
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__	:Optional[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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					return torch.atana(lowerCAmelCase__				,    lowerCAmelCase__	) / math.pi * 2



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.sin(t * math.pi / 2	) ** 2
					lowercase					=		(1 - sigma**2) ** 0.5
					return alpha_sigma_to_t(lowerCAmelCase__				,    lowerCAmelCase__	)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							pass







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__):
												super().__init__()

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



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		MODELS_MAP[model_name]['''url''']
					os.system(f'wget {url} ./'	)

					return f'./{model_name}.ckpt'


lowercase__	:Optional[Any]										=					{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
lowercase__	:Tuple										=					{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
lowercase__	: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",
}
lowercase__	:Dict										=					{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

lowercase__	:Optional[Any]										=					{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

lowercase__	:List[Any]										=					{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}



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



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=13	):
					'''simple docstring'''
					lowercase					=		input_string

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

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

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

					if string.startswith('''main.'''	):
										lowercase					=		string[5:]

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

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

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

					lowercase					=		string_left[1:]

					if "resnets" in new_layer:
										lowercase					=		convert_resconv_naming(lowerCAmelCase__	)
					elif "attentions" in new_layer:
										lowercase					=		convert_attn_naming(lowerCAmelCase__	)
										lowercase					=		new_string_left

					if not isinstance(lowerCAmelCase__				,    lowerCAmelCase__	):
										lowercase					=		prefix + '''.''' + new_layer + '''.''' + string_left
					else:
										lowercase					=		[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
					return new_string



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

										lowercase					=		rename(lowerCAmelCase__	)

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

					return new_state_dict



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



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)

					lowercase					=		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()}'
										lowercase					=		download(lowerCAmelCase__	)

					lowercase					=		MODELS_MAP[model_name]['''sample_rate''']
					lowercase					=		MODELS_MAP[model_name]['''sample_size''']

					lowercase					=		Object()
					lowercase					=		sample_size
					lowercase					=		sample_rate
					lowercase					=		0

					lowercase					=		UNetaDModel(sample_size=lowerCAmelCase__				,    sample_rate=lowerCAmelCase__	)
					lowercase					=		diffusers_model.state_dict()

					lowercase					=		DiffusionUncond(lowerCAmelCase__	)
					orig_model.load_state_dict(torch.load(args.model_path				,    map_location=lowerCAmelCase__	)['''state_dict''']	)
					lowercase					=		orig_model.diffusion_ema.eval()
					lowercase					=		orig_model.state_dict()
					lowercase					=		rename_orig_weights(lowerCAmelCase__	)

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

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

										lowercase					=		value

					diffusers_model.load_state_dict(lowerCAmelCase__	)

					lowercase					=		100
					lowercase					=		33

					lowercase					=		IPNDMScheduler(num_train_timesteps=lowerCAmelCase__	)

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

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

					lowercase					=		DanceDiffusionPipeline(unet=lowerCAmelCase__				,    scheduler=lowerCAmelCase__	)

					lowercase					=		torch.manual_seed(33	)
					lowercase					=		pipe(num_inference_steps=lowerCAmelCase__				,    generator=lowerCAmelCase__	).audios

					lowercase					=		sampling.iplms_sample(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    {}	)
					lowercase					=		generated.clamp(-1				,    1	)

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

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

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

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

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


if __name__ == "__main__":
							lowercase__	:Tuple										=					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__	:Dict										=					parser.parse_args()

							main(args)






from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = 10**-10	):
					'''simple docstring'''
					lowercase					=		a
					while True:
										lowercase					=		Decimal(lowerCAmelCase__	) - (
										    Decimal(eval(lowerCAmelCase__	)	) / Decimal(eval(str(diff(lowerCAmelCase__	)	)	)	)  # noqa: S307
										)
										# This number dictates the accuracy of the answer
										if abs(eval(lowerCAmelCase__	)	) < precision:  # noqa: S307
															return float(lowerCAmelCase__	)


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')




