




import functools



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							      =						len(_lowerCAmelCase	)
 UpperCAmelCase__							      =						len(_lowerCAmelCase	)

 @functools.cache
 def min_distance(_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   int	) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCAmelCase__							      =						int(worda[indexa] != worda[indexa]	)  # current letters not identical
  return min(
      1 + min_distance(indexa + 1						,							_lowerCAmelCase	)						,							1 + min_distance(_lowerCAmelCase						,							indexa + 1	)						,							diff + min_distance(indexa + 1						,							indexa + 1	)						,							)

 return min_distance(0						,							0	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()











import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ASTForAudioClassification, ASTModel
  from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_torchaudio_available():
  import torchaudio

  from transformers import ASTFeatureExtractor
class 			_UpperCamelCase      :
      def __init__(			self   :Any ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Optional[int]=13 ,      lowerCamelCase   :Optional[Any]=2 ,      lowerCamelCase   :Any=24 ,      lowerCamelCase   :Union[str, Any]=16 ,      lowerCamelCase   :Any=True ,      lowerCamelCase   :int=True ,      lowerCamelCase   :Optional[Any]=32 ,      lowerCamelCase   :Union[str, Any]=5 ,      lowerCamelCase   :Tuple=4 ,      lowerCamelCase   :Optional[Any]=37 ,      lowerCamelCase   :Optional[Any]="gelu" ,      lowerCamelCase   :int=0.1 ,      lowerCamelCase   :Tuple=0.1 ,      lowerCamelCase   :List[str]=10 ,      lowerCamelCase   :Optional[Any]=0.02 ,      lowerCamelCase   :Optional[int]=None ,      lowerCamelCase   :Optional[Any]=2 ,      lowerCamelCase   :List[Any]=2 ,      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						parent
       UpperCAmelCase__							 =						batch_size
       UpperCAmelCase__							 =						patch_size
       UpperCAmelCase__							 =						max_length
       UpperCAmelCase__							 =						num_mel_bins
       UpperCAmelCase__							 =						is_training
       UpperCAmelCase__							 =						use_labels
       UpperCAmelCase__							 =						hidden_size
       UpperCAmelCase__							 =						num_hidden_layers
       UpperCAmelCase__							 =						num_attention_heads
       UpperCAmelCase__							 =						intermediate_size
       UpperCAmelCase__							 =						hidden_act
       UpperCAmelCase__							 =						hidden_dropout_prob
       UpperCAmelCase__							 =						attention_probs_dropout_prob
       UpperCAmelCase__							 =						type_sequence_label_size
       UpperCAmelCase__							 =						initializer_range
       UpperCAmelCase__							 =						scope
       UpperCAmelCase__							 =						frequency_stride
       UpperCAmelCase__							 =						time_stride

       # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
       UpperCAmelCase__							 =						(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
       UpperCAmelCase__							 =						(self.max_length - self.patch_size) // self.time_stride + 1
       UpperCAmelCase__							 =						frequency_out_dimension * time_out_dimension
       UpperCAmelCase__							 =						num_patches + 2
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       UpperCAmelCase__							 =						floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]      )

       UpperCAmelCase__							 =						None
       if self.use_labels:
        UpperCAmelCase__							 =						ids_tensor([self.batch_size] ,      self.type_sequence_label_size      )

       UpperCAmelCase__							 =						self.get_config()

       return config, input_values, labels
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Any:
       return ASTConfig(
           patch_size=self.patch_size ,      max_length=self.max_length ,      num_mel_bins=self.num_mel_bins ,      hidden_size=self.hidden_size ,      num_hidden_layers=self.num_hidden_layers ,      num_attention_heads=self.num_attention_heads ,      intermediate_size=self.intermediate_size ,      hidden_act=self.hidden_act ,      hidden_dropout_prob=self.hidden_dropout_prob ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob ,      is_decoder=lowerCamelCase ,      initializer_range=self.initializer_range ,      frequency_stride=self.frequency_stride ,      time_stride=self.time_stride ,      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :List[str]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						ASTModel(config=lowerCamelCase      )
       model.to(lowerCamelCase      )
       model.eval()
       UpperCAmelCase__							 =						model(lowerCamelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )







