
'''simple docstring'''






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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A			      =      logging.get_logger(__name__)

A			      =      torch.device('cpu')





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Optional[int]									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				lowerCamelCase					:			Dict									= Image.open(requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__).raw)
				return im





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]):
				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1])

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1])

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2])

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2])





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Optional[Any]):
				lowerCamelCase					:			Union[str, Any]									= dct.pop(UpperCAmelCase__)
				lowerCamelCase					:			int									= val





def    UpperCAmelCase							(  UpperCAmelCase__							:							Any):
				lowerCamelCase					:			Any									= []
				for k in state_dict.keys():
								lowerCamelCase					:			str									= k
								if ".pwconv" in k:
												lowerCamelCase					:			Optional[Any]									= k_new.replace('.pwconv'       ,							'.point_wise_conv')
								if ".dwconv" in k:
												lowerCamelCase					:			Union[str, Any]									= k_new.replace('.dwconv'       ,							'.depth_wise_conv')
								if ".Proj." in k:
												lowerCamelCase					:			Any									= k_new.replace('.Proj.'       ,							'.proj.')
								if "patch_embed" in k_new:
												lowerCamelCase					:			Any									= k_new.replace('patch_embed'       ,							'swiftformer.patch_embed.patch_embedding')
								if "network" in k_new:
												lowerCamelCase					:			Optional[int]									= k_new.split('.')
												if ls[2].isdigit():
																lowerCamelCase					:			Optional[int]									= 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:])
												else:
																lowerCamelCase					:			str									= k_new.replace('network'       ,							'swiftformer.encoder.network')
								rename_keys.append((k, k_new))
				return rename_keys





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Union[str, Any]):
				lowerCamelCase					:			Optional[int]									= SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				lowerCamelCase					:			str									= 10_00
				lowerCamelCase					:			str									= 'huggingface/label-files'
				lowerCamelCase					:			int									= 'imagenet-1k-id2label.json'
				lowerCamelCase					:			Optional[int]									= json.load(open(hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__       ,							repo_type='dataset')       ,							'r'))
				lowerCamelCase					:			Optional[int]									= {int(UpperCAmelCase__): v for k, v in idalabel.items()}
				lowerCamelCase					:			Any									= idalabel
				lowerCamelCase					:			Any									= {v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								lowerCamelCase					:			Optional[int]									= [3, 3, 6, 4]
								lowerCamelCase					:			Optional[int]									= [48, 56, 1_12, 2_20]

				elif swiftformer_name == "swiftformer_s":
								lowerCamelCase					:			Union[str, Any]									= [3, 3, 9, 6]
								lowerCamelCase					:			str									= [48, 64, 1_68, 2_24]

				elif swiftformer_name == "swiftformer_l1":
								lowerCamelCase					:			List[Any]									= [4, 3, 10, 5]
								lowerCamelCase					:			Tuple									= [48, 96, 1_92, 3_84]

				elif swiftformer_name == "swiftformer_l3":
								lowerCamelCase					:			List[str]									= [4, 4, 12, 6]
								lowerCamelCase					:			List[str]									= [64, 1_28, 3_20, 5_12]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('https'):
												lowerCamelCase					:			str									= torch.hub.load_state_dict_from_url(UpperCAmelCase__       ,							map_location='cpu'       ,							check_hash=UpperCAmelCase__)
								else:
												lowerCamelCase					:			int									= torch.load(UpperCAmelCase__       ,							map_location='cpu')
				lowerCamelCase					:			List[str]									= checkpoint

				lowerCamelCase					:			List[str]									= create_rename_keys(UpperCAmelCase__)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)

				# load HuggingFace model
				lowerCamelCase					:			Any									= SwiftFormerForImageClassification(UpperCAmelCase__).eval()
				hf_model.load_state_dict(UpperCAmelCase__)

				# prepare test inputs
				lowerCamelCase					:			Optional[Any]									= prepare_img()
				lowerCamelCase					:			Tuple									= ViTImageProcessor.from_pretrained('preprocessor_config')
				lowerCamelCase					:			Dict									= processor(images=UpperCAmelCase__       ,							return_tensors='pt')

				# compare outputs from both models
				lowerCamelCase					:			Any									= get_expected_output(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= hf_model(inputs['pixel_values']).logits

				assert hf_logits.shape == torch.Size([1, 10_00])
				assert torch.allclose(hf_logits[0, 0:5]       ,							UpperCAmelCase__       ,							atol=1E-3)

				Path(UpperCAmelCase__).mkdir(exist_ok=UpperCAmelCase__)
				print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}''')
				hf_model.save_pretrained(UpperCAmelCase__)


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--swiftformer_name',
					    default='swiftformer_xs',
					    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
					    type=str,
					    help='Name of the SwiftFormer model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default='./converted_outputs/',
					    type=str,
					    help='Path to the output PyTorch model directory.',
					)
					parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

					A			      =      parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


'''simple docstring'''






import requests
from bsa import BeautifulSoup





def    UpperCAmelCase							(  UpperCAmelCase__							:							str = "AAPL"):
				lowerCamelCase					:			List[Any]									= F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
				lowerCamelCase					:			Dict									= BeautifulSoup(requests.get(UpperCAmelCase__).text       ,							'html.parser')
				lowerCamelCase					:			Dict									= 'My(6px) Pos(r) smartphone_Mt(6px)'
				return soup.find('div'       ,							class_=class_).find('span').text


if __name__ == "__main__":
					for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
										print(f"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
