
'''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


__lowercase							:   int					=     {
    '''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 lowercase_	(   _lowercase			,       _lowercase				)     ->							List[Any]:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

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

class 	__lowercase				(  _lowercase    ):
			pass

class 	__lowercase				(  nn.Module    ):

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

								lowerCamelCase_					:      str     =		DiffusionAttnUnetaD(A			,   n_attn_layers=4      )
								lowerCamelCase_					:      str     =		deepcopy(self.diffusion      )
								lowerCamelCase_					:      Union[str, Any]     =		torch.quasirandom.SobolEngine(1			,   scramble=A      )




def lowercase_	(   _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		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							:   int					=     {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__lowercase							:   List[Any]					=     {
    '''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							:   int					=     {
    '''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							:   Optional[Any]					=     {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}




def lowercase_	(   _lowercase				)     ->							Dict:
					'''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 lowercase_	(   _lowercase				)     ->							Optional[int]:
					'''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 lowercase_	(   _lowercase			,       _lowercase=13				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, Any]     =		input_string

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

					lowerCamelCase_					:      int     =		0
					if string.startswith('''net.3.'''				):
										depth += 1
										lowerCamelCase_					:      List[str]     =		string[6:]
					elif string.startswith('''net.'''				):
										lowerCamelCase_					:      Optional[Any]     =		string[4:]

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

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

					# mid block
					if string[:2].isdigit():
										lowerCamelCase_					:      int     =		string[:2]
										lowerCamelCase_					:      Tuple     =		string[2:]
					else:
										lowerCamelCase_					:      Any     =		string[0]
										lowerCamelCase_					:      Optional[int]     =		string[1:]

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

					lowerCamelCase_					:      Union[str, Any]     =		string_left[1:]

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

					if not isinstance(_lowercase			,       _lowercase				):
										lowerCamelCase_					:      Tuple     =		prefix + '''.''' + new_layer + '''.''' + string_left
					else:
										lowerCamelCase_					:      str     =		[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
					return new_string




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

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

										lowerCamelCase_					:      List[Any]     =		rename(_lowercase				)

										# check if we need to transform from Conv => Linear for attention
										if isinstance(_lowercase			,       _lowercase				):
															lowerCamelCase_					:      Any     =		transform_conv_attns(_lowercase			,       _lowercase			,       _lowercase				)
										else:
															lowerCamelCase_					:      List[Any]     =		v

					return new_state_dict




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Dict:
					'''simple docstring'''

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




def lowercase_	(   _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)

					lowerCamelCase_					:      Tuple     =		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()}"""
										lowerCamelCase_					:      Tuple     =		download(_lowercase				)

					lowerCamelCase_					:      Optional[int]     =		MODELS_MAP[model_name]['''sample_rate''']
					lowerCamelCase_					:      Any     =		MODELS_MAP[model_name]['''sample_size''']

					lowerCamelCase_					:      Optional[Any]     =		Object()
					lowerCamelCase_					:      List[str]     =		sample_size
					lowerCamelCase_					:      List[Any]     =		sample_rate
					lowerCamelCase_					:      Tuple     =		0

					lowerCamelCase_					:      Union[str, Any]     =		UNetaDModel(sample_size=_lowercase			,       sample_rate=_lowercase				)
					lowerCamelCase_					:      int     =		diffusers_model.state_dict()

					lowerCamelCase_					:      int     =		DiffusionUncond(_lowercase				)
					orig_model.load_state_dict(torch.load(args.model_path			,       map_location=_lowercase				)['''state_dict''']				)
					lowerCamelCase_					:      List[str]     =		orig_model.diffusion_ema.eval()
					lowerCamelCase_					:      str     =		orig_model.state_dict()
					lowerCamelCase_					:      Tuple     =		rename_orig_weights(_lowercase				)

					lowerCamelCase_					:      List[Any]     =		set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
					lowerCamelCase_					:      Dict     =		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":
															lowerCamelCase_					:      Dict     =		value.squeeze()

										lowerCamelCase_					:      Any     =		value

					diffusers_model.load_state_dict(_lowercase				)

					lowerCamelCase_					:      int     =		100
					lowerCamelCase_					:      List[str]     =		33

					lowerCamelCase_					:      Optional[Any]     =		IPNDMScheduler(num_train_timesteps=_lowercase				)

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

					lowerCamelCase_					:      Union[str, Any]     =		torch.linspace(1			,       0			,       steps + 1			,       device=_lowercase				)[:-1]
					lowerCamelCase_					:      Optional[Any]     =		get_crash_schedule(_lowercase				)

					lowerCamelCase_					:      List[Any]     =		DanceDiffusionPipeline(unet=_lowercase			,       scheduler=_lowercase				)

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

					lowerCamelCase_					:      str     =		sampling.iplms_sample(_lowercase			,       _lowercase			,       _lowercase			,       {}				)
					lowerCamelCase_					:      Any     =		generated.clamp(-1			,       1				)

					lowerCamelCase_					:      List[str]     =		(generated - audio).abs().sum()
					lowerCamelCase_					:      List[str]     =		(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__":
					__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							:   List[Any]					=     parser.parse_args()

					main(args)




'''simple docstring'''


def lowercase_	(   _lowercase = 1_000				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		-1
					lowerCamelCase_					:      Optional[Any]     =		0
					for a in range(1			,       n // 3				):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowerCamelCase_					:      Any     =		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowerCamelCase_					:      int     =		n - a - b
										if c * c == (a * a + b * b):
															lowerCamelCase_					:      int     =		a * b * c
															if candidate >= product:
																				lowerCamelCase_					:      Any     =		candidate
					return product


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


