
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				={
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 1_3_1_0_7_2,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
}





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				return torch.atana(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_    ) / math.pi * 2





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			torch.sin(t * math.pi / 2    ) ** 2
				lowerCamelCase_						=			(1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_    )




class     _SCREAMING_SNAKE_CASE     (      lowerCamelCase_						):
	pass




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase )				->       int:
					super().__init__()

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





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			MODELS_MAP[model_name]["url"]
				os.system(F'wget {url} ./'    )

				return F'./{model_name}.ckpt'


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

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

__A				={
    '''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(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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=1_3    ):
				lowerCamelCase_						=			input_string

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

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

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

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

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

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

				lowerCamelCase_						=			string_left[1:]

				if "resnets" in new_layer:
								lowerCamelCase_						=			convert_resconv_naming(SCREAMING_SNAKE_CASE_    )
				elif "attentions" in new_layer:
								lowerCamelCase_						=			convert_attn_naming(SCREAMING_SNAKE_CASE_    )
								lowerCamelCase_						=			new_string_left

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





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

								lowerCamelCase_						=			rename(SCREAMING_SNAKE_CASE_    )

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

				return new_state_dict





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





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			torch.device("cuda" if torch.cuda.is_available() else "cpu"    )

				lowerCamelCase_						=			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()}'
								lowerCamelCase_						=			download(SCREAMING_SNAKE_CASE_    )

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

				lowerCamelCase_						=			Object()
				lowerCamelCase_						=			sample_size
				lowerCamelCase_						=			sample_rate
				lowerCamelCase_						=			0

				lowerCamelCase_						=			UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_	, sample_rate=SCREAMING_SNAKE_CASE_    )
				lowerCamelCase_						=			diffusers_model.state_dict()

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

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

								lowerCamelCase_						=			value

				diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_    )

				lowerCamelCase_						=			1_0_0
				lowerCamelCase_						=			3_3

				lowerCamelCase_						=			IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

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

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

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

				lowerCamelCase_						=			torch.manual_seed(3_3    )
				lowerCamelCase_						=			pipe(num_inference_steps=SCREAMING_SNAKE_CASE_	, generator=SCREAMING_SNAKE_CASE_    ).audios

				lowerCamelCase_						=			sampling.iplms_sample(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, {}    )
				lowerCamelCase_						=			generated.clamp(-1	, 1    )

				lowerCamelCase_						=			(generated - audio).abs().sum()
				lowerCamelCase_						=			(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				=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				=parser.parse_args()

			main(args)






"""simple docstring"""




from __future__ import annotations


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						list[int]:
						return [ord(SCREAMING_SNAKE_CASE_  ) - 96 for elem in plain]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list[int]  )   ->						str:
						return "".join(chr(elem + 96  ) for elem in encoded  )


def     lowercase			()   ->						None:
						SCREAMING_SNAKE_CASE													=					encode(input('-> '  ).strip().lower()  )
						print('Encoded: ' ,			SCREAMING_SNAKE_CASE_  )
						print('Decoded:' ,			decode(SCREAMING_SNAKE_CASE_  )  )


if __name__ == "__main__":
	main()


