






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			:					Tuple         =	logging.get_logger(__name__)





def       __lowerCAmelCase      (						__snake_case       ):
					__lowerCAmelCase								=					ASTConfig()

					if "10-10" in model_name:
										pass
					elif "speech-commands" in model_name:
										__lowerCAmelCase								=					128
					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								=					527
										__lowerCAmelCase								=					"audioset-id2label.json"

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

					return config





def       __lowerCAmelCase      (						__snake_case       ):
					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      (						__snake_case					, __snake_case       ):
					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase								=					orig_state_dict.pop(_A       )

										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      (						__snake_case       ):
					__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(_A					, _A       )





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case=False       ):
					__lowerCAmelCase								=					get_audio_spectrogram_transformer_config(_A       )

					__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(_A					, map_location="cpu"       )
					# remove some keys
					remove_keys(_A       )
					# rename some keys
					__lowerCAmelCase								=					convert_state_dict(_A					, _A       )

					# load 🤗 model
					__lowerCAmelCase								=					ASTForAudioClassification(_A       )
					model.eval()

					model.load_state_dict(_A       )

					# verify outputs on dummy input
					# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
					__lowerCAmelCase								=					-4.2_677_393 if "speech-commands" not in model_name else -6.845_978
					__lowerCAmelCase								=					4.5_689_974 if "speech-commands" not in model_name else 5.5_654_526
					__lowerCAmelCase								=					1024 if "speech-commands" not in model_name else 128
					__lowerCAmelCase								=					ASTFeatureExtractor(mean=_A					, std=_A					, max_length=_A       )

					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(_A       )
										__lowerCAmelCase								=					waveform.squeeze().numpy()

					__lowerCAmelCase								=					feature_extractor(_A					, sampling_rate=1_6000					, return_tensors="pt"       )

					# forward pass
					__lowerCAmelCase								=					model(**_A       )
					__lowerCAmelCase								=					outputs.logits

					if model_name == "ast-finetuned-audioset-10-10-0.4593":
										__lowerCAmelCase								=					torch.tensor([-0.8_760, -7.0_042, -8.6_602]       )
					elif model_name == "ast-finetuned-audioset-10-10-0.450":
										__lowerCAmelCase								=					torch.tensor([-1.1_986, -7.0_903, -8.2_718]       )
					elif model_name == "ast-finetuned-audioset-10-10-0.448":
										__lowerCAmelCase								=					torch.tensor([-2.6_128, -8.0_080, -9.4_344]       )
					elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
										__lowerCAmelCase								=					torch.tensor([-1.5_080, -7.4_534, -8.8_917]       )
					elif model_name == "ast-finetuned-audioset-12-12-0.447":
										__lowerCAmelCase								=					torch.tensor([-0.5_050, -6.5_833, -8.0_843]       )
					elif model_name == "ast-finetuned-audioset-14-14-0.443":
										__lowerCAmelCase								=					torch.tensor([-0.3_826, -7.0_336, -8.2_413]       )
					elif model_name == "ast-finetuned-audioset-16-16-0.442":
										__lowerCAmelCase								=					torch.tensor([-1.2_113, -6.9_101, -8.3_470]       )
					elif model_name == "ast-finetuned-speech-commands-v2":
										__lowerCAmelCase								=					torch.tensor([6.1_589, -8.0_566, -8.7_984]       )
					else:
										raise ValueError("Unknown model name"       )
					if not torch.allclose(logits[0, :3]					, _A					, atol=1E-4       ):
										raise ValueError("Logits don't match"       )
					print("Looks ok!"       )

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

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














def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					len(__snake_case       )
     while cur > 1:
          # Find the maximum number in arr
          __lowerCAmelCase								=					arr.index(max(arr[0:cur]       )       )
          # Reverse from 0 to mi
          __lowerCAmelCase								=					arr[mi::-1] + arr[mi + 1 : len(__snake_case       )]
          # Reverse whole list
          __lowerCAmelCase								=					arr[cur - 1 :: -1] + arr[cur : len(__snake_case       )]
          cur -= 1
     return arr


if __name__ == "__main__":
 lowerCamelCase			:					List[str]         =	input('''Enter numbers separated by a comma:\n''').strip()
 lowerCamelCase			:					Tuple         =	[int(item) for item in user_input.split(''',''')]
 print(pancake_sort(unsorted))






