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


_lowerCamelCase   :				Union[str, Any]     			=						{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 13_10_72,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
}





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Any					):
					return torch.atana(UpperCAmelCase__ ,	UpperCAmelCase__					) / math.pi * 2





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					SCREAMING_SNAKE_CASE           =    torch.sin(t * math.pi / 2					) ** 2
					SCREAMING_SNAKE_CASE           =    (1 - sigma**2) ** 0.5
					return alpha_sigma_to_t(UpperCAmelCase__ ,	UpperCAmelCase__					)


class   lowercase      (       a       ):
							pass



class   lowercase      (       nn.Module       ):




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



												'''simple docstring'''

												super().__init__()

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					SCREAMING_SNAKE_CASE           =    MODELS_MAP[model_name]["url"]
					os.system(F"wget {url} ./"					)

					return F"./{model_name}.ckpt"


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

_lowerCamelCase   :				Optional[int]     			=						{
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

_lowerCamelCase   :				Optional[int]     			=						{
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict					):
					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__       :							str					):
					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__       :							str ,	UpperCAmelCase__       :							List[str]=1_3					):
					SCREAMING_SNAKE_CASE           =    input_string

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

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

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

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

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

					if depth == max_depth:
										SCREAMING_SNAKE_CASE           =    MID_NUM_TO_LAYER[layer_num]
										SCREAMING_SNAKE_CASE           =    "mid_block"
					elif depth > 0 and int(UpperCAmelCase__					) < 7:
										SCREAMING_SNAKE_CASE           =    DOWN_NUM_TO_LAYER[layer_num]
										SCREAMING_SNAKE_CASE           =    F"down_blocks.{depth}"
					elif depth > 0 and int(UpperCAmelCase__					) > 7:
										SCREAMING_SNAKE_CASE           =    UP_NUM_TO_LAYER[layer_num]
										SCREAMING_SNAKE_CASE           =    F"up_blocks.{max_depth - depth - 1}"
					elif depth == 0:
										SCREAMING_SNAKE_CASE           =    DEPTH_0_TO_LAYER[layer_num]
										SCREAMING_SNAKE_CASE           =    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}."					)

					SCREAMING_SNAKE_CASE           =    string_left[1:]

					if "resnets" in new_layer:
										SCREAMING_SNAKE_CASE           =    convert_resconv_naming(UpperCAmelCase__					)
					elif "attentions" in new_layer:
										SCREAMING_SNAKE_CASE           =    convert_attn_naming(UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    new_string_left

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





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

										SCREAMING_SNAKE_CASE           =    rename(UpperCAmelCase__					)

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

					return new_state_dict





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Tuple					):
					if len(UpperCAmelCase__					) == 1:
										if len(v.shape					) == 3:
															# weight
															SCREAMING_SNAKE_CASE           =    v[:, :, 0]
										else:
															# bias
															SCREAMING_SNAKE_CASE           =    v
					else:
										# qkv matrices
										SCREAMING_SNAKE_CASE           =    v.shape[0]
										SCREAMING_SNAKE_CASE           =    trippled_shape // 3
										for i in range(3					):
															if len(v.shape					) == 3:
																				SCREAMING_SNAKE_CASE           =    v[i * single_shape : (i + 1) * single_shape, :, 0]
															else:
																				SCREAMING_SNAKE_CASE           =    v[i * single_shape : (i + 1) * single_shape]
					return new_state_dict





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict					):
					SCREAMING_SNAKE_CASE           =    torch.device("cuda" if torch.cuda.is_available() else "cpu"					)

					SCREAMING_SNAKE_CASE           =    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()}"
										SCREAMING_SNAKE_CASE           =    download(UpperCAmelCase__					)

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

					SCREAMING_SNAKE_CASE           =    Object()
					SCREAMING_SNAKE_CASE           =    sample_size
					SCREAMING_SNAKE_CASE           =    sample_rate
					SCREAMING_SNAKE_CASE           =    0

					SCREAMING_SNAKE_CASE           =    UNetaDModel(sample_size=UpperCAmelCase__ ,	sample_rate=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    diffusers_model.state_dict()

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

					SCREAMING_SNAKE_CASE           =    set(renamed_state_dict.keys()					) - set(diffusers_state_dict.keys()					)
					SCREAMING_SNAKE_CASE           =    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":
															SCREAMING_SNAKE_CASE           =    value.squeeze()

										SCREAMING_SNAKE_CASE           =    value

					diffusers_model.load_state_dict(UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    1_0_0
					SCREAMING_SNAKE_CASE           =    3_3

					SCREAMING_SNAKE_CASE           =    IPNDMScheduler(num_train_timesteps=UpperCAmelCase__					)

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

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

					SCREAMING_SNAKE_CASE           =    DanceDiffusionPipeline(unet=UpperCAmelCase__ ,	scheduler=UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    torch.manual_seed(3_3					)
					SCREAMING_SNAKE_CASE           =    pipe(num_inference_steps=UpperCAmelCase__ ,	generator=UpperCAmelCase__					).audios

					SCREAMING_SNAKE_CASE           =    sampling.iplms_sample(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	{}					)
					SCREAMING_SNAKE_CASE           =    generated.clamp(-1 ,	1					)

					SCREAMING_SNAKE_CASE           =    (generated - audio).abs().sum()
					SCREAMING_SNAKE_CASE           =    (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__":
							_lowerCamelCase   :				Optional[int]     			=						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.''')
							_lowerCamelCase   :				int     			=						parser.parse_args()

							main(args)





from __future__ import annotations

from collections.abc import Iterator


class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   int )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    value
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None



class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   Node )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tree




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Node | None )   ->			int:



												'''simple docstring'''

												if node is None:
																	return 0
												return node.value + (
												    self.depth_first_search(node.left ) + self.depth_first_search(node.right )
												)





							def __iter__(  self				:   List[Any] )   ->			Iterator[int]:



												'''simple docstring'''

												yield self.depth_first_search(self.tree )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




