'RobertaPreLayerNormForQuestionAnswering',
         'RobertaPreLayerNormForSequenceClassification',
         'RobertaPreLayerNormForTokenClassification',
         'RobertaPreLayerNormModel',
         'RobertaPreLayerNormPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase					      =							[
         'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFRobertaPreLayerNormForCausalLM',
         'TFRobertaPreLayerNormForMaskedLM',
         'TFRobertaPreLayerNormForMultipleChoice',
         'TFRobertaPreLayerNormForQuestionAnswering',
         'TFRobertaPreLayerNormForSequenceClassification',
         'TFRobertaPreLayerNormForTokenClassification',
         'TFRobertaPreLayerNormMainLayer',
         'TFRobertaPreLayerNormModel',
         'TFRobertaPreLayerNormPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase					      =							[
         'FlaxRobertaPreLayerNormForCausalLM',
         'FlaxRobertaPreLayerNormForMaskedLM',
         'FlaxRobertaPreLayerNormForMultipleChoice',
         'FlaxRobertaPreLayerNormForQuestionAnswering',
         'FlaxRobertaPreLayerNormForSequenceClassification',
         'FlaxRobertaPreLayerNormForTokenClassification',
         'FlaxRobertaPreLayerNormModel',
         'FlaxRobertaPreLayerNormPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_roberta_prelayernorm import (
         ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
         RobertaPreLayerNormConfig,
         RobertaPreLayerNormOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta_prelayernorm import (
              ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaPreLayerNormForCausalLM,
              RobertaPreLayerNormForMaskedLM,
              RobertaPreLayerNormForMultipleChoice,
              RobertaPreLayerNormForQuestionAnswering,
              RobertaPreLayerNormForSequenceClassification,
              RobertaPreLayerNormForTokenClassification,
              RobertaPreLayerNormModel,
              RobertaPreLayerNormPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta_prelayernorm import (
              TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaPreLayerNormForCausalLM,
              TFRobertaPreLayerNormForMaskedLM,
              TFRobertaPreLayerNormForMultipleChoice,
              TFRobertaPreLayerNormForQuestionAnswering,
              TFRobertaPreLayerNormForSequenceClassification,
              TFRobertaPreLayerNormForTokenClassification,
              TFRobertaPreLayerNormMainLayer,
              TFRobertaPreLayerNormModel,
              TFRobertaPreLayerNormPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta_prelayernorm import (
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase					      =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''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()  )


