



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      :							Dict							       ={
    "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       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase):
					return torch.atana(__lowerCamelCase      ,		__lowerCamelCase) / math.pi * 2



def       _lowerCAmelCase			(_lowerCAmelCase):
					UpperCamelCase_						     =      torch.sin(t * math.pi / 2) ** 2
					UpperCamelCase_						     =      (1 - sigma**2) ** 0.5
					return alpha_sigma_to_t(__lowerCamelCase      ,		__lowerCamelCase)


class 					_lowercase      (a_					):
				'''simple docstring'''







				pass




class 					_lowercase      (nn.Module					):
				'''simple docstring'''







				def __init__(       self   ,       snake_case__			):

									'''simple docstring'''


									super().__init__()

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




def       _lowerCAmelCase			(_lowerCAmelCase):
					UpperCamelCase_						     =      MODELS_MAP[model_name]["url"]
					os.system(f"""wget {url} ./""")

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


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

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

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



def       _lowerCAmelCase			(_lowerCAmelCase):
					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			(_lowerCAmelCase):
					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       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase=13):
					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(__lowerCamelCase) < 7:
										UpperCamelCase_						     =      DOWN_NUM_TO_LAYER[layer_num]
										UpperCamelCase_						     =      f"""down_blocks.{depth}"""
					elif depth > 0 and int(__lowerCamelCase) > 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(__lowerCamelCase) > 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(__lowerCamelCase)
					elif "attentions" in new_layer:
										UpperCamelCase_						     =      convert_attn_naming(__lowerCamelCase)
										UpperCamelCase_						     =      new_string_left

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



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

										UpperCamelCase_						     =      rename(__lowerCamelCase)

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

					return new_state_dict



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase):
					if len(__lowerCamelCase) == 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       _lowerCAmelCase			(_lowerCAmelCase):
					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(__lowerCamelCase)

					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=__lowerCamelCase      ,		sample_rate=__lowerCamelCase)
					UpperCamelCase_						     =      diffusers_model.state_dict()

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

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

										UpperCamelCase_						     =      value

					diffusers_model.load_state_dict(__lowerCamelCase)

					UpperCamelCase_						     =      1_00
					UpperCamelCase_						     =      33

					UpperCamelCase_						     =      IPNDMScheduler(num_train_timesteps=__lowerCamelCase)

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

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

					UpperCamelCase_						     =      DanceDiffusionPipeline(unet=__lowerCamelCase      ,		scheduler=__lowerCamelCase)

					UpperCamelCase_						     =      torch.manual_seed(33)
					UpperCamelCase_						     =      pipe(num_inference_steps=__lowerCamelCase      ,		generator=__lowerCamelCase).audios

					UpperCamelCase_						     =      sampling.iplms_sample(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		{})
					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"      ,		__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__":
	UpperCAmelCase      :							List[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      :							Any							       =parser.parse_args()

	main(args)






def 		__A		(	__lowerCamelCase    )      ->       int:
	a										=							hex_num.strip()
	if not hex_num:
		raise ValueError("""No value was passed to the function"""    )

	a										=							hex_num[0] == """-"""
	if is_negative:
		a										=							hex_num[1:]

	try:
		a										=							int(__lowerCamelCase							,			16    )
	except ValueError:
		raise ValueError("""Invalid value was passed to the function"""    )

	a										=							""""""
	while int_num > 0:
		a										=							str(int_num % 2    ) + bin_str
		int_num >>= 1

	return int(("""-""" + bin_str) if is_negative else bin_str    )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

