



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__						:						str          =      logging.get_logger(__name__)


def     UpperCamelCase(						__UpperCamelCase							:	List[str]				):
		lowerCAmelCase_    :  List[str]             =				ConvNextConfig()

		if "tiny" in checkpoint_url:
				lowerCAmelCase_    :  Dict             =				[3, 3, 9, 3]
				lowerCAmelCase_    :  Tuple             =				[96, 192, 384, 768]
		if "small" in checkpoint_url:
				lowerCAmelCase_    :  Union[str, Any]             =				[3, 3, 27, 3]
				lowerCAmelCase_    :  List[str]             =				[96, 192, 384, 768]
		if "base" in checkpoint_url:
				lowerCAmelCase_    :  Optional[Any]             =				[3, 3, 27, 3]
				lowerCAmelCase_    :  Optional[int]             =				[128, 256, 512, 1024]
		if "large" in checkpoint_url:
				lowerCAmelCase_    :  Optional[Any]             =				[3, 3, 27, 3]
				lowerCAmelCase_    :  Tuple             =				[192, 384, 768, 1536]
		if "xlarge" in checkpoint_url:
				lowerCAmelCase_    :  List[str]             =				[3, 3, 27, 3]
				lowerCAmelCase_    :  Tuple             =				[256, 512, 1024, 2048]

		if "1k" in checkpoint_url:
				lowerCAmelCase_    :  Dict             =				1000
				lowerCAmelCase_    :  int             =				'''imagenet-1k-id2label.json'''
				lowerCAmelCase_    :  List[Any]             =				(1, 1000)
		else:
				lowerCAmelCase_    :  int             =				21841
				lowerCAmelCase_    :  Optional[Any]             =				'''imagenet-22k-id2label.json'''
				lowerCAmelCase_    :  str             =				(1, 21841)

		lowerCAmelCase_    :  Optional[int]             =				'''huggingface/label-files'''
		lowerCAmelCase_    :  Optional[Any]             =				num_labels
		lowerCAmelCase_    :  Tuple             =				json.load(open(hf_hub_download(__UpperCamelCase  ,__UpperCamelCase  ,repo_type='''dataset'''				)  ,'''r'''				)				)
		lowerCAmelCase_    :  Dict             =				{int(__UpperCamelCase				): v for k, v in idalabel.items()}
		if "1k" not in checkpoint_url:
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[9205]
				del idalabel[15027]
		lowerCAmelCase_    :  Union[str, Any]             =				idalabel
		lowerCAmelCase_    :  str             =				{v: k for k, v in idalabel.items()}
		lowerCAmelCase_    :  List[Any]             =				hidden_sizes
		lowerCAmelCase_    :  Any             =				depths

		return config, expected_shape


def     UpperCamelCase(						__UpperCamelCase							:	str				):
		if "downsample_layers.0.0" in name:
				lowerCAmelCase_    :  Tuple             =				name.replace('''downsample_layers.0.0'''  ,'''embeddings.patch_embeddings'''				)
		if "downsample_layers.0.1" in name:
				lowerCAmelCase_    :  Any             =				name.replace('''downsample_layers.0.1'''  ,'''embeddings.norm'''				)  # we rename to layernorm later on
		if "downsample_layers.1.0" in name:
				lowerCAmelCase_    :  Union[str, Any]             =				name.replace('''downsample_layers.1.0'''  ,'''stages.1.downsampling_layer.0'''				)
		if "downsample_layers.1.1" in name:
				lowerCAmelCase_    :  Dict             =				name.replace('''downsample_layers.1.1'''  ,'''stages.1.downsampling_layer.1'''				)
		if "downsample_layers.2.0" in name:
				lowerCAmelCase_    :  Optional[Any]             =				name.replace('''downsample_layers.2.0'''  ,'''stages.2.downsampling_layer.0'''				)
		if "downsample_layers.2.1" in name:
				lowerCAmelCase_    :  Optional[int]             =				name.replace('''downsample_layers.2.1'''  ,'''stages.2.downsampling_layer.1'''				)
		if "downsample_layers.3.0" in name:
				lowerCAmelCase_    :  Union[str, Any]             =				name.replace('''downsample_layers.3.0'''  ,'''stages.3.downsampling_layer.0'''				)
		if "downsample_layers.3.1" in name:
				lowerCAmelCase_    :  Union[str, Any]             =				name.replace('''downsample_layers.3.1'''  ,'''stages.3.downsampling_layer.1'''				)
		if "stages" in name and "downsampling_layer" not in name:
				# stages.0.0. for instance should be renamed to stages.0.layers.0.
				lowerCAmelCase_    :  Dict             =				name[: len('''stages.0'''				)] + '''.layers''' + name[len('''stages.0'''				) :]
		if "stages" in name:
				lowerCAmelCase_    :  Optional[int]             =				name.replace('''stages'''  ,'''encoder.stages'''				)
		if "norm" in name:
				lowerCAmelCase_    :  Dict             =				name.replace('''norm'''  ,'''layernorm'''				)
		if "gamma" in name:
				lowerCAmelCase_    :  Union[str, Any]             =				name.replace('''gamma'''  ,'''layer_scale_parameter'''				)
		if "head" in name:
				lowerCAmelCase_    :  int             =				name.replace('''head'''  ,'''classifier'''				)

