




'''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


_lowerCAmelCase      	=					{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 131072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
}

def 			__lowerCAmelCase					(    snake_case__					,      snake_case__						):
			return torch.atana(snake_case__					,      snake_case__						) / math.pi * 2

def 			__lowerCAmelCase					(    snake_case__						):
			__UpperCamelCase      :			str									=   torch.sin(t * math.pi / 2						) ** 2
			__UpperCamelCase      :			Tuple									=   (1 - sigma**2) ** 0.5
			return alpha_sigma_to_t(snake_case__					,      snake_case__						)



class 					A		( snake_case_		):




				'''simple docstring'''





				pass







class 					A		( nn.Module		):




				'''simple docstring'''





				def __init__(self ,  _UpperCAmelCase			)	->       Optional[int]:
							super().__init__()

							__UpperCamelCase      :			Optional[Any]									=   DiffusionAttnUnetaD(UpperCamelCase__ ,  n_attn_layers=4			)
							__UpperCamelCase      :			Union[str, Any]									=   deepcopy(self.diffusion			)
							__UpperCamelCase      :			Tuple									=   torch.quasirandom.SobolEngine(1 ,  scramble=UpperCamelCase__			)







def 			__lowerCAmelCase					(    snake_case__						):
			__UpperCamelCase      :			List[str]									=   MODELS_MAP[model_name]["url"]
			os.system(F"wget {url} ./"						)

			return F"./{model_name}.ckpt"


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

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

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

def 			__lowerCAmelCase					(    snake_case__						):





			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					(    snake_case__						):





			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 			__lowerCAmelCase					(    snake_case__					,      snake_case__=13						):
			__UpperCamelCase      :			int									=   input_string

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

			__UpperCamelCase      :			int									=   0
			if string.startswith("net.3."						):
						depth += 1
						__UpperCamelCase      :			List[str]									=   string[6:]
			elif string.startswith("net."						):
						__UpperCamelCase      :			Optional[Any]									=   string[4:]

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

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

			# mid block
			if string[:2].isdigit():
						__UpperCamelCase      :			Optional[Any]									=   string[:2]
						__UpperCamelCase      :			List[str]									=   string[2:]
			else:
						__UpperCamelCase      :			Optional[int]									=   string[0]
						__UpperCamelCase      :			Union[str, Any]									=   string[1:]

			if depth == max_depth:
						__UpperCamelCase      :			Optional[Any]									=   MID_NUM_TO_LAYER[layer_num]
						__UpperCamelCase      :			Tuple									=   "mid_block"
			elif depth > 0 and int(snake_case__						) < 7:
						__UpperCamelCase      :			List[str]									=   DOWN_NUM_TO_LAYER[layer_num]
						__UpperCamelCase      :			Dict									=   F"down_blocks.{depth}"
			elif depth > 0 and int(snake_case__						) > 7:
						__UpperCamelCase      :			Union[str, Any]									=   UP_NUM_TO_LAYER[layer_num]
						__UpperCamelCase      :			List[Any]									=   F"up_blocks.{max_depth - depth - 1}"
			elif depth == 0:
						__UpperCamelCase      :			Optional[int]									=   DEPTH_0_TO_LAYER[layer_num]
						__UpperCamelCase      :			List[Any]									=   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      :			Tuple									=   string_left[1:]

			if "resnets" in new_layer:
						__UpperCamelCase      :			Union[str, Any]									=   convert_resconv_naming(snake_case__						)
			elif "attentions" in new_layer:
						__UpperCamelCase      :			Union[str, Any]									=   convert_attn_naming(snake_case__						)
						__UpperCamelCase      :			List[str]									=   new_string_left

			if not isinstance(snake_case__					,      snake_case__						):
						__UpperCamelCase      :			List[Any]									=   prefix + "." + new_layer + "." + string_left
			else:
						__UpperCamelCase      :			Any									=   [prefix + "." + new_layer + "." + s for s in string_left]
			return new_string

