





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__           =			{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 131_072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
}





def      _UpperCamelCase  (a__ :Dict ,      a__ :List[str]							):






							"""simple docstring"""





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





def      _UpperCamelCase  (a__ :Optional[int]							):






							"""simple docstring"""





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

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							pass






class 							__SCREAMING_SNAKE_CASE       (  nn.Module							):






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

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






def      _UpperCamelCase  (a__ :Any							):






							"""simple docstring"""





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

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


UpperCamelCase__           =			{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
UpperCamelCase__           =			{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
UpperCamelCase__           =			{
    "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__           =			{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

UpperCamelCase__           =			{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

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





def      _UpperCamelCase  (a__ :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      _UpperCamelCase  (a__ :Optional[int]							):






							"""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      _UpperCamelCase  (a__ :List[str] ,      a__ :List[str]=13							):






							"""simple docstring"""





							UpperCamelCase__					  =      input_string

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

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

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

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

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

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

							UpperCamelCase__					  =      string_left[1:]

							if "resnets" in new_layer:
														UpperCamelCase__					  =      convert_resconv_naming(a__							)
							elif "attentions" in new_layer:
														UpperCamelCase__					  =      convert_attn_naming(a__							)
														UpperCamelCase__					  =      new_string_left

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





def      _UpperCamelCase  (a__ :Tuple							):






							"""simple docstring"""





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

														UpperCamelCase__					  =      rename(a__							)

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

							return new_state_dict





def      _UpperCamelCase  (a__ :Optional[int] ,      a__ :Optional[Any] ,      a__ :Tuple							):






							"""simple docstring"""





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





def      _UpperCamelCase  (a__ :Union[str, Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""							)

							UpperCamelCase__					  =      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()}"""
														UpperCamelCase__					  =      download(a__							)

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

							UpperCamelCase__					  =      Object()
							UpperCamelCase__					  =      sample_size
							UpperCamelCase__					  =      sample_rate
							UpperCamelCase__					  =      0

							UpperCamelCase__					  =      UNetaDModel(sample_size=a__ ,      sample_rate=a__							)
							UpperCamelCase__					  =      diffusers_model.state_dict()

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

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

														UpperCamelCase__					  =      value

							diffusers_model.load_state_dict(a__							)

							UpperCamelCase__					  =      100
							UpperCamelCase__					  =      33

							UpperCamelCase__					  =      IPNDMScheduler(num_train_timesteps=a__							)

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

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

							UpperCamelCase__					  =      DanceDiffusionPipeline(unet=a__ ,      scheduler=a__							)

							UpperCamelCase__					  =      torch.manual_seed(33							)
							UpperCamelCase__					  =      pipe(num_inference_steps=a__ ,      generator=a__							).audios

							UpperCamelCase__					  =      sampling.iplms_sample(a__ ,      a__ ,      a__ ,      {}							)
							UpperCamelCase__					  =      generated.clamp(-1 ,      1							)

							UpperCamelCase__					  =      (generated - audio).abs().sum()
							UpperCamelCase__					  =      (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__           =			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__           =			parser.parse_args()

					main(args)











# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")

class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = True	,			__lowerCAmelCase = False	):
														UpperCamelCase__					  =      scheduler
														UpperCamelCase__					  =      optimizers if isinstance(__lowerCAmelCase	,			(list, tuple)	) else [optimizers]
														UpperCamelCase__					  =      split_batches
														UpperCamelCase__					  =      step_with_optimizer
														UpperCamelCase__					  =      GradientState()






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														if not self.step_with_optimizer:
																					# No link between scheduler and optimizer -> just step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					return

														# Otherwise, first make sure the optimizer was stepped.
														if not self.gradient_state.sync_gradients:
																					if self.gradient_state.adjust_scheduler:
																												self.scheduler._step_count += 1
																					return

														for opt in self.optimizers:
																					if opt.step_was_skipped:
																												return
														if self.split_batches:
																					# Split batches -> the training dataloader batch size is not changed so one step per training step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														else:
																					# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																					# num_processes steps per training step
																					UpperCamelCase__					  =      AcceleratorState().num_processes
																					for _ in range(__lowerCAmelCase	):
																												# Special case when using OneCycle and `drop_last` was not used
																												if hasattr(self.scheduler	,			"""total_steps"""	):
																																			if self.scheduler._step_count <= self.scheduler.total_steps:
																																										self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												else:
																																			self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_last_lr()






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.state_dict()






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														self.scheduler.load_state_dict(__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_lr()







							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														return self.scheduler.print_lr(*__lowerCAmelCase	,			**__lowerCAmelCase	)




