





"""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		:					Union[str, Any]	      =     {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 131072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
}






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]       ,			_lowercase		:       Union[str, Any]   )					->Union[str, Any]:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Union[str, Any]:






	'''simple docstring'''






	a :						Dict      =							torch.sin(t * math.pi / 2   ) ** 2
	a :						List[str]      =							(1 - sigma**2) ** 0.5
	return alpha_sigma_to_t(_lowercase       ,			_lowercase   )


class 			__UpperCamelCase							(		a__		):
							pass


class 			__UpperCamelCase							(		nn.Module		):






							def __init__(  self ,   lowerCAmelCase__ )      ->					Any:
								super().__init__()

								a :						Tuple      =							DiffusionAttnUnetaD(lowerCAmelCase__ ,   n_attn_layers=4 )
								a :						Union[str, Any]      =							deepcopy(self.diffusion )
								a :						List[Any]      =							torch.quasirandom.SobolEngine(1 ,   scramble=lowerCAmelCase__ )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Dict:






	'''simple docstring'''






	a :						List[Any]      =							MODELS_MAP[model_name]["url"]
	os.system(F"""wget {url} ./"""   )

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


a		:					Optional[int]	      =     {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
a		:					Dict	      =     {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
a		:					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''',
}
a		:					Dict	      =     {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

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

a		:					Union[str, Any]	      =     {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict   )					->Optional[Any]:






	'''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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]   )					->List[str]:






	'''simple docstring'''






	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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict       ,			_lowercase		:       Optional[Any]=13   )					->List[Any]:






	'''simple docstring'''






	a :						int      =							input_string

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

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

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

	if string.startswith("main."   ):
		a :						Optional[int]      =							string[5:]

	# mid block
	if string[:2].isdigit():
		a :						List[str]      =							string[:2]
		a :						List[str]      =							string[2:]
	else:
		a :						Optional[Any]      =							string[0]
		a :						Any      =							string[1:]

	if depth == max_depth:
		a :						Any      =							MID_NUM_TO_LAYER[layer_num]
		a :						List[Any]      =							"mid_block"
	elif depth > 0 and int(_lowercase   ) < 7:
		a :						int      =							DOWN_NUM_TO_LAYER[layer_num]
		a :						Optional[int]      =							F"""down_blocks.{depth}"""
	elif depth > 0 and int(_lowercase   ) > 7:
		a :						Optional[Any]      =							UP_NUM_TO_LAYER[layer_num]
		a :						List[Any]      =							F"""up_blocks.{max_depth - depth - 1}"""
	elif depth == 0:
		a :						List[str]      =							DEPTH_0_TO_LAYER[layer_num]
		a :						Tuple      =							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}."""   )

	a :						Optional[int]      =							string_left[1:]

	if "resnets" in new_layer:
		a :						Dict      =							convert_resconv_naming(_lowercase   )
	elif "attentions" in new_layer:
		a :						Union[str, Any]      =							convert_attn_naming(_lowercase   )
		a :						str      =							new_string_left

	if not isinstance(_lowercase       ,			_lowercase   ):
		a :						Dict      =							prefix + "." + new_layer + "." + string_left
	else:
		a :						int      =							[prefix + "." + new_layer + "." + s for s in string_left]
	return new_string






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int   )					->Tuple:






	'''simple docstring'''






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

		a :						Dict      =							rename(_lowercase   )

		# check if we need to transform from Conv => Linear for attention
		if isinstance(_lowercase       ,			_lowercase   ):
			a :						Tuple      =							transform_conv_attns(_lowercase       ,			_lowercase       ,			_lowercase   )
		else:
			a :						int      =							v

	return new_state_dict






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Tuple       ,			_lowercase		:       Optional[Any]   )					->Dict:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int   )					->str:






	'''simple docstring'''






	a :						Any      =							torch.device("cuda" if torch.cuda.is_available() else "cpu"   )

	a :						Dict      =							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 :						Optional[Any]      =							download(_lowercase   )

	a :						str      =							MODELS_MAP[model_name]["sample_rate"]
	a :						Any      =							MODELS_MAP[model_name]["sample_size"]

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

	a :						List[str]      =							UNetaDModel(sample_size=_lowercase       ,			sample_rate=_lowercase   )
	a :						List[Any]      =							diffusers_model.state_dict()

	a :						Dict      =							DiffusionUncond(_lowercase   )
	orig_model.load_state_dict(torch.load(args.model_path       ,			map_location=_lowercase   )["state_dict"]   )
	a :						str      =							orig_model.diffusion_ema.eval()
	a :						str      =							orig_model.state_dict()
	a :						Tuple      =							rename_orig_weights(_lowercase   )

	a :						Optional[int]      =							set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
	a :						Optional[Any]      =							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":
			a :						Optional[Any]      =							value.squeeze()

		a :						str      =							value

	diffusers_model.load_state_dict(_lowercase   )

	a :						List[Any]      =							100
	a :						Optional[int]      =							33

	a :						int      =							IPNDMScheduler(num_train_timesteps=_lowercase   )

	a :						Optional[Any]      =							torch.manual_seed(_lowercase   )
	a :						List[str]      =							torch.randn([1, 2, config.sample_size]       ,			generator=_lowercase   ).to(_lowercase   )

	a :						int      =							torch.linspace(1       ,			0       ,			steps + 1       ,			device=_lowercase   )[:-1]
	a :						List[str]      =							get_crash_schedule(_lowercase   )

	a :						Tuple      =							DanceDiffusionPipeline(unet=_lowercase       ,			scheduler=_lowercase   )

	a :						List[Any]      =							torch.manual_seed(33   )
	a :						List[str]      =							pipe(num_inference_steps=_lowercase       ,			generator=_lowercase   ).audios

	a :						Optional[Any]      =							sampling.iplms_sample(_lowercase       ,			_lowercase       ,			_lowercase       ,			{}   )
	a :						Optional[int]      =							generated.clamp(-1       ,			1   )

	a :						str      =							(generated - audio).abs().sum()
	a :						Optional[Any]      =							(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		:					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.''')
		a		:					Optional[Any]	      =     parser.parse_args()

		main(args)









"""simple docstring"""




from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a		:					int	      =     numpy.array([0, 0])
a		:					Optional[Any]	      =     numpy.array([0.5, 0.866_0254])
a		:					Tuple	      =     numpy.array([1, 0])
a		:					List[str]	      =     [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]       ,			_lowercase		:       int   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						List[str]      =							initial_vectors
	for _ in range(_lowercase   ):
		a :						Optional[Any]      =							iteration_step(_lowercase   )
	return vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[]
	for i, start_vector in enumerate(vectors[:-1]   ):
		a :						str      =							vectors[i + 1]
		new_vectors.append(_lowercase   )
		a :						Optional[int]      =							end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3   )
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3       ,			60   )   )
		new_vectors.append(start_vector + difference_vector * 2 / 3   )
	new_vectors.append(vectors[-1]   )
	return new_vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       numpy.ndarray       ,			_lowercase		:       float   )					->numpy.ndarray:






	'''simple docstring'''






	a :						int      =							numpy.radians(_lowercase   )
	a,			a :						Optional[int]      =							numpy.cos(_lowercase   ), numpy.sin(_lowercase   )
	a :						Dict      =							numpy.array(((c, -s), (s, c))   )
	return numpy.dot(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->None:






	'''simple docstring'''






	a :						Dict      =							plt.gca()
	axes.set_aspect("equal"   )

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	a,			a :						Any      =							zip(*_lowercase   )
	plt.plot(_lowercase       ,			_lowercase   )
	plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a		:					Optional[int]	      =     iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)


