





"""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


lowerCAmelCase__     			=			{
    '''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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return torch.atana(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) / math.pi * 2


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.sin(t * math.pi / 2    ) ** 2
		UpperCamelCase   =	(1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )





class        _lowerCamelCase      (       _lowercase ):
						pass





class        _lowerCamelCase      (       nn.Module ):


						def __init__(self   ,							__a		) ->   Optional[Any]:
								super().__init__()

								UpperCamelCase   =	DiffusionAttnUnetaD(__a   ,							n_attn_layers=4		)
								UpperCamelCase   =	deepcopy(self.diffusion		)
								UpperCamelCase   =	torch.quasirandom.SobolEngine(1   ,							scramble=__a		)



def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	MODELS_MAP[model_name]["url"]
		os.system(F"wget {url} ./"    )

		return F"./{model_name}.ckpt"


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

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

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


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		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       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=13    ):




		"""simple docstring"""


		UpperCamelCase   =	input_string

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

		UpperCamelCase   =	0
		if string.startswith("net.3."    ):
				depth += 1
				UpperCamelCase   =	string[6:]
		elif string.startswith("net."    ):
				UpperCamelCase   =	string[4:]

		while string.startswith("main.7."    ):
				depth += 1
				UpperCamelCase   =	string[7:]

		if string.startswith("main."    ):
				UpperCamelCase   =	string[5:]

		# mid block
		if string[:2].isdigit():
				UpperCamelCase   =	string[:2]
				UpperCamelCase   =	string[2:]
		else:
				UpperCamelCase   =	string[0]
				UpperCamelCase   =	string[1:]

		if depth == max_depth:
				UpperCamelCase   =	MID_NUM_TO_LAYER[layer_num]
				UpperCamelCase   =	"mid_block"
		elif depth > 0 and int(_SCREAMING_SNAKE_CASE    ) < 7:
				UpperCamelCase   =	DOWN_NUM_TO_LAYER[layer_num]
				UpperCamelCase   =	F"down_blocks.{depth}"
		elif depth > 0 and int(_SCREAMING_SNAKE_CASE    ) > 7:
				UpperCamelCase   =	UP_NUM_TO_LAYER[layer_num]
				UpperCamelCase   =	F"up_blocks.{max_depth - depth - 1}"
		elif depth == 0:
				UpperCamelCase   =	DEPTH_0_TO_LAYER[layer_num]
				UpperCamelCase   =	F"up_blocks.{max_depth - 1}" if int(_SCREAMING_SNAKE_CASE    ) > 3 else "down_blocks.0"

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

		UpperCamelCase   =	string_left[1:]

		if "resnets" in new_layer:
				UpperCamelCase   =	convert_resconv_naming(_SCREAMING_SNAKE_CASE    )
		elif "attentions" in new_layer:
				UpperCamelCase   =	convert_attn_naming(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	new_string_left

		if not isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	prefix + "." + new_layer + "." + string_left
		else:
				UpperCamelCase   =	[prefix + "." + new_layer + "." + s for s in string_left]
		return new_string


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	{}
		for k, v in state_dict.items():
				if k.endswith("kernel"    ):
						# up- and downsample layers, don't have trainable weights
						continue

				UpperCamelCase   =	rename(_SCREAMING_SNAKE_CASE    )

				# check if we need to transform from Conv => Linear for attention
				if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	transform_conv_attns(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				else:
						UpperCamelCase   =	v

		return new_state_dict


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if len(_SCREAMING_SNAKE_CASE    ) == 1:
				if len(v.shape    ) == 3:
						# weight
						UpperCamelCase   =	v[:, :, 0]
				else:
						# bias
						UpperCamelCase   =	v
		else:
				# qkv matrices
				UpperCamelCase   =	v.shape[0]
				UpperCamelCase   =	trippled_shape // 3
				for i in range(3    ):
						if len(v.shape    ) == 3:
								UpperCamelCase   =	v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								UpperCamelCase   =	v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.device("cuda" if torch.cuda.is_available() else "cpu"    )

		UpperCamelCase   =	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()}"
				UpperCamelCase   =	download(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	MODELS_MAP[model_name]["sample_rate"]
		UpperCamelCase   =	MODELS_MAP[model_name]["sample_size"]

		UpperCamelCase   =	Object()
		UpperCamelCase   =	sample_size
		UpperCamelCase   =	sample_rate
		UpperCamelCase   =	0

		UpperCamelCase   =	UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE  ,	sample_rate=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	diffusers_model.state_dict()

		UpperCamelCase   =	DiffusionUncond(_SCREAMING_SNAKE_CASE    )
		orig_model.load_state_dict(torch.load(args.model_path  ,	map_location=_SCREAMING_SNAKE_CASE    )["state_dict"]    )
		UpperCamelCase   =	orig_model.diffusion_ema.eval()
		UpperCamelCase   =	orig_model.state_dict()
		UpperCamelCase   =	rename_orig_weights(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	set(renamed_state_dict.keys()    ) - set(diffusers_state_dict.keys()    )
		UpperCamelCase   =	set(diffusers_state_dict.keys()    ) - set(renamed_state_dict.keys()    )

		assert len(_SCREAMING_SNAKE_CASE    ) == 0, F"Problem with {renamed_minus_diffusers}"
		assert all(k.endswith("kernel"    ) for k in list(_SCREAMING_SNAKE_CASE    )    ), 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":
						UpperCamelCase   =	value.squeeze()

				UpperCamelCase   =	value

		diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	100
		UpperCamelCase   =	33

		UpperCamelCase   =	IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	torch.manual_seed(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	torch.randn([1, 2, config.sample_size]  ,	generator=_SCREAMING_SNAKE_CASE    ).to(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	torch.linspace(1  ,	0  ,	steps + 1  ,	device=_SCREAMING_SNAKE_CASE    )[:-1]
		UpperCamelCase   =	get_crash_schedule(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE  ,	scheduler=_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	torch.manual_seed(33    )
		UpperCamelCase   =	pipe(num_inference_steps=_SCREAMING_SNAKE_CASE  ,	generator=_SCREAMING_SNAKE_CASE    ).audios

		UpperCamelCase   =	sampling.iplms_sample(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	{}    )
		UpperCamelCase   =	generated.clamp(-1  ,	1    )

		UpperCamelCase   =	(generated - audio).abs().sum()
		UpperCamelCase   =	(generated - audio).abs().max()

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

		print("Diff sum"  ,	_SCREAMING_SNAKE_CASE    )
		print("Diff max"  ,	_SCREAMING_SNAKE_CASE    )

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

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


if __name__ == "__main__":
						lowerCAmelCase__     			=			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.''')
						lowerCAmelCase__     			=			parser.parse_args()

						main(args)













"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	0
		for ch in input_str:
				UpperCamelCase   =	ord(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	pow(2  ,	_SCREAMING_SNAKE_CASE    )

				# If we already turned on bit for current character's unicode
				if bitmap >> ch_unicode & 1 == 1:
						return False
				bitmap |= ch_bit_index_on
		return True


if __name__ == "__main__":
						import doctest

						doctest.testmod()