      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			str:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__
       ) , (
           UpperCAmelCase__
       ) , (
           UpperCAmelCase__
       ) , 
       )							 =						config_and_inputs
       UpperCAmelCase__							 =						{"input_values": input_values}
       return config, inputs_dict



@require_torch
class 			_UpperCamelCase      (				lowerCAmelCase	,       lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		(
          (
              ASTModel,
              ASTForAudioClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_        =		(
          {"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
          if is_torch_available()
          else {}
      )
      UpperCAmelCase_        =		False
      UpperCAmelCase_        =		False
      UpperCAmelCase_        =		False
      UpperCAmelCase_        =		False
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :str ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int      )     ->			str:
       if pipeline_test_casse_name == "AudioClassificationPipelineTests":
        return True

       return False
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			int:
       UpperCAmelCase__							 =						ASTModelTester(self      )
       UpperCAmelCase__							 =						ConfigTester(self ,      config_class=lowerCamelCase ,      has_text_modality=lowerCamelCase ,      hidden_size=37      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			str:
       self.config_tester.run_common_tests()
      @unittest.skip(reason="AST does not use inputs_embeds"      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Optional[Any]:
       pass
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Any:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							 =						model_class(lowerCamelCase      )
        self.assertIsInstance(model.get_input_embeddings() ,      (nn.Module)      )
        UpperCAmelCase__							 =						model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase ,      nn.Linear      )      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			List[str]:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							 =						model_class(lowerCamelCase      )
        UpperCAmelCase__							 =						inspect.signature(model.forward      )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCAmelCase__							 =						[*signature.parameters.keys()]

        UpperCAmelCase__							 =						["input_values"]
        self.assertListEqual(arg_names[:1] ,      lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase      )







      @slow
      def 	UpperCAmelCase_						(			self   :int      )     ->			Optional[Any]:
       for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__							 =						ASTModel.from_pretrained(lowerCamelCase      )
        self.assertIsNotNone(lowerCamelCase      )



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						hf_hub_download(
     repo_id="nielsr/audio-spectogram-transformer-checkpoint"						,							filename="sample_audio.flac"						,							repo_type="dataset"	)

 UpperCAmelCase__ , UpperCAmelCase__							 =						torchaudio.load(_lowerCAmelCase	)

 return audio, sampling_rate


@require_torch
@require_torchaudio
class 			_UpperCamelCase      (				unittest.TestCase		):
      @cached_property
      def 	UpperCAmelCase_						(			self   :str      )     ->			Dict:
       return (
           ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"      )
           if is_torchaudio_available()
           else None
       )







      @slow
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       UpperCAmelCase__							 =						self.default_feature_extractor
       UpperCAmelCase__							 =						ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						self.default_feature_extractor
       UpperCAmelCase__ , UpperCAmelCase__							 =						prepare_audio()
       UpperCAmelCase__							 =						audio.squeeze().numpy()
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      sampling_rate=lowerCamelCase ,      return_tensors="pt"      ).to(lowerCamelCase      )

       # forward pass
       with torch.no_grad():
        UpperCAmelCase__							 =						model(**lowerCamelCase      )

       # verify the logits
       UpperCAmelCase__							 =						torch.Size((1, 527)      )
       self.assertEqual(outputs.logits.shape ,      lowerCamelCase      )

       UpperCAmelCase__							 =						torch.tensor([-0.87_60, -7.00_42, -8.66_02]      ).to(lowerCamelCase      )

       self.assertTrue(torch.allclose(outputs.logits[0, :3] ,      lowerCamelCase ,      atol=1e-4      )      )





