




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		:				str       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					int   ):






			'''simple docstring'''







			a    =			ASTConfig()

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

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

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

			return config




def 			__magic_name__				( A		:					Dict   ):






			'''simple docstring'''







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

			return name




def 			__magic_name__				( A		:					Tuple,  A		:					Optional[Any]   ):






			'''simple docstring'''







			for key in orig_state_dict.copy().keys():
						a    =			orig_state_dict.pop(A   )

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

			return orig_state_dict




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			a    =			[
			    "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 			__magic_name__				( A		:					List[str],  A		:					Optional[int],  A		:					Any=False   ):






			'''simple docstring'''







			a    =			get_audio_spectrogram_transformer_config(A   )

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

			# load 🤗 model
			a    =			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
			a    =			-4.2_67_73_93 if "speech-commands" not in model_name else -6.84_59_78
			a    =			4.5_68_99_74 if "speech-commands" not in model_name else 5.5_65_45_26
			a    =			1024 if "speech-commands" not in model_name else 128
			a    =			ASTFeatureExtractor(mean=A,  std=A,  max_length=A   )

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

						a    ,		a    =			torchaudio.load(A   )
						a    =			waveform.squeeze().numpy()

			a    =			feature_extractor(A,  sampling_rate=16000,  return_tensors="pt"   )

			# forward pass
			a    =			model(**A   )
			a    =			outputs.logits

			if model_name == "ast-finetuned-audioset-10-10-0.4593":
						a    =			torch.tensor([-0.87_60, -7.00_42, -8.66_02]   )
			elif model_name == "ast-finetuned-audioset-10-10-0.450":
						a    =			torch.tensor([-1.19_86, -7.09_03, -8.27_18]   )
			elif model_name == "ast-finetuned-audioset-10-10-0.448":
						a    =			torch.tensor([-2.61_28, -8.00_80, -9.43_44]   )
			elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
						a    =			torch.tensor([-1.50_80, -7.45_34, -8.89_17]   )
			elif model_name == "ast-finetuned-audioset-12-12-0.447":
						a    =			torch.tensor([-0.50_50, -6.58_33, -8.08_43]   )
			elif model_name == "ast-finetuned-audioset-14-14-0.443":
						a    =			torch.tensor([-0.38_26, -7.03_36, -8.24_13]   )
			elif model_name == "ast-finetuned-audioset-16-16-0.442":
						a    =			torch.tensor([-1.21_13, -6.91_01, -8.34_70]   )
			elif model_name == "ast-finetuned-speech-commands-v2":
						a    =			torch.tensor([6.15_89, -8.05_66, -8.79_84]   )
			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		:				Tuple       			=		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		:				Dict       			=		parser.parse_args()
		convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Any        = ['vqvae']
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				Union[str, Any]:




        '''simple docstring'''
        super().__init__()
        self.register_modules(unet=lowerCAmelCase__				, scheduler=lowerCAmelCase__				, mel=lowerCAmelCase__				, vqvae=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        return 50 if isinstance(self.scheduler				, lowerCAmelCase__ ) else 1_000
 @torch.no_grad()
 def __call__(   self				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__=True				, )					->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   steps or self.get_default_steps()
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size ) == int:
               lowercase__:   Optional[int]  			=   (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
               lowercase__:   List[str]  			=   randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )				, generator=lowerCAmelCase__				, device=self.device				, )
        lowercase__:   List[Any]  			=   noise
        lowercase__:   int  			=   None

        if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(lowerCAmelCase__				, lowerCAmelCase__ )
               lowercase__:   int  			=   self.mel.audio_slice_to_image(lowerCAmelCase__ )
               lowercase__:   int  			=   np.frombuffer(input_image.tobytes()				, dtype='uint8' ).reshape(
                   (input_image.height, input_image.width) )
               lowercase__:   str  			=   (input_image / 255) * 2 - 1
               lowercase__:   Union[str, Any]  			=   torch.tensor(input_image[np.newaxis, :, :]				, dtype=torch.float ).to(self.device )

