






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 					lowercase__				(					__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_						=						SwinConfig()
					UpperCAmelCase_						=						swin_name.split('_'  )

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

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

					if "in22k" in swin_name:
										UpperCAmelCase_						=						21_841
					else:
										UpperCAmelCase_						=						1_000
										UpperCAmelCase_						=						"""huggingface/label-files"""
										UpperCAmelCase_						=						"""imagenet-1k-id2label.json"""
										UpperCAmelCase_						=						json.load(open(hf_hub_download(lowercase__					,	lowercase__					,	repo_type='dataset'  )					,	'r'  )  )
										UpperCAmelCase_						=						{int(lowercase__  ): v for k, v in idalabel.items()}
										UpperCAmelCase_						=						idalabel
										UpperCAmelCase_						=						{v: k for k, v in idalabel.items()}

					UpperCAmelCase_						=						img_size
					UpperCAmelCase_						=						num_classes
					UpperCAmelCase_						=						embed_dim
					UpperCAmelCase_						=						depths
					UpperCAmelCase_						=						num_heads
					UpperCAmelCase_						=						window_size

					return config




def 					lowercase__				(					__snake_case   :							List[str]  ):


					'''simple docstring'''


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

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

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

					return name




def 					lowercase__				(					__snake_case   :							Any					,	__snake_case   :							Any  ):


					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										UpperCAmelCase_						=						orig_state_dict.pop(lowercase__  )

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

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

					return orig_state_dict




def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							Any  ):


					'''simple docstring'''


					UpperCAmelCase_						=						timm.create_model(lowercase__					,	pretrained=lowercase__  )
					timm_model.eval()

					UpperCAmelCase_						=						get_swin_config(lowercase__  )
					UpperCAmelCase_						=						SwinForImageClassification(lowercase__  )
					model.eval()

					UpperCAmelCase_						=						convert_state_dict(timm_model.state_dict()					,	lowercase__  )
					model.load_state_dict(lowercase__  )

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

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

					UpperCAmelCase_						=						timm_model(inputs['pixel_values']  )
					UpperCAmelCase_						=						model(**lowercase__  ).logits

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

					print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"  )
					model.save_pretrained(lowercase__  )

					print(F"Saving image processor to {pytorch_dump_folder_path}"  )
					image_processor.save_pretrained(lowercase__  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						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.'
		)

		__UpperCAmelCase     							=						parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) == 0:
										return False
					UpperCAmelCase_   :				Optional[int]						=						len(__snake_case  ) // 2
					if a_list[midpoint] == item:
										return True
					if item < a_list[midpoint]:
										return binary_search(a_list[:midpoint]					,	__snake_case  )
					else:
										return binary_search(a_list[midpoint + 1 :]					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						input('Enter numbers separated by comma:\n').strip()
		__UpperCAmelCase     							=						[int(item.strip()) for item in user_input.split(',')]
		__UpperCAmelCase     							=						int(input('Enter the number to be found in the list:\n').strip())
		__UpperCAmelCase     							=						'' if binary_search(sequence, target) else 'not '
		print(F'{target} was {not_str}found in {sequence}')






