





'''simple docstring'''






import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A             =			{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 131_072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
}





def       lowercase_      (		lowercase__				,  lowercase__						)   ->List[Any]:
						return torch.atana(lowercase__				,  lowercase__						) / math.pi * 2





def       lowercase_      (		lowercase__						)   ->Any:
						_snake_case:   int  					=		torch.sin(t * math.pi / 2						) ** 2
						_snake_case:   List[Any]  					=		(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(lowercase__				,  lowercase__						)







class 			lowerCamelCase					(						UpperCamelCase__  ):
					pass







class 			lowerCamelCase					(						nn.Module  ):


					def __init__(					self	:  Tuple       ,     __snake_case	:  Dict       ):





											'''simple docstring'''





											super().__init__()

											_snake_case:   Dict  					=		DiffusionAttnUnetaD(__snake_case       ,     n_attn_layers=4       )
											_snake_case:   int  					=		deepcopy(self.diffusion       )
											_snake_case:   Dict  					=		torch.quasirandom.SobolEngine(1       ,     scramble=__snake_case       )





def       lowercase_      (		lowercase__						)   ->List[str]:
						_snake_case:   Any  					=		MODELS_MAP[model_name]['url']
						os.system(F'''wget {url} ./'''						)

						return F'''./{model_name}.ckpt'''


A             =			{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
A             =			{
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
A             =			{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
A             =			{
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

A             =			{
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

A             =			{
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}





def       lowercase_      (		lowercase__						)   ->int:




						if name.startswith('skip'						):
												return name.replace('skip'				,  RES_CONV_MAP['skip']						)

						# name has to be of format main.{digit}
						if not name.startswith('main.'						):
												raise ValueError(F'''ResConvBlock error with {name}'''						)

						return name.replace(name[:6]				,  RES_CONV_MAP[name[:6]]						)





def       lowercase_      (		lowercase__						)   ->Dict:




						for key, value in ATTN_MAP.items():
												if name.startswith(lowercase__						) and not isinstance(lowercase__				,  lowercase__						):
																		return name.replace(lowercase__				,  lowercase__						)
												elif name.startswith(lowercase__						):
																		return [name.replace(lowercase__				,  lowercase__						) for v in value]
						raise ValueError(F'''Attn error with {name}'''						)





def       lowercase_      (		lowercase__				,  lowercase__=13						)   ->List[Any]:
						_snake_case:   Optional[Any]  					=		input_string

						if string.split('.'						)[0] == "timestep_embed":
												return string.replace('timestep_embed'				,  'time_proj'						)

						_snake_case:   Tuple  					=		0
						if string.startswith('net.3.'						):
												depth += 1
												_snake_case:   Union[str, Any]  					=		string[6:]
						elif string.startswith('net.'						):
												_snake_case:   int  					=		string[4:]

						while string.startswith('main.7.'						):
												depth += 1
												_snake_case:   Optional[int]  					=		string[7:]

						if string.startswith('main.'						):
												_snake_case:   Union[str, Any]  					=		string[5:]

						# mid block
						if string[:2].isdigit():
												_snake_case:   Any  					=		string[:2]
												_snake_case:   Optional[int]  					=		string[2:]
						else:
												_snake_case:   Optional[Any]  					=		string[0]
												_snake_case:   Optional[int]  					=		string[1:]

						if depth == max_depth:
												_snake_case:   Union[str, Any]  					=		MID_NUM_TO_LAYER[layer_num]
												_snake_case:   List[str]  					=		'mid_block'
						elif depth > 0 and int(lowercase__						) < 7:
												_snake_case:   Tuple  					=		DOWN_NUM_TO_LAYER[layer_num]
												_snake_case:   str  					=		F'''down_blocks.{depth}'''
						elif depth > 0 and int(lowercase__						) > 7:
												_snake_case:   Optional[int]  					=		UP_NUM_TO_LAYER[layer_num]
												_snake_case:   Dict  					=		F'''up_blocks.{max_depth - depth - 1}'''
						elif depth == 0:
												_snake_case:   Dict  					=		DEPTH_0_TO_LAYER[layer_num]
												_snake_case:   Optional[int]  					=		F'''up_blocks.{max_depth - 1}''' if int(lowercase__						) > 3 else 'down_blocks.0'

						if not string_left.startswith('.'						):
												raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''						)

						_snake_case:   Union[str, Any]  					=		string_left[1:]

						if "resnets" in new_layer:
												_snake_case:   List[str]  					=		convert_resconv_naming(lowercase__						)
						elif "attentions" in new_layer:
												_snake_case:   Optional[Any]  					=		convert_attn_naming(lowercase__						)
												_snake_case:   Dict  					=		new_string_left

						if not isinstance(lowercase__				,  lowercase__						):
												_snake_case:   List[Any]  					=		prefix + '.' + new_layer + '.' + string_left
						else:
												_snake_case:   Optional[Any]  					=		[prefix + '.' + new_layer + '.' + s for s in string_left]
						return new_string





def       lowercase_      (		lowercase__						)   ->Optional[Any]:
						_snake_case:   Optional[int]  					=		{}
						for k, v in state_dict.items():
												if k.endswith('kernel'						):
																		# up- and downsample layers, don't have trainable weights
																		continue

