




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       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =			ASTConfig()

							if "10-10" in model_name:
														pass
							elif "speech-commands" in model_name:
														lowerCAmelCase   =			128
							elif "12-12" in model_name:
														lowerCAmelCase   =			12
														lowerCAmelCase   =			12
							elif "14-14" in model_name:
														lowerCAmelCase   =			14
														lowerCAmelCase   =			14
							elif "16-16" in model_name:
														lowerCAmelCase   =			16
														lowerCAmelCase   =			16
							else:
														raise ValueError('''Model not supported'''							)

							lowerCAmelCase   =			'''huggingface/label-files'''
							if "speech-commands" in model_name:
														lowerCAmelCase   =			35
														lowerCAmelCase   =			'''speech-commands-v2-id2label.json'''
							else:
														lowerCAmelCase   =			527
														lowerCAmelCase   =			'''audioset-id2label.json'''

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

							return config



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

							return name



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							for key in orig_state_dict.copy().keys():
														lowerCAmelCase   =			orig_state_dict.pop(_snake_case							)

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

							return orig_state_dict



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =			[
							    '''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(_snake_case						, _snake_case							)



@torch.no_grad()
def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case=False							):
							lowerCAmelCase   =			get_audio_spectrogram_transformer_config(_snake_case							)

							lowerCAmelCase   =			{
							    '''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
							lowerCAmelCase   =			model_name_to_url[model_name]
							lowerCAmelCase   =			torch.hub.load_state_dict_from_url(_snake_case						, map_location='''cpu'''							)
							# remove some keys
							remove_keys(_snake_case							)
							# rename some keys
							lowerCAmelCase   =			convert_state_dict(_snake_case						, _snake_case							)

							# load 🤗 model
							lowerCAmelCase   =			ASTForAudioClassification(_snake_case							)
							model.eval()

							model.load_state_dict(_snake_case							)

							# verify outputs on dummy input
							# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
							lowerCAmelCase   =			-4.2677393 if '''speech-commands''' not in model_name else -6.845978
							lowerCAmelCase   =			4.5689974 if '''speech-commands''' not in model_name else 5.5654526
							lowerCAmelCase   =			1024 if '''speech-commands''' not in model_name else 128
							lowerCAmelCase   =			ASTFeatureExtractor(mean=_snake_case						, std=_snake_case						, max_length=_snake_case							)

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

														lowerCAmelCase   ,     lowerCAmelCase   =			torchaudio.load(_snake_case							)
														lowerCAmelCase   =			waveform.squeeze().numpy()

							lowerCAmelCase   =			feature_extractor(_snake_case						, sampling_rate=16000						, return_tensors='''pt'''							)

							# forward pass
							lowerCAmelCase   =			model(**_snake_case							)
							lowerCAmelCase   =			outputs.logits

							if model_name == "ast-finetuned-audioset-10-10-0.4593":
														lowerCAmelCase   =			torch.tensor([-0.8760, -7.0042, -8.6602]							)
							elif model_name == "ast-finetuned-audioset-10-10-0.450":
														lowerCAmelCase   =			torch.tensor([-1.1986, -7.0903, -8.2718]							)
							elif model_name == "ast-finetuned-audioset-10-10-0.448":
														lowerCAmelCase   =			torch.tensor([-2.6128, -8.0080, -9.4344]							)
							elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
														lowerCAmelCase   =			torch.tensor([-1.5080, -7.4534, -8.8917]							)
							elif model_name == "ast-finetuned-audioset-12-12-0.447":
														lowerCAmelCase   =			torch.tensor([-0.5050, -6.5833, -8.0843]							)
							elif model_name == "ast-finetuned-audioset-14-14-0.443":
														lowerCAmelCase   =			torch.tensor([-0.3826, -7.0336, -8.2413]							)
							elif model_name == "ast-finetuned-audioset-16-16-0.442":
														lowerCAmelCase   =			torch.tensor([-1.2113, -6.9101, -8.3470]							)
							elif model_name == "ast-finetuned-speech-commands-v2":
														lowerCAmelCase   =			torch.tensor([6.1589, -8.0566, -8.7984]							)
							else:
														raise ValueError('''Unknown model name'''							)
							if not torch.allclose(logits[0, :3]						, _snake_case						, atol=1E-4							):
														raise ValueError('''Logits don\'t match'''							)
							print('''Looks ok!'''							)

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









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Tuple										=IFInpaintingSuperResolutionPipeline
				__a							: Dict										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__a							: int										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}  )
				__a							: Union[str, Any]										=PipelineTesterMixin.required_optional_params - {"""latents"""}



				def       __snake_case		( self						):
											return self._get_superresolution_dummy_components()



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
											if str(UpperCAmelCase_						).startswith('''mps'''						):
																		lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											else:
																		lowerCAmelCase   =		torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)

											lowerCAmelCase   =		floats_tensor((1, 3, 16, 16)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

											lowerCAmelCase   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': image,
											    '''original_image''': original_image,
											    '''mask_image''': mask_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''output_type''': '''numpy''',
											}

											return inputs



				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available()       , reason='''XFormers attention is only available with CUDA and `xformers` installed'''       , )
				def       __snake_case		( self						):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



				def       __snake_case		( self						):
											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != '''cuda'''       , reason='''float16 requires CUDA'''						)
				def       __snake_case		( self						):
											# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
											super().test_save_load_floataa(expected_max_diff=1E-1						)



				def       __snake_case		( self						):
											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



				def       __snake_case		( self						):
											self._test_save_load_local()






				def       __snake_case		( self						):
											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2       , )



