
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			tempfile.mkdtemp()

													__UpperCamelCase       =			BlipImageProcessor()
													__UpperCamelCase       =			GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''			)

													__UpperCamelCase       =			BlipaProcessor(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

													processor.save_pretrained(self.tmpdirname			)



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





													'''simple docstring'''






													return AutoProcessor.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_			).tokenizer



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






													return AutoProcessor.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_			).image_processor



							def     A__		(  self			)->   int:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname			)



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





													'''simple docstring'''






													__UpperCamelCase       =			[np.random.randint(255     ,	size=(3, 30, 400)     ,	dtype=np.uinta			)]

													__UpperCamelCase       =			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_     ,	0     ,	-1			)			) for x in image_inputs]

													return image_inputs



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





													'''simple docstring'''






													__UpperCamelCase       =			BlipaProcessor(tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()			)
													processor.save_pretrained(self.tmpdirname			)

													__UpperCamelCase       =			self.get_tokenizer(bos_token='''(BOS)'''     ,	eos_token='''(EOS)'''			)
													__UpperCamelCase       =			self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0			)

													__UpperCamelCase       =			BlipaProcessor.from_pretrained(
													    self.tmpdirname     ,	bos_token='''(BOS)'''     ,	eos_token='''(EOS)'''     ,	do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0			)

													self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer_add_kwargs.get_vocab()			)
													self.assertIsInstance(processor.tokenizer     ,	SCREAMING_SNAKE_CASE_			)

													self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor_add_kwargs.to_json_string()			)
													self.assertIsInstance(processor.image_processor     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			self.prepare_image_inputs()

													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''			)
													__UpperCamelCase       =			processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''			)

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()     ,	input_processor[key].sum()     ,	delta=1E-2			)



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			'''lower newer'''

													__UpperCamelCase       =			processor(text=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			tokenizer(SCREAMING_SNAKE_CASE_     ,	return_token_type_ids=SCREAMING_SNAKE_CASE_			)

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



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			'''lower newer'''
													__UpperCamelCase       =			self.prepare_image_inputs()

													__UpperCamelCase       =			processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_			)

													self.assertListEqual(list(inputs.keys()			)     ,	['''pixel_values''', '''input_ids''', '''attention_mask''']			)

													# test if it raises when no input is passed
													with pytest.raises(SCREAMING_SNAKE_CASE_			):
																			processor()



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





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__UpperCamelCase       =			processor.batch_decode(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

													self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			'''lower newer'''
													__UpperCamelCase       =			self.prepare_image_inputs()

													__UpperCamelCase       =			processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_			)

													# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
													self.assertListEqual(list(inputs.keys()			)     ,	['''pixel_values''', '''input_ids''', '''attention_mask''']			)






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			)			)




