


"""simple docstring"""




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

	if is_vision_available():
		from transformers import MaskaFormerImageProcessor

if is_vision_available():
	from PIL import Image
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=2						,       lowerCAmelCase__=True						,       lowerCAmelCase__=False						,       lowerCAmelCase__=10						,       lowerCAmelCase__=3						,       lowerCAmelCase__=32 * 8						,       lowerCAmelCase__=32 * 8						,       lowerCAmelCase__=4						,       lowerCAmelCase__=64						,       )     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					is_training
								SCREAMING_SNAKE_CASE													=					use_auxiliary_loss
								SCREAMING_SNAKE_CASE													=					num_queries
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					min_size
								SCREAMING_SNAKE_CASE													=					max_size
								SCREAMING_SNAKE_CASE													=					num_labels
								SCREAMING_SNAKE_CASE													=					hidden_dim
								SCREAMING_SNAKE_CASE													=					hidden_dim







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
								    lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.ones([self.batch_size, self.min_size, self.max_size]						,       device=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					(
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,       device=lowerCAmelCase__				) > 0.5
								).float()
								SCREAMING_SNAKE_CASE													=					(torch.rand((self.batch_size, self.num_labels)						,       device=lowerCAmelCase__				) > 0.5).long()

								SCREAMING_SNAKE_CASE													=					self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					MaskaFormerConfig(
								    hidden_size=self.hidden_dim						,       )
								SCREAMING_SNAKE_CASE													=					self.num_queries
								SCREAMING_SNAKE_CASE													=					self.num_labels

								SCREAMING_SNAKE_CASE													=					[1, 1, 1, 1]
								SCREAMING_SNAKE_CASE													=					self.num_channels

								SCREAMING_SNAKE_CASE													=					64
								SCREAMING_SNAKE_CASE													=					128
								SCREAMING_SNAKE_CASE													=					self.hidden_dim
								SCREAMING_SNAKE_CASE													=					self.hidden_dim
								SCREAMING_SNAKE_CASE													=					self.hidden_dim
								return config







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE													=					{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
								return config, inputs_dict







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					output.encoder_hidden_states
								SCREAMING_SNAKE_CASE													=					output.pixel_decoder_hidden_states
								SCREAMING_SNAKE_CASE													=					output.transformer_decoder_hidden_states

								self.parent.assertTrue(len(lowerCAmelCase__				)						,       len(config.backbone_config.depths				)				)
								self.parent.assertTrue(len(lowerCAmelCase__				)						,       len(config.backbone_config.depths				)				)
								self.parent.assertTrue(len(lowerCAmelCase__				)						,       config.decoder_layers				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__=False				)     ->							Tuple:
								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					MaskaFormerModel(config=lowerCAmelCase__				)
														model.to(lowerCAmelCase__				)
														model.eval()

														SCREAMING_SNAKE_CASE													=					model(pixel_values=lowerCAmelCase__						,       pixel_mask=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__				)

								self.parent.assertEqual(
								    output.transformer_decoder_last_hidden_state.shape						,       (self.batch_size, self.num_queries, self.hidden_dim)						,       )
								# let's ensure the other two hidden state exists
								self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
								self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

								if output_hidden_states:
														self.check_output_hidden_state(lowerCAmelCase__						,       lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.eval()

								def comm_check_on_output(lowerCAmelCase__				):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
														self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape						,       (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)						,       )
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape						,       (self.batch_size, self.num_queries, self.num_labels + 1)				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(pixel_values=lowerCAmelCase__						,       pixel_mask=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__				)

														comm_check_on_output(lowerCAmelCase__				)

														SCREAMING_SNAKE_CASE													=					model(
														    pixel_values=lowerCAmelCase__						,       pixel_mask=lowerCAmelCase__						,       mask_labels=lowerCAmelCase__						,       class_labels=lowerCAmelCase__				)

								comm_check_on_output(lowerCAmelCase__				)

								self.parent.assertTrue(result.loss is not None				)
								self.parent.assertEqual(result.loss.shape						,       torch.Size([1]				)				)





@require_torch
class 					lowerCAmelCase	(					lowerCamelCase_	,    lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Dict								=     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE_						:     Dict								=     {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

		SCREAMING_SNAKE_CASE_						:     str								=     False
		SCREAMING_SNAKE_CASE_						:     Any								=     False
		SCREAMING_SNAKE_CASE_						:     List[str]								=     False
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     False







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					MaskaFormerModelTester(self				)
								SCREAMING_SNAKE_CASE													=					ConfigTester(self						,       config_class=lowerCAmelCase__						,       has_text_modality=lowerCAmelCase__				)







		def 							__A						(	self				)     ->							str:
								self.config_tester.run_common_tests()







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__						,       **lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase__				)







