


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







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Dict:
							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				)    ->				Tuple:
							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				)    ->				List[Any]:
							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				)    ->				Optional[int]:
							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							:    Optional[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.'
				)

				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    parser.parse_args()
				convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""
from math import factorial







def 						_a				(      _SCREAMING_SNAKE_CASE = 20				)    ->				int:
							snake_case_           =		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
							# 2, 3,...
							snake_case_           =		n // 2

							return int(factorial(_SCREAMING_SNAKE_CASE				) / (factorial(_SCREAMING_SNAKE_CASE				) * factorial(n - k				))				)


if __name__ == "__main__":
				import sys

				if len(sys.argv) == 1:
								print(solution(20))
				else:
								try:
												__SCREAMING_SNAKE_CASE							:    Optional[int]		=    int(sys.argv[1])
												print(solution(n))
								except ValueError:
												print('Invalid entry - please enter a number.')



