
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__							:							Dict									=			{
    "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 	A_ (							snake_case							:   List[str] ,      snake_case							:   Optional[Any] )      ->    Tuple:



						'''simple docstring'''

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







def 	A_ (							snake_case							:   Optional[int] )      ->    Any:



						'''simple docstring'''

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							pass






class        SCREAMING_SNAKE_CASE__    (							nn.Module    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

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







def 	A_ (							snake_case							:   Union[str, Any] )      ->    List[str]:



						'''simple docstring'''

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

						return f"./{model_name}.ckpt"


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

lowercase__							:							Union[str, Any]									=			{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

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







def 	A_ (							snake_case							:   str )      ->    Optional[int]:



						'''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 	A_ (							snake_case							:   Tuple )      ->    Dict:



						'''simple docstring'''

						for key, value in ATTN_MAP.items():
												if name.startswith(snake_case ) and not isinstance(snake_case ,      snake_case ):
																		return name.replace(snake_case ,      snake_case )
												elif name.startswith(snake_case ):
																		return [name.replace(snake_case ,      snake_case ) for v in value]
						raise ValueError(f"Attn error with {name}" )







def 	A_ (							snake_case							:   Union[str, Any] ,      snake_case							:   Optional[Any]=13 )      ->    Optional[int]:



						'''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(snake_case ) < 7:
												__UpperCamelCase       =			DOWN_NUM_TO_LAYER[layer_num]
												__UpperCamelCase       =			f"down_blocks.{depth}"
						elif depth > 0 and int(snake_case ) > 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(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}." )

						__UpperCamelCase       =			string_left[1:]

						if "resnets" in new_layer:
												__UpperCamelCase       =			convert_resconv_naming(snake_case )
						elif "attentions" in new_layer:
												__UpperCamelCase       =			convert_attn_naming(snake_case )
												__UpperCamelCase       =			new_string_left

						if not isinstance(snake_case ,      snake_case ):
												__UpperCamelCase       =			prefix + '''.''' + new_layer + '''.''' + string_left
						else:
												__UpperCamelCase       =			[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
						return new_string







def 	A_ (							snake_case							:   Tuple )      ->    Optional[int]:



						'''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(snake_case )

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

						return new_state_dict







def 	A_ (							snake_case							:   str ,      snake_case							:   Dict ,      snake_case							:   List[Any] )      ->    Tuple:



						'''simple docstring'''

						if len(snake_case ) == 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 	A_ (							snake_case							:   Tuple )      ->    List[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(snake_case )

						__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=snake_case ,      sample_rate=snake_case )
						__UpperCamelCase       =			diffusers_model.state_dict()

						__UpperCamelCase       =			DiffusionUncond(snake_case )
						orig_model.load_state_dict(torch.load(args.model_path ,      map_location=snake_case )['''state_dict'''] )
						__UpperCamelCase       =			orig_model.diffusion_ema.eval()
						__UpperCamelCase       =			orig_model.state_dict()
						__UpperCamelCase       =			rename_orig_weights(snake_case )

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

						assert len(snake_case ) == 0, f"Problem with {renamed_minus_diffusers}"
						assert all(k.endswith('''kernel''' ) for k in list(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":
																		__UpperCamelCase       =			value.squeeze()

												__UpperCamelCase       =			value

						diffusers_model.load_state_dict(snake_case )

						__UpperCamelCase       =			100
						__UpperCamelCase       =			33

						__UpperCamelCase       =			IPNDMScheduler(num_train_timesteps=snake_case )

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

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

						__UpperCamelCase       =			DanceDiffusionPipeline(unet=snake_case ,      scheduler=snake_case )

						__UpperCamelCase       =			torch.manual_seed(33 )
						__UpperCamelCase       =			pipe(num_inference_steps=snake_case ,      generator=snake_case ).audios

						__UpperCamelCase       =			sampling.iplms_sample(snake_case ,      snake_case ,      snake_case ,      {} )
						__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''' ,      snake_case )
						print('''Diff max''' ,      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__":
				lowercase__							:							Tuple									=			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__							:							Optional[Any]									=			parser.parse_args()

				main(args)






def 	A_ (							snake_case							:   int = 10 ,      snake_case							:   int = 1000 ,      snake_case							:   bool = True )      ->    int:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError('''Invalid value for min_val or max_val (min_value < max_value)''' )
						return min_val if option else max_val




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



						'''simple docstring'''

						return int((number_a + number_a) / 2 )




def 	A_ (							snake_case							:   int ,      snake_case							:   int ,      snake_case							:   int )      ->    None:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case )
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError('''argument value for lower and higher must be(lower > higher)''' )

						if not lower < to_guess < higher:
												raise ValueError(
												    '''guess value must be within the range of lower and higher value''' )

						def answer(snake_case							:   int ) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print('''started...''' )

						__UpperCamelCase       =			lower
						__UpperCamelCase       =			higher

						__UpperCamelCase       =			[]

						while True:
												__UpperCamelCase       =			get_avg(snake_case ,      snake_case )
												last_numbers.append(snake_case )

												if answer(snake_case ) == "low":
																		__UpperCamelCase       =			number
												elif answer(snake_case ) == "high":
																		__UpperCamelCase       =			number
												else:
																		break

						print(f"guess the number : {last_numbers[-1]}" )
						print(f"details : {last_numbers!s}" )




def 	A_ (							)      ->    None:



						'''simple docstring'''

						__UpperCamelCase       =			int(input('''Enter lower value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter high value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter value to guess : ''' ).strip() )
						guess_the_number(snake_case ,      snake_case ,      snake_case )


if __name__ == "__main__":
				main()




