
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


A		: Dict								=     {
    '''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__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Dict:
						return torch.atana(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ) / math.pi * 2




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     int:
						_lowercase					       =					torch.sin(t * math.pi / 2       ) ** 2
						_lowercase					       =					(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )



class    a_ (			_a    ):
			pass







class    a_ (			nn.Module    ):






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

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







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     Tuple:
						_lowercase					       =					MODELS_MAP[model_name]["""url"""]
						os.system(f"""wget {url} ./"""       )

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


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

A		: List[str]								=     {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     Tuple:
						for key, value in ATTN_MAP.items():
												if name.startswith(SCREAMING_SNAKE_CASE_       ) and not isinstance(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ):
																		return name.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												elif name.startswith(SCREAMING_SNAKE_CASE_       ):
																		return [name.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ) for v in value]
						raise ValueError(f"""Attn error with {name}"""       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	str=13       )  ->     Tuple:
						_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(SCREAMING_SNAKE_CASE_       ) < 7:
												_lowercase					       =					DOWN_NUM_TO_LAYER[layer_num]
												_lowercase					       =					f"""down_blocks.{depth}"""
						elif depth > 0 and int(SCREAMING_SNAKE_CASE_       ) > 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(SCREAMING_SNAKE_CASE_       ) > 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(SCREAMING_SNAKE_CASE_       )
						elif "attentions" in new_layer:
												_lowercase					       =					convert_attn_naming(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					new_string_left

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str       )  ->     List[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(SCREAMING_SNAKE_CASE_       )

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

						return new_state_dict




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Optional[int]:
						if len(SCREAMING_SNAKE_CASE_       ) == 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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_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(SCREAMING_SNAKE_CASE_       )

						_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=SCREAMING_SNAKE_CASE_		,   sample_rate=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					diffusers_model.state_dict()

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

						_lowercase					       =					set(renamed_state_dict.keys()       ) - set(diffusers_state_dict.keys()       )
						_lowercase					       =					set(diffusers_state_dict.keys()       ) - set(renamed_state_dict.keys()       )

						assert len(SCREAMING_SNAKE_CASE_       ) == 0, f"""Problem with {renamed_minus_diffusers}"""
						assert all(k.endswith("""kernel"""       ) for k in list(SCREAMING_SNAKE_CASE_       )       ), 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(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					1_00
						_lowercase					       =					33

						_lowercase					       =					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_       )

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

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

						_lowercase					       =					DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_		,   scheduler=SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					torch.manual_seed(33       )
						_lowercase					       =					pipe(num_inference_steps=SCREAMING_SNAKE_CASE_		,   generator=SCREAMING_SNAKE_CASE_       ).audios

						_lowercase					       =					sampling.iplms_sample(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   {}       )
						_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"""		,   SCREAMING_SNAKE_CASE_       )
						print("""Diff max"""		,   SCREAMING_SNAKE_CASE_       )

						assert diff_max < 1E-3, f"""Diff max: {diff_max} is too much :-/"""

						print(f"""Conversion for {model_name} successful!"""       )


if __name__ == "__main__":
		A		: 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.''')
		A		: Any								=     parser.parse_args()

		main(args)

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[int]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     bool:
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[[False] * (required_sum + 1) for _ in range(arr_len + 1       )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1       ):
												_lowercase					       =					True

						# sum is not zero and set is empty then false
						for i in range(1		,   required_sum + 1       ):
												_lowercase					       =					False

						for i in range(1		,   arr_len + 1       ):
												for j in range(1		,   required_sum + 1       ):
																		if arr[i - 1] > j:
																								_lowercase					       =					subset[i - 1][j]
																		if arr[i - 1] <= j:
																								_lowercase					       =					subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()