
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        SCREAMING_SNAKE_CASE__    :

       """simple docstring"""






       def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=24     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=2     ,	)->   Optional[int]:





             '''simple docstring'''






             __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     A__		(  self			)->   Dict:





             '''simple docstring'''






             __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     A__		(  self			)->   Optional[int]:





             '''simple docstring'''






             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=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	frequency_stride=self.frequency_stride     ,	time_stride=self.time_stride     ,	)



       def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[Any]:





             '''simple docstring'''






             __UpperCamelCase       =			ASTModel(config=SCREAMING_SNAKE_CASE_			)
             model.to(SCREAMING_SNAKE_CASE_			)
             model.eval()
             __UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
             self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)			)



       def     A__		(  self			)->   Tuple:





             '''simple docstring'''






             __UpperCamelCase       =			self.prepare_config_and_inputs()
             (
                 (
                 __UpperCamelCase							
             )  ,							(
                 __UpperCamelCase							
             )  ,							(
                 __UpperCamelCase							
             )  ,														
             )       =			config_and_inputs
             __UpperCamelCase       =			{'''input_values''': input_values}
             return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

       """simple docstring"""






       _snake_case     		=  (
           (
               ASTModel,
               ASTForAudioClassification,
           )
           if is_torch_available()
           else ()
       )
       _snake_case     		=  (
           {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
           if is_torch_available()
           else {}
       )
       _snake_case     		=  False
       _snake_case     		=  False
       _snake_case     		=  False
       _snake_case     		=  False



       def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   int:





             '''simple docstring'''






             if pipeline_test_casse_name == "AudioClassificationPipelineTests":
                   return True

             return False



       def     A__		(  self			)->   Tuple:





             '''simple docstring'''






             __UpperCamelCase       =			ASTModelTester(self			)
             __UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37			)



       def     A__		(  self			)->   Tuple:





             '''simple docstring'''






             self.config_tester.run_common_tests()



       @unittest.skip(reason='''AST does not use inputs_embeds'''			)
       def     A__		(  self			)->   Union[str, Any]:





             '''simple docstring'''






             pass



       def     A__		(  self			)->   List[Any]:





             '''simple docstring'''






             __UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
                   self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)			)
                   __UpperCamelCase       =			model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_     ,	nn.Linear			)			)



       def     A__		(  self			)->   List[str]:





             '''simple docstring'''






             __UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
                   __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]     ,	SCREAMING_SNAKE_CASE_			)



       def     A__		(  self			)->   List[Any]:





             '''simple docstring'''






             __UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



       @slow
       def     A__		(  self			)->   Optional[Any]:





             '''simple docstring'''






             for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __UpperCamelCase       =			ASTModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





def 	A_ (							)      ->    int:



      '''simple docstring'''

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

      __UpperCamelCase  ,							__UpperCamelCase       =			torchaudio.load(snake_case )

      return audio, sampling_rate






@require_torch
@require_torchaudio
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

       """simple docstring"""






       @cached_property
       def     A__		(  self			)->   List[str]:





             '''simple docstring'''






             return (
                 ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			)
                 if is_torchaudio_available()
                 else None
             )



       @slow
       def     A__		(  self			)->   Dict:





             '''simple docstring'''






             __UpperCamelCase       =			self.default_feature_extractor
             __UpperCamelCase       =			ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			).to(SCREAMING_SNAKE_CASE_			)

             __UpperCamelCase       =			self.default_feature_extractor
             __UpperCamelCase  ,							__UpperCamelCase       =			prepare_audio()
             __UpperCamelCase       =			audio.squeeze().numpy()
             __UpperCamelCase       =			feature_extractor(SCREAMING_SNAKE_CASE_     ,	sampling_rate=SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)

             # forward pass
             with torch.no_grad():
                   __UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

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

             __UpperCamelCase       =			torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]			).to(SCREAMING_SNAKE_CASE_			)

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






def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




