

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    Dict 	=			ASTConfig()

	if "10-10" in model_name:
		pass
	elif "speech-commands" in model_name:
		__lowerCamelCase    :    Any 	=			1_2_8
	elif "12-12" in model_name:
		__lowerCamelCase    :    Optional[Any] 	=			1_2
		__lowerCamelCase    :    Optional[int] 	=			1_2
	elif "14-14" in model_name:
		__lowerCamelCase    :    List[Any] 	=			1_4
		__lowerCamelCase    :    Optional[int] 	=			1_4
	elif "16-16" in model_name:
		__lowerCamelCase    :    Tuple 	=			1_6
		__lowerCamelCase    :    List[Any] 	=			1_6
	else:
		raise ValueError('Model not supported'      )

	__lowerCamelCase    :    Union[str, Any] 	=			'huggingface/label-files'
	if "speech-commands" in model_name:
		__lowerCamelCase    :    List[Any] 	=			3_5
		__lowerCamelCase    :    Optional[int] 	=			'speech-commands-v2-id2label.json'
	else:
		__lowerCamelCase    :    Any 	=			5_2_7
		__lowerCamelCase    :    List[str] 	=			'audioset-id2label.json'

	__lowerCamelCase    :    Union[str, Any] 	=			json.load(open(hf_hub_download(lowerCamelCase__     ,			lowerCamelCase__     ,			repo_type='dataset'      )     ,			'r'      )      )
	__lowerCamelCase    :    Optional[Any] 	=			{int(lowerCamelCase__      ): v for k, v in idalabel.items()}
	__lowerCamelCase    :    List[str] 	=			idalabel
	__lowerCamelCase    :    List[Any] 	=			{v: k for k, v in idalabel.items()}

	return config






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	if "module.v" in name:
		__lowerCamelCase    :    Optional[int] 	=			name.replace('module.v'     ,			'audio_spectrogram_transformer'      )
	if "cls_token" in name:
		__lowerCamelCase    :    Tuple 	=			name.replace('cls_token'     ,			'embeddings.cls_token'      )
	if "dist_token" in name:
		__lowerCamelCase    :    Union[str, Any] 	=			name.replace('dist_token'     ,			'embeddings.distillation_token'      )
	if "pos_embed" in name:
		__lowerCamelCase    :    List[str] 	=			name.replace('pos_embed'     ,			'embeddings.position_embeddings'      )
	if "patch_embed.proj" in name:
		__lowerCamelCase    :    List[Any] 	=			name.replace('patch_embed.proj'     ,			'embeddings.patch_embeddings.projection'      )
	# transformer blocks
	if "blocks" in name:
		__lowerCamelCase    :    Tuple 	=			name.replace('blocks'     ,			'encoder.layer'      )
	if "attn.proj" in name:
		__lowerCamelCase    :    List[str] 	=			name.replace('attn.proj'     ,			'attention.output.dense'      )
	if "attn" in name:
		__lowerCamelCase    :    List[str] 	=			name.replace('attn'     ,			'attention.self'      )
	if "norm1" in name:
		__lowerCamelCase    :    str 	=			name.replace('norm1'     ,			'layernorm_before'      )
	if "norm2" in name:
		__lowerCamelCase    :    int 	=			name.replace('norm2'     ,			'layernorm_after'      )
	if "mlp.fc1" in name:
		__lowerCamelCase    :    int 	=			name.replace('mlp.fc1'     ,			'intermediate.dense'      )
	if "mlp.fc2" in name:
		__lowerCamelCase    :    List[str] 	=			name.replace('mlp.fc2'     ,			'output.dense'      )
	# final layernorm
	if "audio_spectrogram_transformer.norm" in name:
		__lowerCamelCase    :    Dict 	=			name.replace('audio_spectrogram_transformer.norm'     ,			'audio_spectrogram_transformer.layernorm'      )
	# classifier head
	if "module.mlp_head.0" in name:
		__lowerCamelCase    :    Tuple 	=			name.replace('module.mlp_head.0'     ,			'classifier.layernorm'      )
	if "module.mlp_head.1" in name:
		__lowerCamelCase    :    Any 	=			name.replace('module.mlp_head.1'     ,			'classifier.dense'      )

	return name






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	for key in orig_state_dict.copy().keys():
		__lowerCamelCase    :    Dict 	=			orig_state_dict.pop(lowerCamelCase__      )

		if "qkv" in key:
			__lowerCamelCase    :    Optional[Any] 	=			key.split('.'      )
			__lowerCamelCase    :    List[Any] 	=			int(key_split[3]      )
			__lowerCamelCase    :    List[str] 	=			config.hidden_size
			if "weight" in key:
				__lowerCamelCase    :    Optional[Any] 	=			val[:dim, :]
				__lowerCamelCase    :    int 	=			val[dim : dim * 2, :]
				__lowerCamelCase    :    int 	=			val[-dim:, :]
			else:
				__lowerCamelCase    :    Optional[Any] 	=			val[:dim]
				__lowerCamelCase    :    List[Any] 	=			val[dim : dim * 2]
				__lowerCamelCase    :    List[Any] 	=			val[-dim:]
		else:
			__lowerCamelCase    :    Tuple 	=			val

	return orig_state_dict






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    List[Any] 	=			[
	    '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(lowerCamelCase__     ,			lowerCamelCase__      )






@torch.no_grad()
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					Optional[Any]:
	__lowerCamelCase    :    Any 	=			get_audio_spectrogram_transformer_config(lowerCamelCase__      )

	__lowerCamelCase    :    Optional[Any] 	=			{
	    '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    :    Union[str, Any] 	=			model_name_to_url[model_name]
	__lowerCamelCase    :    Dict 	=			torch.hub.load_state_dict_from_url(lowerCamelCase__     ,			map_location='cpu'      )
	# remove some keys
	remove_keys(lowerCamelCase__      )
	# rename some keys
	__lowerCamelCase    :    Any 	=			convert_state_dict(lowerCamelCase__     ,			lowerCamelCase__      )

	# load 🤗 model
	__lowerCamelCase    :    Union[str, Any] 	=			ASTForAudioClassification(lowerCamelCase__      )
	model.eval()

	model.load_state_dict(lowerCamelCase__      )

	# verify outputs on dummy input
	# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
	__lowerCamelCase    :    Optional[int] 	=			-4.267_7393 if 'speech-commands' not in model_name else -6.84_5978
	__lowerCamelCase    :    Tuple 	=			4.568_9974 if 'speech-commands' not in model_name else 5.565_4526
	__lowerCamelCase    :    str 	=			1_0_2_4 if 'speech-commands' not in model_name else 1_2_8
	__lowerCamelCase    :    List[Any] 	=			ASTFeatureExtractor(mean=lowerCamelCase__     ,			std=lowerCamelCase__     ,			max_length=lowerCamelCase__      )

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

		__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			torchaudio.load(lowerCamelCase__      )
		__lowerCamelCase    :    int 	=			waveform.squeeze().numpy()

	__lowerCamelCase    :    Tuple 	=			feature_extractor(lowerCamelCase__     ,			sampling_rate=1_6_0_0_0     ,			return_tensors='pt'      )

	# forward pass
	__lowerCamelCase    :    List[Any] 	=			model(**lowerCamelCase__      )
	__lowerCamelCase    :    str 	=			outputs.logits

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

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

	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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




