

'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a					:			str							=    logging.get_logger(__name__)

_a					:			Dict							=    {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[Any]     	= "cvt"
  def __init__(  self ,			a_=3 ,			a_=[7, 3, 3] ,			a_=[4, 2, 2] ,			a_=[2, 1, 1] ,			a_=[6_4, 1_9_2, 3_8_4] ,			a_=[1, 3, 6] ,			a_=[1, 2, 1_0] ,			a_=[4.0, 4.0, 4.0] ,			a_=[0.0, 0.0, 0.0] ,			a_=[0.0, 0.0, 0.0] ,			a_=[0.0, 0.0, 0.1] ,			a_=[True, True, True] ,			a_=[False, False, True] ,			a_=["dw_bn", "dw_bn", "dw_bn"] ,			a_=[3, 3, 3] ,			a_=[1, 1, 1] ,			a_=[2, 2, 2] ,			a_=[1, 1, 1] ,			a_=[1, 1, 1] ,			a_=0.02 ,			a_=1E-12 ,			**a_ ,			)				->					Union[str, Any]:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						num_channels
    UpperCAmelCase    				=						patch_sizes
    UpperCAmelCase    				=						patch_stride
    UpperCAmelCase    				=						patch_padding
    UpperCAmelCase    				=						embed_dim
    UpperCAmelCase    				=						num_heads
    UpperCAmelCase    				=						depth
    UpperCAmelCase    				=						mlp_ratio
    UpperCAmelCase    				=						attention_drop_rate
    UpperCAmelCase    				=						drop_rate
    UpperCAmelCase    				=						drop_path_rate
    UpperCAmelCase    				=						qkv_bias
    UpperCAmelCase    				=						cls_token
    UpperCAmelCase    				=						qkv_projection_method
    UpperCAmelCase    				=						kernel_qkv
    UpperCAmelCase    				=						padding_kv
    UpperCAmelCase    				=						stride_kv
    UpperCAmelCase    				=						padding_q
    UpperCAmelCase    				=						stride_q
    UpperCAmelCase    				=						initializer_range
    UpperCAmelCase    				=						layer_norm_eps





'''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()  )


