


"""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							:      Dict						=      logging.get_logger(__name__)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_          =						ASTConfig()

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

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

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

							return config







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

							return name







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							for key in orig_state_dict.copy().keys():
														snake_case_          =						orig_state_dict.pop(_SCREAMING_SNAKE_CASE			)

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

							return orig_state_dict







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			str:
							snake_case_          =						get_audio_spectrogram_transformer_config(_SCREAMING_SNAKE_CASE			)

							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
							snake_case_          =						model_name_to_url[model_name]
							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
							snake_case_          =						convert_state_dict(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# load 🤗 model
							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
							snake_case_          =						-4.267_7393 if """speech-commands""" not in model_name else -6.84_5978
							snake_case_          =						4.568_9974 if """speech-commands""" not in model_name else 5.565_4526
							snake_case_          =						1_024 if """speech-commands""" not in model_name else 128
							snake_case_          =						ASTFeatureExtractor(mean=_SCREAMING_SNAKE_CASE			,	std=_SCREAMING_SNAKE_CASE			,	max_length=_SCREAMING_SNAKE_CASE			)

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

														snake_case_    ,  snake_case_          =						torchaudio.load(_SCREAMING_SNAKE_CASE			)
														snake_case_          =						waveform.squeeze().numpy()

							snake_case_          =						feature_extractor(_SCREAMING_SNAKE_CASE			,	sampling_rate=16_000			,	return_tensors="""pt"""			)

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

							if model_name == "ast-finetuned-audioset-10-10-0.4593":
														snake_case_          =						torch.tensor([-0.8760, -7.0042, -8.6602]			)
							elif model_name == "ast-finetuned-audioset-10-10-0.450":
														snake_case_          =						torch.tensor([-1.1986, -7.0903, -8.2718]			)
							elif model_name == "ast-finetuned-audioset-10-10-0.448":
														snake_case_          =						torch.tensor([-2.6128, -8.0080, -9.4344]			)
							elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
														snake_case_          =						torch.tensor([-1.5080, -7.4534, -8.8917]			)
							elif model_name == "ast-finetuned-audioset-12-12-0.447":
														snake_case_          =						torch.tensor([-0.5050, -6.5833, -8.0843]			)
							elif model_name == "ast-finetuned-audioset-14-14-0.443":
														snake_case_          =						torch.tensor([-0.3826, -7.0336, -8.2413]			)
							elif model_name == "ast-finetuned-audioset-16-16-0.442":
														snake_case_          =						torch.tensor([-1.2113, -6.9101, -8.3470]			)
							elif model_name == "ast-finetuned-speech-commands-v2":
														snake_case_          =						torch.tensor([6.1589, -8.0566, -8.7984]			)
							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__":
				__SCREAMING_SNAKE_CASE							:      List[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							:      Dict						=      parser.parse_args()
				convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__SCREAMING_SNAKE_CASE							:      int						=      {
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[str]							 =      """canine"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Union[str, Any]=12						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: List[Any]="gelu"						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: List[str]=16_384						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: int=0.02						,    UpperCAmelCase_							: Tuple=1E-12						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: int=0XE000						,    UpperCAmelCase_							: Optional[int]=0XE001						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[Any]=8						,    UpperCAmelCase_							: Dict=16_384						,    UpperCAmelCase_							: Optional[int]=128						,    **UpperCAmelCase_							: Any						,    )   ->int:



														"""simple docstring"""



														super().__init__(pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_						,    **UpperCAmelCase_    )

														snake_case_          =						max_position_embeddings
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						type_vocab_size
														snake_case_          =						layer_norm_eps

														# Character config:
														snake_case_          =						downsampling_rate
														snake_case_          =						upsampling_kernel_size
														snake_case_          =						num_hash_functions
														snake_case_          =						num_hash_buckets
														snake_case_          =						local_transformer_stride



