
'''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()
__A								=			logging.get_logger(__name__)





def        _A						(							lowercase__		):
						lowercase__							       =				ASTConfig()

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

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

						lowercase__							       =				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						repo_type="""dataset"""		)						,						"""r"""		)		)
						lowercase__							       =				{int(_SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
						lowercase__							       =				idalabel
						lowercase__							       =				{v: k for k, v in idalabel.items()}

						return config





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

						return name





def        _A						(							lowercase__						,						lowercase__		):
						for key in orig_state_dict.copy().keys():
												lowercase__							       =				orig_state_dict.pop(_SCREAMING_SNAKE_CASE		)

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

						return orig_state_dict





def        _A						(							lowercase__		):
						lowercase__							       =				[
						    '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        _A						(							lowercase__						,						lowercase__						,						lowercase__=False		):
						lowercase__							       =				get_audio_spectrogram_transformer_config(_SCREAMING_SNAKE_CASE		)

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

						# load 🤗 model
						lowercase__							       =				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
						lowercase__							       =				-4.2_6_7_7_3_9_3 if 'speech-commands' not in model_name else -6.8_4_5_9_7_8
						lowercase__							       =				4.5_6_8_9_9_7_4 if 'speech-commands' not in model_name else 5.5_6_5_4_5_2_6
						lowercase__							       =				1024 if 'speech-commands' not in model_name else 128
						lowercase__							       =				ASTFeatureExtractor(mean=_SCREAMING_SNAKE_CASE						,						std=_SCREAMING_SNAKE_CASE						,						max_length=_SCREAMING_SNAKE_CASE		)

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

												lowercase__							       =				torchaudio.load(_SCREAMING_SNAKE_CASE		)
												lowercase__							       =				waveform.squeeze().numpy()

						lowercase__							       =				feature_extractor(_SCREAMING_SNAKE_CASE						,						sampling_rate=16000						,						return_tensors="""pt"""		)

						# forward pass
						lowercase__							       =				model(**_SCREAMING_SNAKE_CASE		)
						lowercase__							       =				outputs.logits

						if model_name == "ast-finetuned-audioset-10-10-0.4593":
												lowercase__							       =				torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]		)
						elif model_name == "ast-finetuned-audioset-10-10-0.450":
												lowercase__							       =				torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8]		)
						elif model_name == "ast-finetuned-audioset-10-10-0.448":
												lowercase__							       =				torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4]		)
						elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
												lowercase__							       =				torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7]		)
						elif model_name == "ast-finetuned-audioset-12-12-0.447":
												lowercase__							       =				torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3]		)
						elif model_name == "ast-finetuned-audioset-14-14-0.443":
												lowercase__							       =				torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3]		)
						elif model_name == "ast-finetuned-audioset-16-16-0.442":
												lowercase__							       =				torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0]		)
						elif model_name == "ast-finetuned-speech-commands-v2":
												lowercase__							       =				torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4]		)
						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__":
				__A								=			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."
				)

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







"""simple docstring"""
class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None)	->			List[str]:





        '''simple docstring'''





        a__:     Dict										=	data
        a__:     List[Any]										=	previous
        a__:     Any										=	next_node



      def __str__(						self)	->			str:





        '''simple docstring'''





        return f'{self.data}'



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self.data



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





        '''simple docstring'''





        return self.next



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





        '''simple docstring'''





        return self.previous







class    __snake_case       :



      def __init__(						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	head



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





        '''simple docstring'''





        return self



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





        '''simple docstring'''





        if not self.current:
          raise StopIteration
        else:
          a__:     Dict										=	self.current.get_data()
          a__:     Optional[Any]										=	self.current.get_next()
          return value







class    __snake_case       :



      def __init__(						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	None  # First node in list
        a__:     Optional[int]										=	None  # Last node in list



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





        '''simple docstring'''





        a__:     Dict										=	self.head
        a__:     Optional[Any]										=	[]
        while current is not None:
          nodes.append(current.get_data())
          a__:     str										=	current.get_next()
        return " ".join(str(lowercase) for node in nodes)



      def __contains__(						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[int]										=	self.head
        while current:
          if current.get_data() == value:
            return True
          a__:     Dict										=	current.get_next()
        return False



      def __iter__(						self)	->			int:





        '''simple docstring'''





        return LinkedListIterator(self.head)



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





        '''simple docstring'''





        if self.head:
          return self.head.get_data()
        return None



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





        '''simple docstring'''





        if self.tail:
          return self.tail.get_data()
        return None



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          a__:     Optional[Any]										=	node
          a__:     Optional[Any]										=	node
        else:
          self.insert_before_node(self.head    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          self.set_head(lowercase)
        else:
          self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Tuple										=	Node(lowercase)
        if self.head is None:
          self.set_head(lowercase)
        else:
          self.set_tail(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Union[str, Any]										=	node
        a__:     Optional[Any]										=	node.previous

        if node.get_previous() is None:
          a__:     Tuple										=	node_to_insert
        else:
          a__:     int										=	node_to_insert

        a__:     Optional[int]										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Optional[int]										=	node
        a__:     Tuple										=	node.next

        if node.get_next() is None:
          a__:     Optional[int]										=	node_to_insert
        else:
          a__:     Any										=	node_to_insert

        a__:     str										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Any										=	1
        a__:     Tuple										=	Node(lowercase)
        a__:     Tuple										=	self.head
        while node:
          if current_position == position:
            self.insert_before_node(lowercase    ,			lowercase)
            return
          current_position += 1
          a__:     List[Any]										=	node.next
        self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Node:





        '''simple docstring'''





        a__:     Tuple										=	self.head
        while node:
          if node.get_data() == item:
            return node
          a__:     List[str]										=	node.get_next()
        raise Exception('Node not found')



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Any:





        '''simple docstring'''





        if (node := self.get_node(lowercase)) is not None:
          if node == self.head:
            a__:     Any										=	self.head.get_next()

          if node == self.tail:
            a__:     List[Any]										=	self.tail.get_previous()

          self.remove_node_pointers(lowercase)



      @staticmethod
      def   lowerCamelCase_   (						lowercase)	->			None:





        '''simple docstring'''





        if node.get_next():
          a__:     Any										=	node.previous

        if node.get_previous():
          a__:     List[str]										=	node.next

        a__:     int										=	None
        a__:     Union[str, Any]										=	None



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return self.head is None







def 						__a   (	)	->None:
  pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()


