

'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					str		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Optional[Any]   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				original_name.split('.'   )[0]
							SCREAMING_SNAKE_CASE_				=				key.split('.'   )
							SCREAMING_SNAKE_CASE_				=				int(key_list[key_list.index(lowerCAmelCase__   ) - 2]   )
							SCREAMING_SNAKE_CASE_				=				int(key_list[key_list.index(lowerCAmelCase__   ) - 1]   )
							SCREAMING_SNAKE_CASE_				=				orig_block_num - offset

							SCREAMING_SNAKE_CASE_				=				key.replace(F"""{orig_block_num}.{layer_num}.{original_name}"""		,F"""block.{new_block_num}.{layer_num}.{new_name}"""   )
							return key




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]   )      -> Tuple:
							SCREAMING_SNAKE_CASE_				=				OrderedDict()
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				0, 0
							for key, value in state_dict.items():
														if key.startswith('network'   ):
																					SCREAMING_SNAKE_CASE_				=				key.replace('network'		,'poolformer.encoder'   )
														if "proj" in key:
																					# Works for the first embedding as well as the internal embedding layers
																					if key.endswith('bias'   ) and "patch_embed" not in key:
																												patch_emb_offset += 1
																					SCREAMING_SNAKE_CASE_				=				key[: key.find('proj'   )]
																					SCREAMING_SNAKE_CASE_				=				key.replace(lowerCAmelCase__		,F"""patch_embeddings.{total_embed_found}."""   )
																					SCREAMING_SNAKE_CASE_				=				key.replace('proj'		,'projection'   )
																					if key.endswith('bias'   ):
																												total_embed_found += 1
														if "patch_embeddings" in key:
																					SCREAMING_SNAKE_CASE_				=				'poolformer.encoder.' + key
														if "mlp.fc1" in key:
																					SCREAMING_SNAKE_CASE_				=				replace_key_with_offset(lowerCAmelCase__		,lowerCAmelCase__		,'mlp.fc1'		,'output.conv1'   )
														if "mlp.fc2" in key:
																					SCREAMING_SNAKE_CASE_				=				replace_key_with_offset(lowerCAmelCase__		,lowerCAmelCase__		,'mlp.fc2'		,'output.conv2'   )
														if "norm1" in key:
																					SCREAMING_SNAKE_CASE_				=				replace_key_with_offset(lowerCAmelCase__		,lowerCAmelCase__		,'norm1'		,'before_norm'   )
														if "norm2" in key:
																					SCREAMING_SNAKE_CASE_				=				replace_key_with_offset(lowerCAmelCase__		,lowerCAmelCase__		,'norm2'		,'after_norm'   )
														if "layer_scale_1" in key:
																					SCREAMING_SNAKE_CASE_				=				replace_key_with_offset(lowerCAmelCase__		,lowerCAmelCase__		,'layer_scale_1'		,'layer_scale_1'   )
														if "layer_scale_2" in key:
																					SCREAMING_SNAKE_CASE_				=				replace_key_with_offset(lowerCAmelCase__		,lowerCAmelCase__		,'layer_scale_2'		,'layer_scale_2'   )
														if "head" in key:
																					SCREAMING_SNAKE_CASE_				=				key.replace('head'		,'classifier'   )
														SCREAMING_SNAKE_CASE_				=				value
							return new_state_dict




def 							_UpperCamelCase				(   )      -> str:
							SCREAMING_SNAKE_CASE_				=				'http://images.cocodataset.org/val2017/000000039769.jpg'
							SCREAMING_SNAKE_CASE_				=				Image.open(requests.get(lowerCAmelCase__		,stream=lowerCAmelCase__   ).raw   )

							return image




@torch.no_grad()
def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Optional[Any]   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				PoolFormerConfig()

							# set attributes based on model_name
							SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
							SCREAMING_SNAKE_CASE_				=				model_name[-3:]
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				(1, 1000)

							# set config attributes
							SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(lowerCAmelCase__		,lowerCAmelCase__		,repo_type='dataset'   )		,'r'   )   )
							SCREAMING_SNAKE_CASE_				=				{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_				=				idalabel
							SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}
							if size == "s12":
														SCREAMING_SNAKE_CASE_				=				[2, 2, 6, 2]
														SCREAMING_SNAKE_CASE_				=				[64, 128, 320, 512]
														SCREAMING_SNAKE_CASE_				=				4.0
														SCREAMING_SNAKE_CASE_				=				0.9
							elif size == "s24":
														SCREAMING_SNAKE_CASE_				=				[4, 4, 12, 4]
														SCREAMING_SNAKE_CASE_				=				[64, 128, 320, 512]
														SCREAMING_SNAKE_CASE_				=				4.0
														SCREAMING_SNAKE_CASE_				=				0.9
							elif size == "s36":
														SCREAMING_SNAKE_CASE_				=				[6, 6, 18, 6]
														SCREAMING_SNAKE_CASE_				=				[64, 128, 320, 512]
														SCREAMING_SNAKE_CASE_				=				4.0
														SCREAMING_SNAKE_CASE_				=				1E-6
														SCREAMING_SNAKE_CASE_				=				0.9
							elif size == "m36":
														SCREAMING_SNAKE_CASE_				=				[6, 6, 18, 6]
														SCREAMING_SNAKE_CASE_				=				[96, 192, 384, 768]
														SCREAMING_SNAKE_CASE_				=				4.0
														SCREAMING_SNAKE_CASE_				=				1E-6
														SCREAMING_SNAKE_CASE_				=				0.95
							elif size == "m48":
														SCREAMING_SNAKE_CASE_				=				[8, 8, 24, 8]
														SCREAMING_SNAKE_CASE_				=				[96, 192, 384, 768]
														SCREAMING_SNAKE_CASE_				=				4.0
														SCREAMING_SNAKE_CASE_				=				1E-6
														SCREAMING_SNAKE_CASE_				=				0.95
							else:
														raise ValueError(F"""Size {size} not supported"""   )

