
"""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


UpperCamelCase_					:						int				= {
    '''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 							A_     (__a				,   __a				):

					'''simple docstring'''





					return torch.atana(__a				,   __a				) / math.pi * 2
def 							A_     (__a				):

					'''simple docstring'''





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



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			pass





class __lowerCAmelCase				(					nn.Module      ):




			"""simple docstring"""







			def __init__(				self						:					str       ,   _snake_case						:					Optional[Any]	)      -> int:







								"""simple docstring"""
								super().__init__()

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





def 							A_     (__a				):

					'''simple docstring'''





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

					return f'./{model_name}.ckpt'


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

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

UpperCamelCase_					:						Dict				= {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}
def 							A_     (__a				):

					'''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_     (__a				):

					'''simple docstring'''





					for key, value in ATTN_MAP.items():
										if name.startswith(__a				) and not isinstance(__a				,   __a				):
															return name.replace(__a				,   __a				)
										elif name.startswith(__a				):
															return [name.replace(__a				,   __a				) for v in value]
					raise ValueError(f'Attn error with {name}'				)
def 							A_     (__a				,   __a=13				):

					'''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(__a				) < 7:
										A_				 =						DOWN_NUM_TO_LAYER[layer_num]
										A_				 =						f'down_blocks.{depth}'
					elif depth > 0 and int(__a				) > 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(__a				) > 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(__a				)
					elif "attentions" in new_layer:
										A_				 =						convert_attn_naming(__a				)
										A_				 =						new_string_left

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

					'''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(__a				)

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

					return new_state_dict
def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					if len(__a				) == 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 							A_     (__a				):

					'''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(__a				)

					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=__a				,   sample_rate=__a				)
					A_				 =						diffusers_model.state_dict()

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

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

					assert len(__a				) == 0, f'Problem with {renamed_minus_diffusers}'
					assert all(k.endswith("kernel"				) for k in list(__a				)				), 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(__a				)

					A_				 =						100
					A_				 =						33

					A_				 =						IPNDMScheduler(num_train_timesteps=__a				)

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

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

					A_				 =						DanceDiffusionPipeline(unet=__a				,   scheduler=__a				)

					A_				 =						torch.manual_seed(33				)
					A_				 =						pipe(num_inference_steps=__a				,   generator=__a				).audios

					A_				 =						sampling.iplms_sample(__a				,   __a				,   __a				,   {}				)
					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"				,   __a				)
					print("Diff max"				,   __a				)

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

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


if __name__ == "__main__":
		UpperCamelCase_					:						str				= 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.''')
		UpperCamelCase_					:						List[str]				= parser.parse_args()

		main(args)






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						y
					else:
										raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

					return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
		print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")




