'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase		:				List[str]	       =		logging.get_logger(__name__)

_lowercase		:				int	       =		{
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}





class 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase):
   UpperCamelCase__       = '''nat'''

   UpperCamelCase__       = {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }







   def __init__(			self       :						Optional[int]       ,    lowercase_       :						Optional[Any]=4       ,    lowercase_       :						Any=3       ,    lowercase_       :						List[str]=64       ,    lowercase_       :						List[str]=[3, 4, 6, 5]       ,    lowercase_       :						Optional[int]=[2, 4, 8, 16]       ,    lowercase_       :						List[str]=7       ,    lowercase_       :						int=3.0       ,    lowercase_       :						Tuple=True       ,    lowercase_       :						Dict=0.0       ,    lowercase_       :						Union[str, Any]=0.0       ,    lowercase_       :						int=0.1       ,    lowercase_       :						str="gelu"       ,    lowercase_       :						Union[str, Any]=0.02       ,    lowercase_       :						List[str]=1E-5       ,    lowercase_       :						int=0.0       ,    lowercase_       :						int=None       ,    lowercase_       :						Union[str, Any]=None       ,    **lowercase_       :						str       ,    ):
          super().__init__(**lowercase_					)

          lowercase_      :    List[Any]       		= patch_size
          lowercase_      :    Optional[Any]       		= num_channels
          lowercase_      :    Any       		= embed_dim
          lowercase_      :    Any       		= depths
          lowercase_      :    Dict       		= len(lowercase_					)
          lowercase_      :    Optional[Any]       		= num_heads
          lowercase_      :    Union[str, Any]       		= kernel_size
          lowercase_      :    Any       		= mlp_ratio
          lowercase_      :    List[str]       		= qkv_bias
          lowercase_      :    Dict       		= hidden_dropout_prob
          lowercase_      :    List[Any]       		= attention_probs_dropout_prob
          lowercase_      :    int       		= drop_path_rate
          lowercase_      :    Optional[int]       		= hidden_act
          lowercase_      :    List[str]       		= layer_norm_eps
          lowercase_      :    Dict       		= initializer_range
          # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowercase_      :    str       		= int(embed_dim * 2 ** (len(lowercase_					) - 1)					)
          lowercase_      :    Optional[Any]       		= layer_scale_init_value
          lowercase_      :    Union[str, Any]       		= ["""stem"""] + [f'''stage{idx}''' for idx in range(1       ,    len(lowercase_					) + 1					)]
          lowercase_      :    int       		= get_aligned_output_features_output_indices(
              out_features=lowercase_       ,    out_indices=lowercase_       ,    stage_names=self.stage_names					)






'''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 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    List[Any]       		= """ylacombe/bark-small"""
										lowercase_      :    List[str]       		= tempfile.mkdtemp()
										lowercase_      :    Tuple       		= """en_speaker_1"""
										lowercase_      :    Union[str, Any]       		= """This is a test string"""
										lowercase_      :    int       		= """speaker_embeddings_path.json"""
										lowercase_      :    Any       		= """speaker_embeddings"""







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    **lowercase_       :						Optional[int]					):
										return AutoTokenizer.from_pretrained(self.checkpoint       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										shutil.rmtree(self.tmpdirname					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Any       		= self.get_tokenizer()

										lowercase_      :    Optional[Any]       		= BarkProcessor(tokenizer=lowercase_					)

										processor.save_pretrained(self.tmpdirname					)
										lowercase_      :    Union[str, Any]       		= BarkProcessor.from_pretrained(self.tmpdirname					)

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







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Any       		= 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       ,    )

										lowercase_      :    List[str]       		= self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""					)

										lowercase_      :    Optional[Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )

										lowercase_      :    Optional[int]       		= 35
										lowercase_      :    int       		= 2
										lowercase_      :    Union[str, Any]       		= 8

										lowercase_      :    Union[str, Any]       		= {
										    """semantic_prompt""": np.ones(lowercase_					),
										    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
										    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
										}

										# test providing already loaded voice_preset
										lowercase_      :    str       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)

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

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

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

										# test loading voice preset from the hub
										lowercase_      :    Union[str, Any]       		= processor(text=self.input_string       ,    voice_preset=self.voice_preset					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[str]       		= self.get_tokenizer()

										lowercase_      :    int       		= BarkProcessor(tokenizer=lowercase_					)

										lowercase_      :    Any       		= processor(text=self.input_string					)

										lowercase_      :    List[str]       		= tokenizer(
										    self.input_string       ,    padding="""max_length"""       ,    max_length=256       ,    add_special_tokens=lowercase_       ,    return_attention_mask=lowercase_       ,    return_token_type_ids=lowercase_       ,    )

										for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key].squeeze().tolist()					)





