




"""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


__A       						=	{
    '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__   (						lowercase__     :   Any				,   lowercase__     :   Tuple      ):
		return torch.atana(__snake_case				,   __snake_case      ) / math.pi * 2



def      UpperCamelCase__   (						lowercase__     :   int      ):
		snake_case						:  Any    	= torch.sin(t * math.pi / 2      ) ** 2
		snake_case						:  Optional[Any]    	= (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(__snake_case				,   __snake_case      )
class 	lowerCamelCase__			(     __SCREAMING_SNAKE_CASE	):
			pass







class 	lowerCamelCase__			(     nn.Module	):


			def __init__(   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__()

					snake_case						:  int    	= DiffusionAttnUnetaD(_SCREAMING_SNAKE_CASE ,       n_attn_layers=4							)
					snake_case						:  Union[str, Any]    	= deepcopy(self.diffusion							)
					snake_case						:  Any    	= torch.quasirandom.SobolEngine(1 ,       scramble=_SCREAMING_SNAKE_CASE							)







def      UpperCamelCase__   (						lowercase__     :   List[Any]      ):
		snake_case						:  Union[str, Any]    	= 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      UpperCamelCase__   (						lowercase__     :   Any      ):
		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__   (						lowercase__     :   List[str]      ):
		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      UpperCamelCase__   (						lowercase__     :   Union[str, Any]				,   lowercase__     :   Dict=13      ):
		snake_case						:  Optional[int]    	= input_string

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

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

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

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

		# mid block
		if string[:2].isdigit():
				snake_case						:  int    	= string[:2]
				snake_case						:  Dict    	= string[2:]
		else:
				snake_case						:  Tuple    	= string[0]
				snake_case						:  Any    	= string[1:]

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

		snake_case						:  Tuple    	= string_left[1:]

		if "resnets" in new_layer:
				snake_case						:  int    	= convert_resconv_naming(__snake_case      )
		elif "attentions" in new_layer:
				snake_case						:  Any    	= convert_attn_naming(__snake_case      )
				snake_case						:  List[str]    	= new_string_left

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



def      UpperCamelCase__   (						lowercase__     :   List[str]      ):
		snake_case						:  Optional[Any]    	= {}
		for k, v in state_dict.items():
				if k.endswith("kernel"      ):
						# up- and downsample layers, don't have trainable weights
						continue

				snake_case						:  str    	= rename(__snake_case      )

				# check if we need to transform from Conv => Linear for attention
				if isinstance(__snake_case				,   __snake_case      ):
						snake_case						:  List[str]    	= transform_conv_attns(__snake_case				,   __snake_case				,   __snake_case      )
				else:
						snake_case						:  List[Any]    	= v

		return new_state_dict



def      UpperCamelCase__   (						lowercase__     :   Tuple				,   lowercase__     :   Optional[Any]				,   lowercase__     :   Optional[Any]      ):
		if len(__snake_case      ) == 1:
				if len(v.shape      ) == 3:
						# weight
						snake_case						:  List[str]    	= v[:, :, 0]
				else:
						# bias
						snake_case						:  str    	= v
		else:
				# qkv matrices
				snake_case						:  Optional[int]    	= v.shape[0]
				snake_case						:  Any    	= trippled_shape // 3
				for i in range(3      ):
						if len(v.shape      ) == 3:
								snake_case						:  Tuple    	= v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								snake_case						:  int    	= v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict



def      UpperCamelCase__   (						lowercase__     :   Union[str, Any]      ):
		snake_case						:  int    	= torch.device("cuda" if torch.cuda.is_available() else "cpu"      )

		snake_case						:  Dict    	= 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()}'''
				snake_case						:  int    	= download(__snake_case      )

		snake_case						:  Dict    	= MODELS_MAP[model_name]["sample_rate"]
		snake_case						:  Optional[Any]    	= MODELS_MAP[model_name]["sample_size"]

		snake_case						:  int    	= Object()
		snake_case						:  str    	= sample_size
		snake_case						:  str    	= sample_rate
		snake_case						:  str    	= 0

		snake_case						:  Dict    	= UNetaDModel(sample_size=__snake_case				,   sample_rate=__snake_case      )
		snake_case						:  List[Any]    	= diffusers_model.state_dict()

		snake_case						:  Union[str, Any]    	= DiffusionUncond(__snake_case      )
		orig_model.load_state_dict(torch.load(args.model_path				,   map_location=__snake_case      )["state_dict"]      )
		snake_case						:  Any    	= orig_model.diffusion_ema.eval()
		snake_case						:  Any    	= orig_model.state_dict()
		snake_case						:  int    	= rename_orig_weights(__snake_case      )