               if self.vqvae is not None:
                      lowercase__:   Optional[int]  			=   self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__				, 0 ) ).latent_dist.sample(
                          generator=lowerCAmelCase__ )[0]
                      lowercase__:   Dict  			=   self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                      lowercase__:   List[Any]  			=   self.scheduler.add_noise(lowerCAmelCase__				, lowerCAmelCase__				, self.scheduler.timesteps[start_step - 1] )

               lowercase__:   str  			=   (
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               lowercase__:   Dict  			=   int(mask_start_secs * pixels_per_second )
               lowercase__:   Tuple  			=   int(mask_end_secs * pixels_per_second )
               lowercase__:   List[Any]  			=   self.scheduler.add_noise(lowerCAmelCase__				, lowerCAmelCase__				, torch.tensor(self.scheduler.timesteps[start_step:] ) )

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
               if isinstance(self.unet				, lowerCAmelCase__ ):
                      lowercase__:   Union[str, Any]  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )['sample']
               else:
                      lowercase__:   Optional[Any]  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__ )['sample']

               if isinstance(self.scheduler				, lowerCAmelCase__ ):
                      lowercase__:   List[str]  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, eta=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']
               else:
                      lowercase__:   int  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']

               if mask is not None:
                      if mask_start > 0:
                             lowercase__:   List[Any]  			=   mask[:, step, :, :mask_start]
                      if mask_end > 0:
                             lowercase__:   Optional[int]  			=   mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
               # 0.18215 was scaling factor used in training to ensure unit variance
               lowercase__:   Union[str, Any]  			=   1 / self.vqvae.config.scaling_factor * images
               lowercase__:   Optional[Any]  			=   self.vqvae.decode(lowerCAmelCase__ )['sample']

        lowercase__:   Dict  			=   (images / 2 + 0.5).clamp(0				, 1 )
        lowercase__:   List[Any]  			=   images.cpu().permute(0				, 2				, 3				, 1 ).numpy()
        lowercase__:   int  			=   (images * 255).round().astype('uint8' )
        lowercase__:   Optional[Any]  			=   list(
            (Image.fromarray(_[:, :, 0] ) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(lowerCAmelCase__				, mode='RGB' ).convert('L' ) for _ in images) )

        lowercase__:   Dict  			=   [self.mel.image_to_audio(lowerCAmelCase__ ) for _ in images]
        if not return_dict:
               return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase__ )[:, np.newaxis, :] )				, **ImagePipelineOutput(lowerCAmelCase__ ) )
 @torch.no_grad()
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = 50 )					->				np.ndarray:




        '''simple docstring'''
        assert isinstance(self.scheduler				, lowerCAmelCase__ )
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   np.array(
            [np.frombuffer(image.tobytes()				, dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
        lowercase__:   str  			=   (sample / 255) * 2 - 1
        lowercase__:   str  			=   torch.Tensor(lowerCAmelCase__ ).to(self.device )

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps				, (0,) ) ):
               lowercase__:   Union[str, Any]  			=   t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
               lowercase__:   Optional[Any]  			=   self.scheduler.alphas_cumprod[t]
               lowercase__:   str  			=   (
                   self.scheduler.alphas_cumprod[prev_timestep]
                   if prev_timestep >= 0
                   else self.scheduler.final_alpha_cumprod
               )
               lowercase__:   str  			=   1 - alpha_prod_t
               lowercase__:   int  			=   self.unet(lowerCAmelCase__				, lowerCAmelCase__ )['sample']
               lowercase__:   int  			=   (1 - alpha_prod_t_prev) ** 0.5 * model_output
               lowercase__:   Optional[int]  			=   (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
               lowercase__:   Any  			=   sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample
 @staticmethod
 def 		SCREAMING_SNAKE_CASE__   (   lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				torch.Tensor:




        '''simple docstring'''
        lowercase__:   Any  			=   acos(torch.dot(torch.flatten(lowerCAmelCase__ )				, torch.flatten(lowerCAmelCase__ ) ) / torch.norm(lowerCAmelCase__ ) / torch.norm(lowerCAmelCase__ ) )
        return sin((1 - alpha) * theta ) * xa / sin(lowerCAmelCase__ ) + sin(alpha * theta ) * xa / sin(lowerCAmelCase__ )


