
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()
_snake_case      :							Any     	=    logging.get_logger(__name__)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				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(__a		,    __a		,    repo_type="dataset"						)		,    "r"						)						)
				lowercase         =      {int(__a						): v for k, v in idalabel.items()}
				lowercase         =      idalabel
				lowercase         =      {v: k for k, v in idalabel.items()}

				return config

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				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  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				for key in orig_state_dict.copy().keys():
								lowercase         =      orig_state_dict.pop(__a						)

								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  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				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(__a		,    __a						)

@torch.no_grad()
def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__=False						):







				'''simple docstring'''


				lowercase         =      get_audio_spectrogram_transformer_config(__a						)

				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(__a		,    map_location="cpu"						)
				# remove some keys
				remove_keys(__a						)
				# rename some keys
				lowercase         =      convert_state_dict(__a		,    __a						)

				# load 🤗 model
				lowercase         =      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
				lowercase         =      -4.2_6_7_7_3_9_3 if "speech-commands" not in model_name else -6.8_4_5_9_7_8
				lowercase         =      4.5_6_8_9_9_7_4 if "speech-commands" not in model_name else 5.5_6_5_4_5_2_6
				lowercase         =      1024 if "speech-commands" not in model_name else 128
				lowercase         =      ASTFeatureExtractor(mean=__a		,    std=__a		,    max_length=__a						)

				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(__a						)
								lowercase         =      waveform.squeeze().numpy()

				lowercase         =      feature_extractor(__a		,    sampling_rate=16000		,    return_tensors="pt"						)

				# forward pass
				lowercase         =      model(**__a						)
				lowercase         =      outputs.logits

				if model_name == "ast-finetuned-audioset-10-10-0.4593":
								lowercase         =      torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]						)
				elif model_name == "ast-finetuned-audioset-10-10-0.450":
								lowercase         =      torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8]						)
				elif model_name == "ast-finetuned-audioset-10-10-0.448":
								lowercase         =      torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4]						)
				elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
								lowercase         =      torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7]						)
				elif model_name == "ast-finetuned-audioset-12-12-0.447":
								lowercase         =      torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3]						)
				elif model_name == "ast-finetuned-audioset-14-14-0.443":
								lowercase         =      torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3]						)
				elif model_name == "ast-finetuned-audioset-16-16-0.442":
								lowercase         =      torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0]						)
				elif model_name == "ast-finetuned-speech-commands-v2":
								lowercase         =      torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4]						)
				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__":
						_snake_case      :							Dict     	=    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."
						)

						_snake_case      :							List[str]     	=    parser.parse_args()
						convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_       :    Dict						      =			{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    int						      =			['''PLBartTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Optional[int]						      =			[
		    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PLBartForCausalLM''',
		    '''PLBartForConditionalGeneration''',
		    '''PLBartForSequenceClassification''',
		    '''PLBartModel''',
		    '''PLBartPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_plbart import PLBartTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_plbart import (
				    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PLBartForCausalLM,
				    PLBartForConditionalGeneration,
				    PLBartForSequenceClassification,
				    PLBartModel,
				    PLBartPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    Optional[int]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