		snake_case						:  List[Any]    	= set(renamed_state_dict.keys()      ) - set(diffusers_state_dict.keys()      )
		snake_case						:  Union[str, Any]    	= 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":
						snake_case						:  int    	= value.squeeze()

				snake_case						:  Dict    	= value

		diffusers_model.load_state_dict(__snake_case      )

		snake_case						:  List[str]    	= 100
		snake_case						:  Dict    	= 33

		snake_case						:  Tuple    	= IPNDMScheduler(num_train_timesteps=__snake_case      )

		snake_case						:  Union[str, Any]    	= torch.manual_seed(__snake_case      )
		snake_case						:  int    	= torch.randn([1, 2, config.sample_size]				,   generator=__snake_case      ).to(__snake_case      )

		snake_case						:  List[str]    	= torch.linspace(1				,   0				,   steps + 1				,   device=__snake_case      )[:-1]
		snake_case						:  Optional[int]    	= get_crash_schedule(__snake_case      )

		snake_case						:  List[str]    	= DanceDiffusionPipeline(unet=__snake_case				,   scheduler=__snake_case      )

		snake_case						:  Any    	= torch.manual_seed(33      )
		snake_case						:  Any    	= pipe(num_inference_steps=__snake_case				,   generator=__snake_case      ).audios

		snake_case						:  str    	= sampling.iplms_sample(__snake_case				,   __snake_case				,   __snake_case				,   {}      )
		snake_case						:  int    	= generated.clamp(-1				,   1      )

		snake_case						:  Any    	= (generated - audio).abs().sum()
		snake_case						:  List[str]    	= (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__":
							__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 itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def 			_lowercase				(  __snake_case = "laptop"	)						->   DataFrame:
			__lowerCAmelCase      :  str												=       F"""https://www.amazon.in/laptop/s?k={product}"""
			__lowerCAmelCase      :  Union[str, Any]												=       {
			    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
			    "Accept-Language": "en-US, en;q=0.5",
			}
			__lowerCAmelCase      :  List[str]												=       BeautifulSoup(requests.get(__snake_case					,headers=__snake_case	).text	)
			# Initialize a Pandas dataframe with the column titles
			__lowerCAmelCase      :  Dict												=       DataFrame(
			    columns=[
			        "Product Title",
			        "Product Link",
			        "Current Price of the product",
			        "Product Rating",
			        "MRP of the product",
			        "Discount",
			    ]	)
			# Loop through each entry and store them in the dataframe
			for item, _ in zip_longest(
			    soup.find_all(
			        "div"					,attrs={"class": "s-result-item", "data-component-type": "s-search-result"}					,)					,soup.find_all("div"					,attrs={"class": "a-row a-size-base a-color-base"}	)					,):
						try:
									__lowerCAmelCase      :  Any												=       item.ha.text
									__lowerCAmelCase      :  Union[str, Any]												=       "https://www.amazon.in/" + item.ha.a["href"]
									__lowerCAmelCase      :  Any												=       item.find("span"					,attrs={"class": "a-offscreen"}	).text
									try:
												__lowerCAmelCase      :  Union[str, Any]												=       item.find("span"					,attrs={"class": "a-icon-alt"}	).text
									except AttributeError:
												__lowerCAmelCase      :  Optional[Any]												=       "Not available"
									try:
												__lowerCAmelCase      :  Union[str, Any]												=       (
												    "₹"
												    + item.find(
												        "span"					,attrs={"class": "a-price a-text-price"}	).text.split("₹"	)[1]
												)
									except AttributeError:
												__lowerCAmelCase      :  Dict												=       ""
									try:
												__lowerCAmelCase      :  str												=       float(
												    (
												        (
												            float(product_mrp.strip("₹"	).replace(","					,""	)	)
												            - float(product_price.strip("₹"	).replace(","					,""	)	)
												        )
												        / float(product_mrp.strip("₹"	).replace(","					,""	)	)
												    )
												    * 100	)
									except ValueError:
												__lowerCAmelCase      :  List[str]												=       float("nan"	)
						except AttributeError:
									pass
						__lowerCAmelCase      :  int												=       [
						    product_title,
						    product_link,
						    product_price,
						    product_rating,
						    product_mrp,
						    discount,
						]
			__lowerCAmelCase      :  Union[str, Any]												=       " "
			__lowerCAmelCase      :  Union[str, Any]												=       " "
			data_frame.index += 1
			return data_frame


if __name__ == "__main__":
	__snake_case							:   Any      =    'headphones'
	get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")