


"""simple docstring"""




from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase						=      logging.get_logger(__name__)

# TODO Update this
__UpperCamelCase						=      {
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     str								=     """esm"""







		def __init__(	self						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None						,       lowerCAmelCase__=768						,       lowerCAmelCase__=12						,       lowerCAmelCase__=12						,       lowerCAmelCase__=3_072						,       lowerCAmelCase__=0.1						,       lowerCAmelCase__=0.1						,       lowerCAmelCase__=1_026						,       lowerCAmelCase__=0.02						,       lowerCAmelCase__=1e-12						,       lowerCAmelCase__="absolute"						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__=False						,       lowerCAmelCase__=False						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None						,       **lowerCAmelCase__						,       )     ->							Any:
								super().__init__(pad_token_id=lowerCAmelCase__						,       mask_token_id=lowerCAmelCase__						,       **lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					vocab_size
								SCREAMING_SNAKE_CASE													=					hidden_size
								SCREAMING_SNAKE_CASE													=					num_hidden_layers
								SCREAMING_SNAKE_CASE													=					num_attention_heads
								SCREAMING_SNAKE_CASE													=					intermediate_size
								SCREAMING_SNAKE_CASE													=					hidden_dropout_prob
								SCREAMING_SNAKE_CASE													=					attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE													=					max_position_embeddings
								SCREAMING_SNAKE_CASE													=					initializer_range
								SCREAMING_SNAKE_CASE													=					layer_norm_eps
								SCREAMING_SNAKE_CASE													=					position_embedding_type
								SCREAMING_SNAKE_CASE													=					use_cache
								SCREAMING_SNAKE_CASE													=					emb_layer_norm_before
								SCREAMING_SNAKE_CASE													=					token_dropout
								SCREAMING_SNAKE_CASE													=					is_folding_model
								if is_folding_model:
														if esmfold_config is None:
																				logger.info('No esmfold_config supplied for folding model, using default values.'				)
																				SCREAMING_SNAKE_CASE													=					EsmFoldConfig()
														elif isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
																				SCREAMING_SNAKE_CASE													=					EsmFoldConfig(**lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					esmfold_config
														if vocab_list is None:
																				logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'				)
																				SCREAMING_SNAKE_CASE													=					get_default_vocab_list()
														else:
																				SCREAMING_SNAKE_CASE													=					vocab_list
								else:
														SCREAMING_SNAKE_CASE													=					None
														SCREAMING_SNAKE_CASE													=					None
								if self.esmfold_config is not None and getattr(self.esmfold_config						,       'use_esm_attn_map'						,       lowerCAmelCase__				):
														raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					super().to_dict()
								if isinstance(self.esmfold_config						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					self.esmfold_config.to_dict()
								return output





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     str								=     None
		SCREAMING_SNAKE_CASE_						:     bool								=     True
		SCREAMING_SNAKE_CASE_						:     bool								=     False
		SCREAMING_SNAKE_CASE_						:     bool								=     False
		SCREAMING_SNAKE_CASE_						:     bool								=     False
		SCREAMING_SNAKE_CASE_						:     float								=     0

		SCREAMING_SNAKE_CASE_						:     bool								=     True
		SCREAMING_SNAKE_CASE_						:     bool								=     False

		SCREAMING_SNAKE_CASE_						:     int								=     1_2_8
		SCREAMING_SNAKE_CASE_						:     "TrunkConfig"								=     None







		def 							__A						(	self				)     ->							str:
								if self.trunk is None:
														SCREAMING_SNAKE_CASE													=					TrunkConfig()
								elif isinstance(self.trunk						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					TrunkConfig(**self.trunk				)







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					asdict(self				)
								SCREAMING_SNAKE_CASE													=					self.trunk.to_dict()
								return output





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     int								=     4_8
		SCREAMING_SNAKE_CASE_						:     int								=     1_0_2_4
		SCREAMING_SNAKE_CASE_						:     int								=     1_2_8
		SCREAMING_SNAKE_CASE_						:     int								=     3_2
		SCREAMING_SNAKE_CASE_						:     int								=     3_2
		SCREAMING_SNAKE_CASE_						:     int								=     3_2
		SCREAMING_SNAKE_CASE_						:     float								=     0
		SCREAMING_SNAKE_CASE_						:     float								=     0
		SCREAMING_SNAKE_CASE_						:     bool								=     False
		SCREAMING_SNAKE_CASE_						:     int								=     4
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     1_2_8
		SCREAMING_SNAKE_CASE_						:     "StructureModuleConfig"								=     None







		def 							__A						(	self				)     ->							Dict:
								if self.structure_module is None:
														SCREAMING_SNAKE_CASE													=					StructureModuleConfig()
								elif isinstance(self.structure_module						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					StructureModuleConfig(**self.structure_module				)

								if self.max_recycles <= 0:
														raise ValueError(F'`max_recycles` should be positive, got {self.max_recycles}.'				)
								if self.sequence_state_dim % self.sequence_state_dim != 0:
														raise ValueError(
														    '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
														    F' {self.sequence_state_dim} and {self.sequence_state_dim}.'				)
								if self.pairwise_state_dim % self.pairwise_state_dim != 0:
														raise ValueError(
														    '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
														    F' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'				)

								SCREAMING_SNAKE_CASE													=					self.sequence_state_dim // self.sequence_head_width
								SCREAMING_SNAKE_CASE													=					self.pairwise_state_dim // self.pairwise_head_width

								if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
														raise ValueError(
														    '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
														    F' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'				)
								if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
														raise ValueError(
														    '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
														    F' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'				)
								if self.pairwise_state_dim % 2 != 0:
														raise ValueError(F'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'				)

								if self.dropout >= 0.4:
														raise ValueError(F'`dropout` should not be greater than 0.4, got {self.dropout}.'				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					asdict(self				)
								SCREAMING_SNAKE_CASE													=					self.structure_module.to_dict()
								return output





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     int								=     3_8_4
		SCREAMING_SNAKE_CASE_						:     int								=     1_2_8
		SCREAMING_SNAKE_CASE_						:     int								=     1_6
		SCREAMING_SNAKE_CASE_						:     int								=     1_2_8
		SCREAMING_SNAKE_CASE_						:     int								=     1_2
		SCREAMING_SNAKE_CASE_						:     int								=     4
		SCREAMING_SNAKE_CASE_						:     int								=     8
		SCREAMING_SNAKE_CASE_						:     float								=     0.1
		SCREAMING_SNAKE_CASE_						:     int								=     8
		SCREAMING_SNAKE_CASE_						:     int								=     1
		SCREAMING_SNAKE_CASE_						:     int								=     2
		SCREAMING_SNAKE_CASE_						:     int								=     7
		SCREAMING_SNAKE_CASE_						:     int								=     1_0
		SCREAMING_SNAKE_CASE_						:     float								=     1e-8
		SCREAMING_SNAKE_CASE_						:     float								=     1e5







		def 							__A						(	self				)     ->							str:
								return asdict(self				)





def     lowercase			()   ->						List[str]:
						return (
						    "<cls>",
						    "<pad>",
						    "<eos>",
						    "<unk>",
						    "L",
						    "A",
						    "G",
						    "V",
						    "S",
						    "E",
						    "R",
						    "T",
						    "I",
						    "D",
						    "P",
						    "K",
						    "Q",
						    "N",
						    "F",
						    "Y",
						    "M",
						    "H",
						    "W",
						    "C",
						    "X",
						    "B",
						    "U",
						    "Z",
						    "O",
						    ".",
						    "-",
						    "<null_1>",
						    "<mask>",
						)






"""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)