		return name


def     UpperCamelCase(						):
		lowerCAmelCase_    :  List[str]             =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_    :  List[str]             =				Image.open(requests.get(__UpperCamelCase  ,stream=__UpperCamelCase				).raw				)
		return im


@torch.no_grad()
def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	Optional[int]				):
		lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[int]             =				get_convnext_config(__UpperCamelCase				)
		# load original state_dict from URL
		lowerCAmelCase_    :  List[str]             =				torch.hub.load_state_dict_from_url(__UpperCamelCase				)['''model''']
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase_    :  List[str]             =				state_dict.pop(__UpperCamelCase				)
				lowerCAmelCase_    :  List[Any]             =				val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				lowerCAmelCase_    :  Tuple             =				state_dict.pop(__UpperCamelCase				)
				if not key.startswith('''classifier'''				):
						lowerCAmelCase_    :  Union[str, Any]             =				'''convnext.''' + key
				lowerCAmelCase_    :  int             =				val

		# load HuggingFace model
		lowerCAmelCase_    :  List[Any]             =				ConvNextForImageClassification(__UpperCamelCase				)
		model.load_state_dict(__UpperCamelCase				)
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		lowerCAmelCase_    :  Union[str, Any]             =				224 if '''224''' in checkpoint_url else 384
		lowerCAmelCase_    :  str             =				ConvNextImageProcessor(size=__UpperCamelCase				)
		lowerCAmelCase_    :  str             =				image_processor(images=prepare_img()  ,return_tensors='''pt'''				).pixel_values

		lowerCAmelCase_    :  List[Any]             =				model(__UpperCamelCase				).logits

		# note: the logits below were obtained without center cropping
		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
				lowerCAmelCase_    :  Dict             =				torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
				lowerCAmelCase_    :  Any             =				torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
				lowerCAmelCase_    :  List[str]             =				torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
				lowerCAmelCase_    :  Optional[int]             =				torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
				lowerCAmelCase_    :  Tuple             =				torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
				lowerCAmelCase_    :  Any             =				torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
				lowerCAmelCase_    :  str             =				torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
				lowerCAmelCase_    :  List[Any]             =				torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
				lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
				lowerCAmelCase_    :  List[Any]             =				torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
				lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
				lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
				lowerCAmelCase_    :  Optional[Any]             =				torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
				lowerCAmelCase_    :  int             =				torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]				)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
				lowerCAmelCase_    :  int             =				torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]				)
		else:
				raise ValueError(f"""Unknown URL: {checkpoint_url}"""				)

		assert torch.allclose(logits[0, :3]  ,__UpperCamelCase  ,atol=1e-3				)
		assert logits.shape == expected_shape

		Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
		print(f"""Saving model to {pytorch_dump_folder_path}"""				)
		model.save_pretrained(__UpperCamelCase				)
		print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
		image_processor.save_pretrained(__UpperCamelCase				)

		print('''Pushing model to the hub...'''				)
		lowerCAmelCase_    :  Union[str, Any]             =				'''convnext'''
		if "tiny" in checkpoint_url:
				model_name += "-tiny"
		elif "small" in checkpoint_url:
				model_name += "-small"
		elif "base" in checkpoint_url:
				model_name += "-base"
		elif "xlarge" in checkpoint_url:
				model_name += "-xlarge"
		elif "large" in checkpoint_url:
				model_name += "-large"
		if "224" in checkpoint_url:
				model_name += "-224"
		elif "384" in checkpoint_url:
				model_name += "-384"
		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
				model_name += "-22k"
		if "22k" in checkpoint_url and "1k" in checkpoint_url:
				model_name += "-22k-1k"

		model.push_to_hub(
		    repo_path_or_name=Path(__UpperCamelCase  ,__UpperCamelCase				)  ,organization='''nielsr'''  ,commit_message='''Add model'''  ,)


if __name__ == "__main__":
						A__						:						Tuple          =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
						    type=str,
						    help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''Path to the output PyTorch model directory.''',
						)

						A__						:						Union[str, Any]          =      parser.parse_args()
						convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










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}''')





