


"""simple docstring"""







import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				SwinConfig()
					__SCREAMING_SNAKE_CASE						   =				swin_name.split("_"   )

					__SCREAMING_SNAKE_CASE						   =				name_split[1]
					__SCREAMING_SNAKE_CASE						   =				int(name_split[4]   )
					__SCREAMING_SNAKE_CASE						   =				int(name_split[3][-1]   )

					if model_size == "tiny":
										__SCREAMING_SNAKE_CASE						   =				96
										__SCREAMING_SNAKE_CASE						   =				(2, 2, 6, 2)
										__SCREAMING_SNAKE_CASE						   =				(3, 6, 12, 24)
					elif model_size == "small":
										__SCREAMING_SNAKE_CASE						   =				96
										__SCREAMING_SNAKE_CASE						   =				(2, 2, 18, 2)
										__SCREAMING_SNAKE_CASE						   =				(3, 6, 12, 24)
					elif model_size == "base":
										__SCREAMING_SNAKE_CASE						   =				128
										__SCREAMING_SNAKE_CASE						   =				(2, 2, 18, 2)
										__SCREAMING_SNAKE_CASE						   =				(4, 8, 16, 32)
					else:
										__SCREAMING_SNAKE_CASE						   =				192
										__SCREAMING_SNAKE_CASE						   =				(2, 2, 18, 2)
										__SCREAMING_SNAKE_CASE						   =				(6, 12, 24, 48)

					if "in22k" in swin_name:
										__SCREAMING_SNAKE_CASE						   =				2_1841
					else:
										__SCREAMING_SNAKE_CASE						   =				1000
										__SCREAMING_SNAKE_CASE						   =				"huggingface/label-files"
										__SCREAMING_SNAKE_CASE						   =				"imagenet-1k-id2label.json"
										__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()}

					__SCREAMING_SNAKE_CASE						   =				img_size
					__SCREAMING_SNAKE_CASE						   =				num_classes
					__SCREAMING_SNAKE_CASE						   =				embed_dim
					__SCREAMING_SNAKE_CASE						   =				depths
					__SCREAMING_SNAKE_CASE						   =				num_heads
					__SCREAMING_SNAKE_CASE						   =				window_size

					return config




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if "patch_embed.proj" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("patch_embed.proj"  ,						"embeddings.patch_embeddings.projection"   )
					if "patch_embed.norm" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("patch_embed.norm"  ,						"embeddings.norm"   )
					if "layers" in name:
										__SCREAMING_SNAKE_CASE						   =				"encoder." + name
					if "attn.proj" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("attn.proj"  ,						"attention.output.dense"   )
					if "attn" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("attn"  ,						"attention.self"   )
					if "norm1" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("norm1"  ,						"layernorm_before"   )
					if "norm2" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("norm2"  ,						"layernorm_after"   )
					if "mlp.fc1" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("mlp.fc1"  ,						"intermediate.dense"   )
					if "mlp.fc2" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("mlp.fc2"  ,						"output.dense"   )

					if name == "norm.weight":
										__SCREAMING_SNAKE_CASE						   =				"layernorm.weight"
					if name == "norm.bias":
										__SCREAMING_SNAKE_CASE						   =				"layernorm.bias"

					if "head" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("head"  ,						"classifier"   )
					else:
										__SCREAMING_SNAKE_CASE						   =				"swin." + name

					return name




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										__SCREAMING_SNAKE_CASE						   =				orig_state_dict.pop(lowerCAmelCase_   )

										if "mask" in key:
															continue
										elif "qkv" in key:
															__SCREAMING_SNAKE_CASE						   =				key.split("."   )
															__SCREAMING_SNAKE_CASE						   =				int(key_split[1]   )
															__SCREAMING_SNAKE_CASE						   =				int(key_split[3]   )
															__SCREAMING_SNAKE_CASE						   =				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

															if "weight" in key:
																				__SCREAMING_SNAKE_CASE						   =				val[:dim, :]
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    dim : dim * 2, :
																				]
																				__SCREAMING_SNAKE_CASE						   =				val[-dim:, :]
															else:
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    :dim
																				]
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    dim : dim * 2
																				]
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    -dim:
																				]
										else:
															__SCREAMING_SNAKE_CASE						   =				val

					return orig_state_dict




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				timm.create_model(lowerCAmelCase_  ,						pretrained=lowerCAmelCase_   )
					timm_model.eval()

					__SCREAMING_SNAKE_CASE						   =				get_swin_config(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				SwinForImageClassification(lowerCAmelCase_   )
					model.eval()

					__SCREAMING_SNAKE_CASE						   =				convert_state_dict(timm_model.state_dict()  ,						lowerCAmelCase_   )
					model.load_state_dict(lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				"http://images.cocodataset.org/val2017/000000039769.jpg"

					__SCREAMING_SNAKE_CASE						   =				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"  ,						"-"   )   )   )
					__SCREAMING_SNAKE_CASE						   =				Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_   ).raw   )
					__SCREAMING_SNAKE_CASE						   =				image_processor(images=lowerCAmelCase_  ,						return_tensors="pt"   )

					__SCREAMING_SNAKE_CASE						   =				timm_model(inputs["pixel_values"]   )
					__SCREAMING_SNAKE_CASE						   =				model(**lowerCAmelCase_   ).logits

					assert torch.allclose(lowerCAmelCase_  ,						lowerCAmelCase_  ,						atol=1E-3   )

					print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""   )
					model.save_pretrained(lowerCAmelCase_   )

					print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
					image_processor.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
		a__				:							Dict						=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swin_name''',
		    default='''swin_tiny_patch4_window7_224''',
		    type=str,
		    help='''Name of the Swin timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		a__				:							Union[str, Any]						=     parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




"""simple docstring"""







import math
import random




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value   ))


# Initial Value
a__				:							Tuple						=     0.02




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(2 * (random.randint(1  ,						100   )) - 1   )

					for _ in range(lowerCAmelCase_   ):
										# Forward propagation
										__SCREAMING_SNAKE_CASE						   =				sigmoid_function(INITIAL_VALUE * weight   )
										# How much did we miss?
										__SCREAMING_SNAKE_CASE						   =				(expected / 100) - layer_a
										# Error delta
										__SCREAMING_SNAKE_CASE						   =				layer_1_error * sigmoid_function(lowerCAmelCase_  ,						lowerCAmelCase_   )
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 100


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__				:							List[str]						=     int(input('''Expected value: '''))
		a__				:							str						=     int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
