



'''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_   :					Union[str, Any]        =     {
    "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   SCREAMING_SNAKE_CASE					(					lowercase_     :   int			,	lowercase_     :   Tuple    ):
						return torch.atana(lowercase_			,	lowercase_    ) / math.pi * 2







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   str    ):
						lowercase   =				torch.sin(t * math.pi / 2    ) ** 2
						lowercase   =				(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(lowercase_			,	lowercase_    )




class   __UpperCamelCase							(a_      ):
	pass




class   __UpperCamelCase							(nn.Module      ):







	def __init__(     self			,					_lowerCAmelCase    )     ->     List[Any]:
							'''simple docstring'''






							super().__init__()

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







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Any    ):
						lowercase   =				MODELS_MAP[model_name]["""url"""]
						os.system(F"""wget {url} ./"""    )

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


lowercase_   :					Union[str, Any]        =     {
    "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_   :					Union[str, 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_   :					Dict        =     {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

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

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







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   str    ):


						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   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[Any]    ):


						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   SCREAMING_SNAKE_CASE					(					lowercase_     :   str			,	lowercase_     :   Union[str, Any]=13    ):
						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(lowercase_    ) < 7:
												lowercase   =				DOWN_NUM_TO_LAYER[layer_num]
												lowercase   =				F"""down_blocks.{depth}"""
						elif depth > 0 and int(lowercase_    ) > 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(lowercase_    ) > 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(lowercase_    )
						elif "attentions" in new_layer:
												lowercase   =				convert_attn_naming(lowercase_    )
												lowercase   =				new_string_left

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







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

												lowercase   =				rename(lowercase_    )

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

						return new_state_dict







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   int			,	lowercase_     :   int			,	lowercase_     :   Optional[int]    ):


						if len(lowercase_    ) == 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   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[int]    ):
						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(lowercase_    )

						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=lowercase_			,	sample_rate=lowercase_    )
						lowercase   =				diffusers_model.state_dict()

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

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

												lowercase   =				value

						diffusers_model.load_state_dict(lowercase_    )

						lowercase   =				100
						lowercase   =				33

						lowercase   =				IPNDMScheduler(num_train_timesteps=lowercase_    )

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

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

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

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

						lowercase   =				sampling.iplms_sample(lowercase_			,	lowercase_			,	lowercase_			,	{}    )
						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"""			,	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_   :					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.''')
						lowercase_   :					List[str]        =     parser.parse_args()

						main(args)





'''simple docstring'''



def       snake_case   (	snake_case      : int					,		snake_case      : int					,		snake_case      : int			) ->  int:


    """simple docstring"""


    if exponent == 1:
        return base
    if exponent % 2 == 0:
        lowerCAmelCase 						=							_modexpt(snake_case					,		exponent // 2					,		snake_case			) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(snake_case					,		exponent - 1					,		snake_case			)) % modulo_value


def       snake_case   (	snake_case      : int = 1777					,		snake_case      : int = 1855					,		snake_case      : int = 8			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							base
    for _ in range(1					,		snake_case			):
        lowerCAmelCase 						=							_modexpt(snake_case					,		snake_case					,		10**digits			)

    return result


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





