



"""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()
__SCREAMING_SNAKE_CASE  :						Tuple    							=						logging.get_logger(__name__)



def        lowerCAmelCase_(   lowercase_     :							Tuple							)	->       Any:
				_lowerCamelCase        =				ASTConfig()

				if "10-10" in model_name:
								pass
				elif "speech-commands" in model_name:
								_lowerCamelCase        =				1_28
				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        =				5_27
								_lowerCamelCase        =				'''audioset-id2label.json'''

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

				return config



def        lowerCAmelCase_(   lowercase_     :							Optional[int]							)	->       Tuple:
				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        lowerCAmelCase_(   lowercase_     :							List[str]						,   lowercase_     :							Any							)	->       str:
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase        =				orig_state_dict.pop(lowercase_							)

								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        lowerCAmelCase_(   lowercase_     :							Any							)	->       Tuple:
				_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(lowercase_						,   lowercase_							)



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							Any						,   lowercase_     :							Any=False							)	->       Optional[Any]:
				_lowerCamelCase        =				get_audio_spectrogram_transformer_config(lowercase_							)

				_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(lowercase_						,   map_location='''cpu'''							)
				# remove some keys
				remove_keys(lowercase_							)
				# rename some keys
				_lowerCamelCase        =				convert_state_dict(lowercase_						,   lowercase_							)

				# load 🤗 model
				_lowerCamelCase        =				ASTForAudioClassification(lowercase_							)
				model.eval()

				model.load_state_dict(lowercase_							)

				# verify outputs on dummy input
				# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
				_lowerCamelCase        =				-4.2_6_7_7_3_9_3 if '''speech-commands''' not in model_name else -6.8_4_5_9_7_8
				_lowerCamelCase        =				4.5_6_8_9_9_7_4 if '''speech-commands''' not in model_name else 5.5_6_5_4_5_2_6
				_lowerCamelCase        =				10_24 if '''speech-commands''' not in model_name else 1_28
				_lowerCamelCase        =				ASTFeatureExtractor(mean=lowercase_						,   std=lowercase_						,   max_length=lowercase_							)

				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(lowercase_							)
								_lowerCamelCase        =				waveform.squeeze().numpy()

				_lowerCamelCase        =				feature_extractor(lowercase_						,   sampling_rate=1_60_00						,   return_tensors='''pt'''							)

				# forward pass
				_lowerCamelCase        =				model(**lowercase_							)
				_lowerCamelCase        =				outputs.logits

				if model_name == "ast-finetuned-audioset-10-10-0.4593":
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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]						,   lowercase_						,   atol=1e-4							):
								raise ValueError('''Logits don\'t match'''							)
				print('''Looks ok!'''							)

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

				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__":
			__SCREAMING_SNAKE_CASE  :						str    							=						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.'''
			)

			__SCREAMING_SNAKE_CASE  :						Any    							=						parser.parse_args()
			convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""
import warnings

from .generation import TFGenerationMixin


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					warnings.warn(
					    'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
					    'be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.',				A__,				)
