




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 ):



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Union[str, Any]					,							**snake_case__	:   int  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								shutil.rmtree(self.tmpdirname  )



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							BarkProcessor(tokenizer=a_  )

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

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



						@slow
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							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	:   Any  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							35
								SCREAMING_SNAKE_CASE_					      =							2
								SCREAMING_SNAKE_CASE_					      =							8

								SCREAMING_SNAKE_CASE_					      =							{
								    '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
								SCREAMING_SNAKE_CASE_					      =							processor(text=self.input_string					,							voice_preset=a_  )

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






						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							BarkProcessor(tokenizer=a_  )

								SCREAMING_SNAKE_CASE_					      =							processor(text=self.input_string  )

								SCREAMING_SNAKE_CASE_					      =							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 ...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


