




'''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()
A     =logging.get_logger(__name__)





def 						snake_case_							(_a	:							List[str]     ):
    UpperCAmelCase			       =		ASTConfig()

    if "10-10" in model_name:
        pass
    elif "speech-commands" in model_name:
        UpperCAmelCase			       =		1_2_8
    elif "12-12" in model_name:
        UpperCAmelCase			       =		1_2
        UpperCAmelCase			       =		1_2
    elif "14-14" in model_name:
        UpperCAmelCase			       =		1_4
        UpperCAmelCase			       =		1_4
    elif "16-16" in model_name:
        UpperCAmelCase			       =		1_6
        UpperCAmelCase			       =		1_6
    else:
        raise ValueError('''Model not supported'''     )

    UpperCAmelCase			       =		'''huggingface/label-files'''
    if "speech-commands" in model_name:
        UpperCAmelCase			       =		3_5
        UpperCAmelCase			       =		'''speech-commands-v2-id2label.json'''
    else:
        UpperCAmelCase			       =		5_2_7
        UpperCAmelCase			       =		'''audioset-id2label.json'''

    UpperCAmelCase			       =		json.load(open(hf_hub_download(_a    ,   _a    ,   repo_type='''dataset'''     )    ,   '''r'''     )     )
    UpperCAmelCase			       =		{int(_a     ): v for k, v in idalabel.items()}
    UpperCAmelCase			       =		idalabel
    UpperCAmelCase			       =		{v: k for k, v in idalabel.items()}

    return config





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

    return name





def 						snake_case_							(_a	:							Dict    ,   _a	:							List[Any]     ):
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase			       =		orig_state_dict.pop(_a     )

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

    return orig_state_dict





def 						snake_case_							(_a	:							Tuple     ):
    UpperCAmelCase			       =		[
        '''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_							(_a	:							int    ,   _a	:							Union[str, Any]    ,   _a	:							Dict=False     ):
    UpperCAmelCase			       =		get_audio_spectrogram_transformer_config(_a     )

    UpperCAmelCase			       =		{
        '''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
    UpperCAmelCase			       =		model_name_to_url[model_name]
    UpperCAmelCase			       =		torch.hub.load_state_dict_from_url(_a    ,   map_location='''cpu'''     )
    # remove some keys
    remove_keys(_a     )
    # rename some keys
    UpperCAmelCase			       =		convert_state_dict(_a    ,   _a     )

    # load 🤗 model
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		-4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
    UpperCAmelCase			       =		4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
    UpperCAmelCase			       =		1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
    UpperCAmelCase			       =		ASTFeatureExtractor(mean=_a    ,   std=_a    ,   max_length=_a     )

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

        UpperCAmelCase       ,     UpperCAmelCase			       =		torchaudio.load(_a     )
        UpperCAmelCase			       =		waveform.squeeze().numpy()

    UpperCAmelCase			       =		feature_extractor(_a    ,   sampling_rate=1_6_0_0_0    ,   return_tensors='''pt'''     )

    # forward pass
    UpperCAmelCase			       =		model(**_a     )
    UpperCAmelCase			       =		outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        UpperCAmelCase			       =		torch.tensor([-0.8760, -7.0042, -8.6602]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        UpperCAmelCase			       =		torch.tensor([-1.1986, -7.0903, -8.2718]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        UpperCAmelCase			       =		torch.tensor([-2.6128, -8.0080, -9.4344]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        UpperCAmelCase			       =		torch.tensor([-1.5080, -7.4534, -8.8917]     )
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        UpperCAmelCase			       =		torch.tensor([-0.5050, -6.5833, -8.0843]     )
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        UpperCAmelCase			       =		torch.tensor([-0.3826, -7.0336, -8.2413]     )
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        UpperCAmelCase			       =		torch.tensor([-1.2113, -6.9101, -8.3470]     )
    elif model_name == "ast-finetuned-speech-commands-v2":
        UpperCAmelCase			       =		torch.tensor([6.1589, -8.0566, -8.7984]     )
    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__":
   A     =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.'
   )