		@unittest.skip(reason='Mask2Former does not use inputs_embeds'				)
		def 							__A						(	self				)     ->							Any:
								pass







		@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'				)
		def 							__A						(	self				)     ->							Optional[Any]:
								pass







		@unittest.skip(reason='Mask2Former is not a generative model'				)
		def 							__A						(	self				)     ->							List[str]:
								pass







		@unittest.skip(reason='Mask2Former does not use token embeddings'				)
		def 							__A						(	self				)     ->							Optional[int]:
								pass







		@require_torch_multi_gpu
		@unittest.skip(
		    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'				)
		def 							__A						(	self				)     ->							Union[str, Any]:
								pass







		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
		def 							__A						(	self				)     ->							Any:
								pass







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

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

														SCREAMING_SNAKE_CASE													=					['pixel_values']
														self.assertListEqual(arg_names[:1]						,       lowerCAmelCase__				)







		@slow
		def 							__A						(	self				)     ->							Union[str, Any]:
								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														SCREAMING_SNAKE_CASE													=					MaskaFormerModel.from_pretrained(lowerCAmelCase__				)
														self.assertIsNotNone(lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					(self.model_tester.min_size,) * 2
								SCREAMING_SNAKE_CASE													=					{
								    'pixel_values': torch.randn((2, 3, *size)						,       device=lowerCAmelCase__				),
								    'mask_labels': torch.randn((2, 10, *size)						,       device=lowerCAmelCase__				),
								    'class_labels': torch.zeros(2						,       10						,       device=lowerCAmelCase__				).long(),
								}
								SCREAMING_SNAKE_CASE													=					self.model_tester.get_config()

								SCREAMING_SNAKE_CASE													=					MaskaFormerForUniversalSegmentation(lowerCAmelCase__				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)
								self.assertTrue(outputs.loss is not None				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__						,       **lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				).to(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__						,       output_attentions=lowerCAmelCase__				)
														self.assertTrue(outputs.attentions is not None				)







		def 							__A						(	self				)     ->							Dict:
								if not self.model_tester.is_training:
														return

								SCREAMING_SNAKE_CASE													=					self.all_model_classes[1]
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()

								SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.train()

								SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__						,       mask_labels=lowerCAmelCase__						,       class_labels=lowerCAmelCase__				).loss
								loss.backward()







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					self.all_model_classes[1]
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE													=					True
								SCREAMING_SNAKE_CASE													=					True

								SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				).to(lowerCAmelCase__				)
								model.train()

								SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__						,       mask_labels=lowerCAmelCase__						,       class_labels=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								SCREAMING_SNAKE_CASE													=					outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()

								SCREAMING_SNAKE_CASE													=					outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								SCREAMING_SNAKE_CASE													=					outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=lowerCAmelCase__				)

								self.assertIsNotNone(encoder_hidden_states.grad				)
								self.assertIsNotNone(pixel_decoder_hidden_states.grad				)
								self.assertIsNotNone(transformer_decoder_hidden_states.grad				)
								self.assertIsNotNone(attentions.grad				)





__UpperCamelCase						=      1E-4


def     lowercase			()   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
						return image


@require_vision
@slow
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@cached_property
		def 							__A						(	self				)     ->							Optional[Any]:
								return "facebook/mask2former-swin-small-coco-instance"







		@cached_property
		def 							__A						(	self				)     ->							str:
								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.default_image_processor
								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					image_processor(lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
								# check size
								self.assertEqual(lowerCAmelCase__						,       (1, 3, 384, 384)				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]				).to(lowerCAmelCase__				)
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]				).to(lowerCAmelCase__				)
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]				).to(lowerCAmelCase__				)
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(lowerCAmelCase__				).eval()
								SCREAMING_SNAKE_CASE													=					self.default_image_processor
								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					image_processor(lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
								# check size
								self.assertEqual(lowerCAmelCase__						,       (1, 3, 384, 384)				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)
								# masks_queries_logits
								SCREAMING_SNAKE_CASE													=					outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape						,       (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
								SCREAMING_SNAKE_CASE													=					[
								    [-8.78_39, -9.00_56, -8.81_21],
								    [-7.41_04, -7.03_13, -6.54_01],
								    [-6.61_05, -6.34_27, -6.46_75],
								]
								SCREAMING_SNAKE_CASE													=					torch.tensor(lowerCAmelCase__				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)
								# class_queries_logits
								SCREAMING_SNAKE_CASE													=					outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape						,       (1, model.config.num_queries, model.config.num_labels + 1)				)
								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [
								        [1.83_24, -8.08_35, -4.19_22],
								        [0.84_50, -9.00_50, -3.60_53],
								        [0.30_45, -7.72_93, -3.02_75],
								    ]				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,       lowerCAmelCase__						,       atol=lowerCAmelCase__				)				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(lowerCAmelCase__				).eval()
								SCREAMING_SNAKE_CASE													=					self.default_image_processor