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

						__UpperCamelCase      :			int									=   rename(snake_case__						)

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

			return new_state_dict

def 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__						):





			if len(snake_case__						) == 1:
						if len(v.shape						) == 3:
									# weight
									__UpperCamelCase      :			Dict									=   v[:, :, 0]
						else:
									# bias
									__UpperCamelCase      :			Dict									=   v
			else:
						# qkv matrices
						__UpperCamelCase      :			str									=   v.shape[0]
						__UpperCamelCase      :			int									=   trippled_shape // 3
						for i in range(3						):
									if len(v.shape						) == 3:
												__UpperCamelCase      :			int									=   v[i * single_shape : (i + 1) * single_shape, :, 0]
									else:
												__UpperCamelCase      :			List[str]									=   v[i * single_shape : (i + 1) * single_shape]
			return new_state_dict

def 			__lowerCAmelCase					(    snake_case__						):
			__UpperCamelCase      :			int									=   torch.device("cuda" if torch.cuda.is_available() else "cpu"						)

			__UpperCamelCase      :			Optional[int]									=   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      :			int									=   download(snake_case__						)

			__UpperCamelCase      :			Optional[int]									=   MODELS_MAP[model_name]["sample_rate"]
			__UpperCamelCase      :			List[str]									=   MODELS_MAP[model_name]["sample_size"]

			__UpperCamelCase      :			List[str]									=   Object()
			__UpperCamelCase      :			Union[str, Any]									=   sample_size
			__UpperCamelCase      :			List[str]									=   sample_rate
			__UpperCamelCase      :			int									=   0

			__UpperCamelCase      :			List[str]									=   UNetaDModel(sample_size=snake_case__					,      sample_rate=snake_case__						)
			__UpperCamelCase      :			Optional[int]									=   diffusers_model.state_dict()

			__UpperCamelCase      :			List[Any]									=   DiffusionUncond(snake_case__						)
			orig_model.load_state_dict(torch.load(args.model_path					,      map_location=snake_case__						)["state_dict"]						)
			__UpperCamelCase      :			Any									=   orig_model.diffusion_ema.eval()
			__UpperCamelCase      :			str									=   orig_model.state_dict()
			__UpperCamelCase      :			str									=   rename_orig_weights(snake_case__						)

			__UpperCamelCase      :			List[Any]									=   set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
			__UpperCamelCase      :			Dict									=   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      :			int									=   value.squeeze()

						__UpperCamelCase      :			Dict									=   value

			diffusers_model.load_state_dict(snake_case__						)

			__UpperCamelCase      :			str									=   100
			__UpperCamelCase      :			Dict									=   33

			__UpperCamelCase      :			Any									=   IPNDMScheduler(num_train_timesteps=snake_case__						)

			__UpperCamelCase      :			Optional[int]									=   torch.manual_seed(snake_case__						)
			__UpperCamelCase      :			List[Any]									=   torch.randn([1, 2, config.sample_size]					,      generator=snake_case__						).to(snake_case__						)

			__UpperCamelCase      :			Union[str, Any]									=   torch.linspace(1					,      0					,      steps + 1					,      device=snake_case__						)[:-1]
			__UpperCamelCase      :			str									=   get_crash_schedule(snake_case__						)

			__UpperCamelCase      :			str									=   DanceDiffusionPipeline(unet=snake_case__					,      scheduler=snake_case__						)

			__UpperCamelCase      :			Tuple									=   torch.manual_seed(33						)
			__UpperCamelCase      :			List[str]									=   pipe(num_inference_steps=snake_case__					,      generator=snake_case__						).audios

			__UpperCamelCase      :			Optional[int]									=   sampling.iplms_sample(snake_case__					,      snake_case__					,      snake_case__					,      {}						)
			__UpperCamelCase      :			Any									=   generated.clamp(-1					,      1						)

			__UpperCamelCase      :			List[Any]									=   (generated - audio).abs().sum()
			__UpperCamelCase      :			Optional[Any]									=   (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__":
				_lowerCAmelCase      	=					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.''')
				_lowerCAmelCase      	=					parser.parse_args()

				main(args)







"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[str]:


    """simple docstring"""





    snake_case    :      List[str]      					=    len(lowercase )
    for i in range(length - 1 ):
        snake_case    :      List[str]      					=    i
        for k in range(i + 1						,							lowercase ):
            if collection[k] < collection[least]:
                snake_case    :      List[str]      					=    k
        if least != i:
            snake_case					,snake_case    :      Union[str, Any]      					=    (collection[i], collection[least])
    return collection


if __name__ == "__main__":
  __snake_case   			=    input("""Enter numbers separated by a comma:\n""").strip()
  __snake_case   			=    [int(item) for item in user_input.split(""",""")]
  print(selection_sort(unsorted))