   A     =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 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  (	__a ):
       __a				:     str   				=					["""vqvae"""]
       def __init__(	self						:	str   ,				lowercase						:	AutoencoderKL   ,				lowercase						:	UNetaDConditionModel   ,				lowercase						:	Mel   ,				lowercase						:	Union[DDIMScheduler, DDPMScheduler]   ,				):



           '''simple docstring'''

           super().__init__()
           self.register_modules(unet=lowercase   ,				scheduler=lowercase   ,				mel=lowercase   ,				vqvae=lowercase				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return 50 if isinstance(self.scheduler   ,				lowercase				) else 1_000
       @torch.no_grad()
       def __call__(	self						:	Optional[Any]   ,				lowercase						:	int = 1   ,				lowercase						:	str = None   ,				lowercase						:	np.ndarray = None   ,				lowercase						:	int = 0   ,				lowercase						:	int = 0   ,				lowercase						:	int = None   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	Tuple=True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		steps or self.get_default_steps()
           self.scheduler.set_timesteps(lowercase				)
           UpperCAmelCase			       =		step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size				) == int:
               UpperCAmelCase			       =		(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
               UpperCAmelCase			       =		randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )   ,				generator=lowercase   ,				device=self.device   ,				)
           UpperCAmelCase			       =		noise
           UpperCAmelCase			       =		None

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

               if self.vqvae is not None:
                   UpperCAmelCase			       =		self.vqvae.encode(torch.unsqueeze(lowercase   ,				0				)				).latent_dist.sample(
                       generator=lowercase				)[0]
                   UpperCAmelCase			       =		self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                   UpperCAmelCase			       =		self.scheduler.add_noise(lowercase   ,				lowercase   ,				self.scheduler.timesteps[start_step - 1]				)

               UpperCAmelCase			       =		(
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               UpperCAmelCase			       =		int(mask_start_secs * pixels_per_second				)
               UpperCAmelCase			       =		int(mask_end_secs * pixels_per_second				)
               UpperCAmelCase			       =		self.scheduler.add_noise(lowercase   ,				lowercase   ,				torch.tensor(self.scheduler.timesteps[start_step:]				)				)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
               if isinstance(self.unet   ,				lowercase				):
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase   ,				lowercase				)['''sample''']
               else:
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase				)['''sample''']

               if isinstance(self.scheduler   ,				lowercase				):
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				eta=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']
               else:
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']

               if mask is not None:
                   if mask_start > 0:
                       UpperCAmelCase			       =		mask[:, step, :, :mask_start]
                   if mask_end > 0:
                       UpperCAmelCase			       =		mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
               # 0.18215 was scaling factor used in training to ensure unit variance
               UpperCAmelCase			       =		1 / self.vqvae.config.scaling_factor * images
               UpperCAmelCase			       =		self.vqvae.decode(lowercase				)['''sample''']

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

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

           return BaseOutput(**AudioPipelineOutput(np.array(lowercase				)[:, np.newaxis, :]				)   ,				**ImagePipelineOutput(lowercase				)				)
       @torch.no_grad()
       def     A      (	self						:	Dict   ,				lowercase						:	List[Image.Image]   ,				lowercase						:	int = 50				):



           '''simple docstring'''

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

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

           return sample







       @staticmethod
       def     A      (	lowercase						:	torch.Tensor   ,				lowercase						:	torch.Tensor   ,				lowercase						:	float				):



           '''simple docstring'''

           UpperCAmelCase			       =		acos(torch.dot(torch.flatten(lowercase				)   ,				torch.flatten(lowercase				)				) / torch.norm(lowercase				) / torch.norm(lowercase				)				)
           return sin((1 - alpha) * theta				) * xa / sin(lowercase				) + sin(alpha * theta				) * xa / sin(lowercase				)