								SCREAMING_SNAKE_CASE													=					image_processor(
								    [np.zeros((3, 800, 1_333)				), np.zeros((3, 800, 1_333)				)]						,       segmentation_maps=[np.zeros((384, 384)				).astype(np.floataa				), np.zeros((384, 384)				).astype(np.floataa				)]						,       return_tensors='pt'						,       )

								SCREAMING_SNAKE_CASE													=					inputs['pixel_values'].to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					[el.to(lowerCAmelCase__				) for el in inputs['mask_labels']]
								SCREAMING_SNAKE_CASE													=					[el.to(lowerCAmelCase__				) for el in inputs['class_labels']]

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)

								self.assertTrue(outputs.loss is not None				)






"""simple docstring"""




import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase						=      logging.get_logger(__name__)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					ASTConfig()

						if "10-10" in model_name:
												pass
						elif "speech-commands" in model_name:
												SCREAMING_SNAKE_CASE													=					1_28
						elif "12-12" in model_name:
												SCREAMING_SNAKE_CASE													=					12
												SCREAMING_SNAKE_CASE													=					12
						elif "14-14" in model_name:
												SCREAMING_SNAKE_CASE													=					14
												SCREAMING_SNAKE_CASE													=					14
						elif "16-16" in model_name:
												SCREAMING_SNAKE_CASE													=					16
												SCREAMING_SNAKE_CASE													=					16
						else:
												raise ValueError('Model not supported'  )

						SCREAMING_SNAKE_CASE													=					'huggingface/label-files'
						if "speech-commands" in model_name:
												SCREAMING_SNAKE_CASE													=					35
												SCREAMING_SNAKE_CASE													=					'speech-commands-v2-id2label.json'
						else:
												SCREAMING_SNAKE_CASE													=					5_27
												SCREAMING_SNAKE_CASE													=					'audioset-id2label.json'

						SCREAMING_SNAKE_CASE													=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			repo_type='dataset'  ) ,			'r'  )  )
						SCREAMING_SNAKE_CASE													=					{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE													=					idalabel
						SCREAMING_SNAKE_CASE													=					{v: k for k, v in idalabel.items()}

						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						List[Any]:
						if "module.v" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('module.v' ,			'audio_spectrogram_transformer'  )
						if "cls_token" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('cls_token' ,			'embeddings.cls_token'  )
						if "dist_token" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('dist_token' ,			'embeddings.distillation_token'  )
						if "pos_embed" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('pos_embed' ,			'embeddings.position_embeddings'  )
						if "patch_embed.proj" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('patch_embed.proj' ,			'embeddings.patch_embeddings.projection'  )
						# transformer blocks
						if "blocks" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('blocks' ,			'encoder.layer'  )
						if "attn.proj" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('attn.proj' ,			'attention.output.dense'  )
						if "attn" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('attn' ,			'attention.self'  )
						if "norm1" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('norm1' ,			'layernorm_before'  )
						if "norm2" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('norm2' ,			'layernorm_after'  )
						if "mlp.fc1" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('mlp.fc1' ,			'intermediate.dense'  )
						if "mlp.fc2" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('mlp.fc2' ,			'output.dense'  )
						# final layernorm
						if "audio_spectrogram_transformer.norm" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('audio_spectrogram_transformer.norm' ,			'audio_spectrogram_transformer.layernorm'  )
						# classifier head
						if "module.mlp_head.0" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('module.mlp_head.0' ,			'classifier.layernorm'  )
						if "module.mlp_head.1" in name:
												SCREAMING_SNAKE_CASE													=					name.replace('module.mlp_head.1' ,			'classifier.dense'  )

						return name


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				Dict  )   ->						Dict:
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE													=					orig_state_dict.pop(SCREAMING_SNAKE_CASE_  )

												if "qkv" in key:
																		SCREAMING_SNAKE_CASE													=					key.split('.'  )
																		SCREAMING_SNAKE_CASE													=					int(key_split[3]  )
																		SCREAMING_SNAKE_CASE													=					config.hidden_size
																		if "weight" in key:
																								SCREAMING_SNAKE_CASE													=					val[:dim, :]
																								SCREAMING_SNAKE_CASE													=					val[dim : dim * 2, :]
																								SCREAMING_SNAKE_CASE													=					val[-dim:, :]
																		else:
																								SCREAMING_SNAKE_CASE													=					val[:dim]
																								SCREAMING_SNAKE_CASE													=					val[dim : dim * 2]
																								SCREAMING_SNAKE_CASE													=					val[-dim:]
												else:
																		SCREAMING_SNAKE_CASE													=					val

						return orig_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					[
						    'module.v.head.weight',
						    'module.v.head.bias',
						    'module.v.head_dist.weight',
						    'module.v.head_dist.bias',
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


