



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()
UpperCamelCase__ =						logging.get_logger(__name__)






def  UpperCAmelCase__    (    lowercase__   ) ->      Union[str, Any]:
						__lowercase					=				ASTConfig()

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

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

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

						return config






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

						return name






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Any:
						for key in orig_state_dict.copy().keys():
												__lowercase					=				orig_state_dict.pop(lowercase__   )

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

						return orig_state_dict






def  UpperCAmelCase__    (    lowercase__   ) ->      List[str]:
						__lowercase					=				[
						    """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(lowercase__						,       lowercase__   )






@torch.no_grad()
def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__=False   ) ->      str:
						__lowercase					=				get_audio_spectrogram_transformer_config(lowercase__   )

						__lowercase					=				{
						    """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
						__lowercase					=				model_name_to_url[model_name]
						__lowercase					=				torch.hub.load_state_dict_from_url(lowercase__						,       map_location="""cpu"""   )
						# remove some keys
						remove_keys(lowercase__   )
						# rename some keys
						__lowercase					=				convert_state_dict(lowercase__						,       lowercase__   )

						# load 🤗 model
						__lowercase					=				ASTForAudioClassification(lowercase__   )
						model.eval()

						model.load_state_dict(lowercase__   )

						# verify outputs on dummy input
						# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
						__lowercase					=				-4.2677393 if """speech-commands""" not in model_name else -6.845978
						__lowercase					=				4.5689974 if """speech-commands""" not in model_name else 5.5654526
						__lowercase					=				1_024 if """speech-commands""" not in model_name else 128
						__lowercase					=				ASTFeatureExtractor(mean=lowercase__						,       std=lowercase__						,       max_length=lowercase__   )

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

												__lowercase	,			__lowercase					=				torchaudio.load(lowercase__   )
												__lowercase					=				waveform.squeeze().numpy()

						__lowercase					=				feature_extractor(lowercase__						,       sampling_rate=16_000						,       return_tensors="""pt"""   )

						# forward pass
						__lowercase					=				model(**lowercase__   )
						__lowercase					=				outputs.logits

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

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

						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__":
			UpperCamelCase__ =						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."
			)

			UpperCamelCase__ =						parser.parse_args()
			convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










import random






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__ = False   ) ->      dict:
						__lowercase					=				{i: [] for i in range(lowercase__   )}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(lowercase__   )
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(lowercase__   ):
												for j in range(i + 1						,       lowercase__   ):
																		if random.random() < probability:
																								graph[i].append(lowercase__   )
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(lowercase__   )
						return graph






def  UpperCAmelCase__    (    lowercase__   ) ->      dict:
						return {
						    i: [j for j in range(lowercase__   ) if i != j] for i in range(lowercase__   )
						}


if __name__ == "__main__":
			import doctest

			doctest.testmod()