												_snake_case:   List[Any]  					=		rename(lowercase__						)

												# check if we need to transform from Conv => Linear for attention
												if isinstance(lowercase__				,  lowercase__						):
																		_snake_case:   Union[str, Any]  					=		transform_conv_attns(lowercase__				,  lowercase__				,  lowercase__						)
												else:
																		_snake_case:   Dict  					=		v

						return new_state_dict





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__						)   ->Any:




						if len(lowercase__						) == 1:
												if len(v.shape						) == 3:
																		# weight
																		_snake_case:   List[str]  					=		v[:, :, 0]
												else:
																		# bias
																		_snake_case:   int  					=		v
						else:
												# qkv matrices
												_snake_case:   Union[str, Any]  					=		v.shape[0]
												_snake_case:   Union[str, Any]  					=		trippled_shape // 3
												for i in range(3						):
																		if len(v.shape						) == 3:
																								_snake_case:   Optional[int]  					=		v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								_snake_case:   Tuple  					=		v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict





def       lowercase_      (		lowercase__						)   ->str:
						_snake_case:   Dict  					=		torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)

						_snake_case:   Any  					=		args.model_path.split('/'						)[-1].split('.'						)[0]
						if not os.path.isfile(args.model_path						):
												assert (
												    model_name == args.model_path
												), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
												_snake_case:   Optional[int]  					=		download(lowercase__						)

						_snake_case:   Optional[int]  					=		MODELS_MAP[model_name]['sample_rate']
						_snake_case:   List[str]  					=		MODELS_MAP[model_name]['sample_size']

						_snake_case:   str  					=		Object()
						_snake_case:   Any  					=		sample_size
						_snake_case:   Dict  					=		sample_rate
						_snake_case:   List[Any]  					=		0

						_snake_case:   str  					=		UNetaDModel(sample_size=lowercase__				,  sample_rate=lowercase__						)
						_snake_case:   str  					=		diffusers_model.state_dict()

						_snake_case:   str  					=		DiffusionUncond(lowercase__						)
						orig_model.load_state_dict(torch.load(args.model_path				,  map_location=lowercase__						)['state_dict']						)
						_snake_case:   Dict  					=		orig_model.diffusion_ema.eval()
						_snake_case:   int  					=		orig_model.state_dict()
						_snake_case:   str  					=		rename_orig_weights(lowercase__						)

						_snake_case:   Optional[int]  					=		set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
						_snake_case:   Optional[int]  					=		set(diffusers_state_dict.keys()						) - set(renamed_state_dict.keys()						)

						assert len(lowercase__						) == 0, F'''Problem with {renamed_minus_diffusers}'''
						assert all(k.endswith('kernel'						) for k in list(lowercase__						)						), F'''Problem with {diffusers_minus_renamed}'''

						for key, value in renamed_state_dict.items():
												assert (
												    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
												), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
												if key == "time_proj.weight":
																		_snake_case:   int  					=		value.squeeze()

												_snake_case:   Optional[Any]  					=		value

						diffusers_model.load_state_dict(lowercase__						)

						_snake_case:   Any  					=		100
						_snake_case:   List[str]  					=		33

						_snake_case:   Any  					=		IPNDMScheduler(num_train_timesteps=lowercase__						)

						_snake_case:   Optional[Any]  					=		torch.manual_seed(lowercase__						)
						_snake_case:   Optional[int]  					=		torch.randn([1, 2, config.sample_size]				,  generator=lowercase__						).to(lowercase__						)

						_snake_case:   Dict  					=		torch.linspace(1				,  0				,  steps + 1				,  device=lowercase__						)[:-1]
						_snake_case:   Optional[int]  					=		get_crash_schedule(lowercase__						)

						_snake_case:   Any  					=		DanceDiffusionPipeline(unet=lowercase__				,  scheduler=lowercase__						)

						_snake_case:   Dict  					=		torch.manual_seed(33						)
						_snake_case:   int  					=		pipe(num_inference_steps=lowercase__				,  generator=lowercase__						).audios

						_snake_case:   str  					=		sampling.iplms_sample(lowercase__				,  lowercase__				,  lowercase__				,  {}						)
						_snake_case:   Any  					=		generated.clamp(-1				,  1						)

						_snake_case:   Optional[Any]  					=		(generated - audio).abs().sum()
						_snake_case:   Tuple  					=		(generated - audio).abs().max()

						if args.save:
												pipe.save_pretrained(args.checkpoint_path						)

						print('Diff sum'				,  lowercase__						)
						print('Diff max'				,  lowercase__						)

						assert diff_max < 1e-3, F'''Diff max: {diff_max} is too much :-/'''

						print(F'''Conversion for {model_name} successful!'''						)


if __name__ == "__main__":
		A             =			argparse.ArgumentParser()

		parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
		parser.add_argument(
		    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
		)
		parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
		A             =			parser.parse_args()

		main(args)












'''simple docstring'''






A   : List[str]             =			'\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A   : List[str]             =			[{'type': 'code', 'content': INSTALL_CONTENT}]
A   : Dict             =			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}