@torch.no_grad()
def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=False  )   ->						Optional[int]:
						SCREAMING_SNAKE_CASE													=					get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					{
						    'ast-finetuned-audioset-10-10-0.4593': (
						        'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-10-10-0.450': (
						        'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-10-10-0.448': (
						        'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-10-10-0.448-v2': (
						        'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-12-12-0.447': (
						        'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-14-14-0.443': (
						        'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-16-16-0.442': (
						        'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
						    ),
						    'ast-finetuned-speech-commands-v2': (
						        'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
						    ),
						}

						# load original state_dict
						SCREAMING_SNAKE_CASE													=					model_name_to_url[model_name]
						SCREAMING_SNAKE_CASE													=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
						# remove some keys
						remove_keys(SCREAMING_SNAKE_CASE_  )
						# rename some keys
						SCREAMING_SNAKE_CASE													=					convert_state_dict(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						# load 🤗 model
						SCREAMING_SNAKE_CASE													=					ASTForAudioClassification(SCREAMING_SNAKE_CASE_  )
						model.eval()

						model.load_state_dict(SCREAMING_SNAKE_CASE_  )

						# verify outputs on dummy input
						# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
						SCREAMING_SNAKE_CASE													=					-4.2_67_73_93 if 'speech-commands' not in model_name else -6.84_59_78
						SCREAMING_SNAKE_CASE													=					4.5_68_99_74 if 'speech-commands' not in model_name else 5.5_65_45_26
						SCREAMING_SNAKE_CASE													=					10_24 if 'speech-commands' not in model_name else 1_28
						SCREAMING_SNAKE_CASE													=					ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE_ ,			std=SCREAMING_SNAKE_CASE_ ,			max_length=SCREAMING_SNAKE_CASE_  )

						if "speech-commands" in model_name:
												SCREAMING_SNAKE_CASE													=					load_dataset('speech_commands' ,			'v0.02' ,			split='validation'  )
												SCREAMING_SNAKE_CASE													=					dataset[0]['audio']['array']
						else:
												SCREAMING_SNAKE_CASE													=					hf_hub_download(
												    repo_id='nielsr/audio-spectogram-transformer-checkpoint' ,			filename='sample_audio.flac' ,			repo_type='dataset' ,			)

												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					torchaudio.load(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					waveform.squeeze().numpy()

						SCREAMING_SNAKE_CASE													=					feature_extractor(SCREAMING_SNAKE_CASE_ ,			sampling_rate=1_60_00 ,			return_tensors='pt'  )

						# forward pass
						SCREAMING_SNAKE_CASE													=					model(**SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					outputs.logits

						if model_name == "ast-finetuned-audioset-10-10-0.4593":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.87_60, -7.00_42, -8.66_02]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.450":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.19_86, -7.09_03, -8.27_18]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.448":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-2.61_28, -8.00_80, -9.43_44]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.50_80, -7.45_34, -8.89_17]  )
						elif model_name == "ast-finetuned-audioset-12-12-0.447":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.50_50, -6.58_33, -8.08_43]  )
						elif model_name == "ast-finetuned-audioset-14-14-0.443":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.38_26, -7.03_36, -8.24_13]  )
						elif model_name == "ast-finetuned-audioset-16-16-0.442":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.21_13, -6.91_01, -8.34_70]  )
						elif model_name == "ast-finetuned-speech-commands-v2":
												SCREAMING_SNAKE_CASE													=					torch.tensor([6.15_89, -8.05_66, -8.79_84]  )
						else:
												raise ValueError('Unknown model name'  )
						if not torch.allclose(logits[0, :3] ,			SCREAMING_SNAKE_CASE_ ,			atol=1E-4  ):
												raise ValueError('Logits don\'t match'  )
						print('Looks ok!'  )

						if pytorch_dump_folder_path is not None:
												Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
												print(F'Saving model {model_name} to {pytorch_dump_folder_path}'  )
												model.save_pretrained(SCREAMING_SNAKE_CASE_  )
												print(F'Saving feature extractor to {pytorch_dump_folder_path}'  )
												feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_  )

						if push_to_hub:
												print('Pushing model and feature extractor to the hub...'  )
												model.push_to_hub(F'MIT/{model_name}'  )
												feature_extractor.push_to_hub(F'MIT/{model_name}'  )


if __name__ == "__main__":
	__UpperCamelCase						=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''ast-finetuned-audioset-10-10-0.4593''',
	    type=str,
	    help='''Name of the Audio Spectrogram Transformer model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	__UpperCamelCase						=      parser.parse_args()
	convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