							# load image processor
							SCREAMING_SNAKE_CASE_				=				PoolFormerImageProcessor(crop_pct=lowerCAmelCase__   )

							# Prepare image
							SCREAMING_SNAKE_CASE_				=				prepare_img()
							SCREAMING_SNAKE_CASE_				=				image_processor(images=lowerCAmelCase__		,return_tensors='pt'   ).pixel_values

							logger.info(F"""Converting model {model_name}..."""   )

							# load original state dict
							SCREAMING_SNAKE_CASE_				=				torch.load(lowerCAmelCase__		,map_location=torch.device('cpu'   )   )

							# rename keys
							SCREAMING_SNAKE_CASE_				=				rename_keys(lowerCAmelCase__   )

							# create HuggingFace model and load state dict
							SCREAMING_SNAKE_CASE_				=				PoolFormerForImageClassification(lowerCAmelCase__   )
							model.load_state_dict(lowerCAmelCase__   )
							model.eval()

							# Define image processor
							SCREAMING_SNAKE_CASE_				=				PoolFormerImageProcessor(crop_pct=lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				image_processor(images=prepare_img()		,return_tensors='pt'   ).pixel_values

							# forward pass
							SCREAMING_SNAKE_CASE_				=				model(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														SCREAMING_SNAKE_CASE_				=				torch.tensor([-0.3_045, -0.6_758, -0.4_869]   )
							elif size == "s24":
														SCREAMING_SNAKE_CASE_				=				torch.tensor([0.4_402, -0.1_374, -0.8_045]   )
							elif size == "s36":
														SCREAMING_SNAKE_CASE_				=				torch.tensor([-0.6_080, -0.5_133, -0.5_898]   )
							elif size == "m36":
														SCREAMING_SNAKE_CASE_				=				torch.tensor([0.3_952, 0.2_263, -1.2_668]   )
							elif size == "m48":
														SCREAMING_SNAKE_CASE_				=				torch.tensor([0.1_167, -0.0_656, -0.3_423]   )
							else:
														raise ValueError(F"""Size {size} not supported"""   )

							# verify logits
							assert logits.shape == expected_shape
							assert torch.allclose(logits[0, :3]		,lowerCAmelCase__		,atol=1E-2   )

							# finally, save model and image processor
							logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
							Path(lowerCAmelCase__   ).mkdir(exist_ok=lowerCAmelCase__   )
							model.save_pretrained(lowerCAmelCase__   )
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
							image_processor.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				List[str]     	=			argparse.ArgumentParser()

							parser.add_argument(
							    "--model_name",
							    default="poolformer_s12",
							    type=str,
							    help="Name of the model you'd like to convert.",
							)
							parser.add_argument(
							    "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
							)
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					str   )      -> bool:
							return credit_card_number.startswith(('34', '35', '37', '4', '5', '6')   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					str   )      -> bool:
							SCREAMING_SNAKE_CASE_				=				credit_card_number
							SCREAMING_SNAKE_CASE_				=				0
							SCREAMING_SNAKE_CASE_				=				len(lowerCAmelCase__   ) - 2
							for i in range(lowerCAmelCase__		,-1		,-2   ):
														#  double the value of every second digit
														SCREAMING_SNAKE_CASE_				=				int(cc_number[i]   )
														digit *= 2
														# If doubling of a number results in a two digit number
														# i.e greater than 9(e.g., 6 × 2 = 12),
														# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
														# to get a single digit number.
														if digit > 9:
																					digit %= 10
																					digit += 1
														SCREAMING_SNAKE_CASE_				=				cc_number[:i] + str(lowerCAmelCase__   ) + cc_number[i + 1 :]
														total += digit

							# Sum up the remaining digits
							for i in range(len(lowerCAmelCase__   ) - 1		,-1		,-2   ):
														total += int(cc_number[i]   )

							return total % 10 == 0




def 							_UpperCamelCase				(   lowerCAmelCase__:					str   )      -> bool:
							SCREAMING_SNAKE_CASE_				=				F"""{credit_card_number} is an invalid credit card number because"""
							if not credit_card_number.isdigit():
														print(F"""{error_message} it has nonnumerical characters."""   )
														return False

							if not 13 <= len(lowerCAmelCase__   ) <= 16:
														print(F"""{error_message} of its length."""   )
														return False

							if not validate_initial_digits(lowerCAmelCase__   ):
														print(F"""{error_message} of its first two digits."""   )
														return False

							if not luhn_validation(lowerCAmelCase__   ):
														print(F"""{error_message} it fails the Luhn check."""   )
														return False

							print(F"""{credit_card_number} is a valid credit card number."""   )
							return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							validate_credit_card_number("4111111111111111")
							validate_credit_card_number("32323")





