



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A__						:						Dict          =      {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]				):
		lowerCAmelCase_    :  Optional[int]             =				f"""pretrained_models/{model_name}"""
		if not os.path.isfile(__UpperCamelCase				):
				os.makedirs('''pretrained_models'''  ,exist_ok=__UpperCamelCase				)
				lowerCAmelCase_    :  Tuple             =				f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
				download_url(__UpperCamelCase  ,'''pretrained_models'''				)
		lowerCAmelCase_    :  List[Any]             =				torch.load(__UpperCamelCase  ,map_location=lambda __UpperCamelCase  ,__UpperCamelCase				: storage				)
		return model


def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]				):
		lowerCAmelCase_    :  Optional[Any]             =				download_model(pretrained_models[args.image_size]				)

		lowerCAmelCase_    :  Dict             =				state_dict['''x_embedder.proj.weight''']
		lowerCAmelCase_    :  Dict             =				state_dict['''x_embedder.proj.bias''']
		state_dict.pop('''x_embedder.proj.weight'''				)
		state_dict.pop('''x_embedder.proj.bias'''				)

		for depth in range(28				):
				lowerCAmelCase_    :  Optional[Any]             =				state_dict[
				    '''t_embedder.mlp.0.weight'''
				]
				lowerCAmelCase_    :  List[str]             =				state_dict[
				    '''t_embedder.mlp.0.bias'''
				]
				lowerCAmelCase_    :  List[str]             =				state_dict[
				    '''t_embedder.mlp.2.weight'''
				]
				lowerCAmelCase_    :  List[Any]             =				state_dict[
				    '''t_embedder.mlp.2.bias'''
				]
				lowerCAmelCase_    :  List[str]             =				state_dict[
				    '''y_embedder.embedding_table.weight'''
				]

				lowerCAmelCase_    :  str             =				state_dict[
				    f"""blocks.{depth}.adaLN_modulation.1.weight"""
				]
				lowerCAmelCase_    :  Optional[Any]             =				state_dict[
				    f"""blocks.{depth}.adaLN_modulation.1.bias"""
				]

				lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  List[str]             =				torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]  ,3  ,dim=0				)
				lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  List[Any]             =				torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]  ,3  ,dim=0				)

				lowerCAmelCase_    :  Optional[Any]             =				q
				lowerCAmelCase_    :  int             =				q_bias
				lowerCAmelCase_    :  Any             =				k
				lowerCAmelCase_    :  Optional[Any]             =				k_bias
				lowerCAmelCase_    :  Optional[Any]             =				v
				lowerCAmelCase_    :  Optional[Any]             =				v_bias

				lowerCAmelCase_    :  List[Any]             =				state_dict[
				    f"""blocks.{depth}.attn.proj.weight"""
				]
				lowerCAmelCase_    :  Tuple             =				state_dict[f"""blocks.{depth}.attn.proj.bias"""]

				lowerCAmelCase_    :  Any             =				state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
				lowerCAmelCase_    :  Union[str, Any]             =				state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
				lowerCAmelCase_    :  Optional[Any]             =				state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
				lowerCAmelCase_    :  Union[str, Any]             =				state_dict[f"""blocks.{depth}.mlp.fc2.bias"""]

				state_dict.pop(f"""blocks.{depth}.attn.qkv.weight"""				)
				state_dict.pop(f"""blocks.{depth}.attn.qkv.bias"""				)
				state_dict.pop(f"""blocks.{depth}.attn.proj.weight"""				)
				state_dict.pop(f"""blocks.{depth}.attn.proj.bias"""				)
				state_dict.pop(f"""blocks.{depth}.mlp.fc1.weight"""				)
				state_dict.pop(f"""blocks.{depth}.mlp.fc1.bias"""				)
				state_dict.pop(f"""blocks.{depth}.mlp.fc2.weight"""				)
				state_dict.pop(f"""blocks.{depth}.mlp.fc2.bias"""				)
				state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.weight"""				)
				state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.bias"""				)

		state_dict.pop('''t_embedder.mlp.0.weight'''				)
		state_dict.pop('''t_embedder.mlp.0.bias'''				)
		state_dict.pop('''t_embedder.mlp.2.weight'''				)
		state_dict.pop('''t_embedder.mlp.2.bias'''				)
		state_dict.pop('''y_embedder.embedding_table.weight'''				)

		lowerCAmelCase_    :  Dict             =				state_dict['''final_layer.adaLN_modulation.1.weight''']
		lowerCAmelCase_    :  Optional[Any]             =				state_dict['''final_layer.adaLN_modulation.1.bias''']
		lowerCAmelCase_    :  List[str]             =				state_dict['''final_layer.linear.weight''']
		lowerCAmelCase_    :  Dict             =				state_dict['''final_layer.linear.bias''']

		state_dict.pop('''final_layer.linear.weight'''				)
		state_dict.pop('''final_layer.linear.bias'''				)
		state_dict.pop('''final_layer.adaLN_modulation.1.weight'''				)
		state_dict.pop('''final_layer.adaLN_modulation.1.bias'''				)

