

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": 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 					_lowercase     (				a_				:					Dict					,a_				:					Optional[int]				)				->	int:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Any				)				->	List[Any]:




					'''simple docstring'''







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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	pass




class 				__UpperCamelCase				(					nn.Module			):







	def __init__(   self: str  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						super().__init__()

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




def 					_lowercase     (				a_				:					Any				)				->	int:




					'''simple docstring'''







					__magic_name__							=    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 					_lowercase     (				a_				:					Tuple				)				->	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 					_lowercase     (				a_				:					List[Any]				)				->	Optional[Any]:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					List[str]					,a_				:					Dict=1_3				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    input_string

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

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

					while string.startswith('main.7.'				):
										depth += 1
										__magic_name__							=    string[7:]

					if string.startswith('main.'				):
										__magic_name__							=    string[5:]

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

					if depth == max_depth:
										__magic_name__							=    MID_NUM_TO_LAYER[layer_num]
										__magic_name__							=    'mid_block'
					elif depth > 0 and int(a_				) < 7:
										__magic_name__							=    DOWN_NUM_TO_LAYER[layer_num]
										__magic_name__							=    F'down_blocks.{depth}'
					elif depth > 0 and int(a_				) > 7:
										__magic_name__							=    UP_NUM_TO_LAYER[layer_num]
										__magic_name__							=    F'up_blocks.{max_depth - depth - 1}'
					elif depth == 0:
										__magic_name__							=    DEPTH_0_TO_LAYER[layer_num]
										__magic_name__							=    F'up_blocks.{max_depth - 1}' if int(a_				) > 3 else 'down_blocks.0'

					if not string_left.startswith('.'				):
										raise ValueError(F'Naming error with {input_string} and string_left: {string_left}.'				)

					__magic_name__							=    string_left[1:]

					if "resnets" in new_layer:
										__magic_name__							=    convert_resconv_naming(a_				)
					elif "attentions" in new_layer:
										__magic_name__							=    convert_attn_naming(a_				)
										__magic_name__							=    new_string_left

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




def 					_lowercase     (				a_				:					Optional[int]				)				->	Optional[int]:




					'''simple docstring'''







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

										__magic_name__							=    rename(a_				)

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

					return new_state_dict




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					Any					,a_				:					Tuple				)				->	List[Any]:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Dict				)				->	int:




					'''simple docstring'''







					__magic_name__							=    torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)

					__magic_name__							=    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()}'
										__magic_name__							=    download(a_				)

					__magic_name__							=    MODELS_MAP[model_name]['sample_rate']
					__magic_name__							=    MODELS_MAP[model_name]['sample_size']

					__magic_name__							=    Object()
					__magic_name__							=    sample_size
					__magic_name__							=    sample_rate
					__magic_name__							=    0

					__magic_name__							=    UNetaDModel(sample_size=a_					,sample_rate=a_				)
					__magic_name__							=    diffusers_model.state_dict()

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

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

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

										__magic_name__							=    value

					diffusers_model.load_state_dict(a_				)

					__magic_name__							=    1_0_0
					__magic_name__							=    3_3

					__magic_name__							=    IPNDMScheduler(num_train_timesteps=a_				)

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

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

					__magic_name__							=    DanceDiffusionPipeline(unet=a_					,scheduler=a_				)

					__magic_name__							=    torch.manual_seed(3_3				)
					__magic_name__							=    pipe(num_inference_steps=a_					,generator=a_				).audios

					__magic_name__							=    sampling.iplms_sample(a_					,a_					,a_					,{}				)
					__magic_name__							=    generated.clamp(-1					,1				)

					__magic_name__							=    (generated - audio).abs().sum()
					__magic_name__							=    (generated - audio).abs().max()

					if args.save:
										pipe.save_pretrained(args.checkpoint_path				)

					print('Diff sum'					,a_				)
					print('Diff max'					,a_				)

					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)







import requests
from bsa import BeautifulSoup




def 					_lowercase     (				a_				:					str = "https://www.worldometers.info/coronavirus"				)				->	dict:




					'''simple docstring'''







					__magic_name__							=    BeautifulSoup(requests.get(a_				).text					,'html.parser'				)
					__magic_name__							=    soup.findAll('h1'				)
					__magic_name__							=    soup.findAll('div'					,{'class': 'maincounter-number'}				)
					keys += soup.findAll('span'					,{'class': 'panel-title'}				)
					values += soup.findAll('div'					,{'class': 'number-table-main'}				)
					return {key.text.strip(): value.text.strip() for key, value in zip(a_					,a_				)}


if __name__ == "__main__":
						print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
						for key, value in world_covidaa_stats().items():
												print(f'''{key}\n{value}\n''')




