


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


_lowercase       :      Dict	=	{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 13_1072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
}





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Optional[Any]						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   Tuple:





	"""simple docstring"""







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






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				pass






class       _UpperCamelCase    (					nn.Module				):

				"""simple docstring"""







				def __init__(						self				,   a__   )	->				List[Any]:
					super().__init__()

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				MODELS_MAP[model_name]["""url"""]
	os.system(f'wget {url} ./'						)

	return f'./{model_name}.ckpt'


_lowercase       :      int	=	{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
_lowercase       :      Union[str, Any]	=	{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
_lowercase       :      Dict	=	{
    "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",
}
_lowercase       :      List[str]	=	{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

_lowercase       :      Any	=	{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

_lowercase       :      Dict	=	{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   List[str]:





	"""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   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Optional[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[int]=13						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				input_string

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

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

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

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

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

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

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

	A								=				string_left[1:]

	if "resnets" in new_layer:
		A								=				convert_resconv_naming(UpperCamelCase__						)
	elif "attentions" in new_layer:
		A								=				convert_attn_naming(UpperCamelCase__						)
		A								=				new_string_left

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   Dict:





	"""simple docstring"""







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

		A								=				rename(UpperCamelCase__						)

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

	return new_state_dict





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     str						) ->   Union[str, Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   List[Any]:





	"""simple docstring"""







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

	A								=				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()}'
		A								=				download(UpperCamelCase__						)

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

	A								=				Object()
	A								=				sample_size
	A								=				sample_rate
	A								=				0

	A								=				UNetaDModel(sample_size=UpperCamelCase__							,					sample_rate=UpperCamelCase__						)
	A								=				diffusers_model.state_dict()

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

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

	assert len(UpperCamelCase__						) == 0, f'Problem with {renamed_minus_diffusers}'
	assert all(k.endswith("""kernel"""						) for k in list(UpperCamelCase__						)						), 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":
			A								=				value.squeeze()

		A								=				value

	diffusers_model.load_state_dict(UpperCamelCase__						)

	A								=				1_00
	A								=				33

	A								=				IPNDMScheduler(num_train_timesteps=UpperCamelCase__						)

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

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

	A								=				DanceDiffusionPipeline(unet=UpperCamelCase__							,					scheduler=UpperCamelCase__						)

	A								=				torch.manual_seed(33						)
	A								=				pipe(num_inference_steps=UpperCamelCase__							,					generator=UpperCamelCase__						).audios

	A								=				sampling.iplms_sample(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					{}						)
	A								=				generated.clamp(-1							,					1						)

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

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

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

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

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


if __name__ == "__main__":
				_lowercase       :      List[Any]	=	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.")
				_lowercase       :      Union[str, Any]	=	parser.parse_args()

				main(args)







def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	return str(UpperCamelCase__						) == str(UpperCamelCase__						)[::-1]





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return int(UpperCamelCase__						) + int(str(UpperCamelCase__						)[::-1]						)





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 1_00_00						) ->   int:





	"""simple docstring"""







	A								=				[]
	for num in range(1							,					UpperCamelCase__						):
		A								=				0
		A								=				num
		while iterations < 50:
			A								=				sum_reverse(UpperCamelCase__						)
			iterations += 1
			if is_palindrome(UpperCamelCase__						):
				break
		else:
			lychrel_nums.append(UpperCamelCase__						)
	return len(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''{solution() = }''')



