





'''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()
__lowerCamelCase						: List[str]		=   logging.get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase       			=ASTConfig()

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

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

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

		return config


def 					_a   (__SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

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

		return name


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		for key in orig_state_dict.copy().keys():
				_UpperCamelCase       			=orig_state_dict.pop(__SCREAMING_SNAKE_CASE   )

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

		return orig_state_dict


def 					_a   (__SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase       			=[
		    '''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   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=False   ):


		"""simple docstring"""

		_UpperCamelCase       			=get_audio_spectrogram_transformer_config(__SCREAMING_SNAKE_CASE   )

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

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

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

				_UpperCamelCase			,     _UpperCamelCase       			=torchaudio.load(__SCREAMING_SNAKE_CASE   )
				_UpperCamelCase       			=waveform.squeeze().numpy()

		_UpperCamelCase       			=feature_extractor(__SCREAMING_SNAKE_CASE  ,      sampling_rate=1_6000  ,      return_tensors='''pt'''   )

		# forward pass
		_UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
		_UpperCamelCase       			=outputs.logits

		if model_name == "ast-finetuned-audioset-10-10-0.4593":
				_UpperCamelCase       			=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":
				_UpperCamelCase       			=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":
				_UpperCamelCase       			=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":
				_UpperCamelCase       			=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":
				_UpperCamelCase       			=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":
				_UpperCamelCase       			=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":
				_UpperCamelCase       			=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":
				_UpperCamelCase       			=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__":
						__lowerCamelCase						: List[Any]		=   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.'
						)

						__lowerCamelCase						: Optional[int]		=   parser.parse_args()
						convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













'''simple docstring'''






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)







class 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				None




@experimental
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  return _map_with_joblib(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE   ) else len(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[]  # We organize the splits ourselve (contiguous splits)
  for index in range(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) // num_proc
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) % num_proc
    _UpperCamelCase       			=div * index + min(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

  if len(__SCREAMING_SNAKE_CASE   ) != sum(len(i[1]   ) for i in split_kwds   ):
    raise ValueError(
        f'''Error dividing inputs iterable among processes. '''
        f'''Total number of objects {len(__SCREAMING_SNAKE_CASE   )}, '''
        f'''length: {sum(len(i[1]   ) for i in split_kwds   )}'''   )

  logger.info(
      f'''Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}'''   )
  _UpperCamelCase			,     _UpperCamelCase       			=None, None
  if not disable_tqdm:
    _UpperCamelCase			,     _UpperCamelCase       			=(RLock(),), tqdm.set_lock
  with Pool(__SCREAMING_SNAKE_CASE  ,      initargs=__SCREAMING_SNAKE_CASE  ,      initializer=__SCREAMING_SNAKE_CASE   ) as pool:
    _UpperCamelCase       			=pool.map(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  logger.info(f'''Finished {num_proc} processes'''   )
  _UpperCamelCase       			=[obj for proc_res in mapped for obj in proc_res]
  logger.info(f'''Unpacked {len(__SCREAMING_SNAKE_CASE   )} objects'''   )

  return mapped


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name  ,      n_jobs=__SCREAMING_SNAKE_CASE   ):
    return joblib.Parallel()(
        joblib.delayed(__SCREAMING_SNAKE_CASE   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _UpperCamelCase       			=None