		# DiT XL/2
		lowerCAmelCase_    :  Optional[int]             =				TransformeraDModel(
		    sample_size=args.image_size // 8  ,num_layers=28  ,attention_head_dim=72  ,in_channels=4  ,out_channels=8  ,patch_size=2  ,attention_bias=__UpperCamelCase  ,num_attention_heads=16  ,activation_fn='''gelu-approximate'''  ,num_embeds_ada_norm=1000  ,norm_type='''ada_norm_zero'''  ,norm_elementwise_affine=__UpperCamelCase  ,)
		transformer.load_state_dict(__UpperCamelCase  ,strict=__UpperCamelCase				)

		lowerCAmelCase_    :  str             =				DDIMScheduler(
		    num_train_timesteps=1000  ,beta_schedule='''linear'''  ,prediction_type='''epsilon'''  ,clip_sample=__UpperCamelCase  ,)

		lowerCAmelCase_    :  Dict             =				AutoencoderKL.from_pretrained(args.vae_model				)

		lowerCAmelCase_    :  List[str]             =				DiTPipeline(transformer=__UpperCamelCase  ,vae=__UpperCamelCase  ,scheduler=__UpperCamelCase				)

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


if __name__ == "__main__":
						A__						:						str          =      argparse.ArgumentParser()

						parser.add_argument(
						    '''--image_size''',
						    default=256,
						    type=int,
						    required=False,
						    help='''Image size of pretrained model, either 256 or 512.''',
						)
						parser.add_argument(
						    '''--vae_model''',
						    default='''stabilityai/sd-vae-ft-ema''',
						    type=str,
						    required=False,
						    help='''Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.''',
						)
						parser.add_argument(
						    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted pipeline or not.'''
						)
						parser.add_argument(
						    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output pipeline.'''
						)

						A__						:						Any          =      parser.parse_args()
						main(args)










from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A__						:						int          =      '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	str				):

		try:
				# Has user provided both inputs?
				if not drug_name or not zip_code:
						return None

				lowerCAmelCase_    :  Union[str, Any]             =				BASE_URL.format(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Optional[int]             =				get(__UpperCamelCase				)

				# Is the response ok?
				response.raise_for_status()

				# Scrape the data using bs4
				lowerCAmelCase_    :  Optional[int]             =				BeautifulSoup(response.text  ,'''html.parser'''				)

				# This list will store the name and price.
				lowerCAmelCase_    :  List[Any]             =				[]

				# Fetch all the grids that contains the items.
				lowerCAmelCase_    :  Union[str, Any]             =				soup.find_all('''div'''  ,{'''class''': '''grid-x pharmCard'''}				)
				if grid_list and len(__UpperCamelCase				) > 0:
						for grid in grid_list:
								# Get the pharmacy price.
								lowerCAmelCase_    :  int             =				grid.find('''p'''  ,{'''class''': '''list-title'''}				).text

								# Get price of the drug.
								lowerCAmelCase_    :  List[Any]             =				grid.find('''span'''  ,{'''p''', '''price price-large'''}				).text

								pharmacy_price_list.append(
								    {
								        '''pharmacy_name''': pharmacy_name,
								        '''price''': price,
								    }				)

				return pharmacy_price_list

		except (HTTPError, exceptions.RequestException, ValueError):
				return None


if __name__ == "__main__":
						# Enter a drug name and a zip code
						A__						:						Dict          =      input('''Enter drug name: ''').strip()
						A__						:						int          =      input('''Enter zip code: ''').strip()

						A__						:						list | None          =      fetch_pharmacy_and_price_list(
						    drug_name, zip_code
						)

						if pharmacy_price_list:
												print(F'''\nSearch results for {drug_name} at location {zip_code}:''')
												for pharmacy_price in pharmacy_price_list:
																		A__						:						Union[str, Any]          =      pharmacy_price['''pharmacy_name''']
																		A__						:						Tuple          =      pharmacy_price['''price''']

																		print(F'''Pharmacy: {name} Price: {price}''')
						else:
												print(F'''No results found for {drug_name}''')





