



"""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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Tuple:
							A         =					SwinConfig()
							A         =					swin_name.split('_'			)

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

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

							if "in22k" in swin_name:
														A         =					21841
							else:
														A         =					1000
														A         =					'huggingface/label-files'
														A         =					'imagenet-1k-id2label.json'
														A         =					json.load(open(hf_hub_download(lowerCamelCase__				,      lowerCamelCase__				,      repo_type='dataset'			)				,      'r'			)			)
														A         =					{int(lowerCamelCase__			): v for k, v in idalabel.items()}
														A         =					idalabel
														A         =					{v: k for k, v in idalabel.items()}

							A         =					img_size
							A         =					num_classes
							A         =					embed_dim
							A         =					depths
							A         =					num_heads
							A         =					window_size

							return config

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							if "patch_embed.proj" in name:
														A         =					name.replace('patch_embed.proj'				,      'embeddings.patch_embeddings.projection'			)
							if "patch_embed.norm" in name:
														A         =					name.replace('patch_embed.norm'				,      'embeddings.norm'			)
							if "layers" in name:
														A         =					'encoder.' + name
							if "attn.proj" in name:
														A         =					name.replace('attn.proj'				,      'attention.output.dense'			)
							if "attn" in name:
														A         =					name.replace('attn'				,      'attention.self'			)
							if "norm1" in name:
														A         =					name.replace('norm1'				,      'layernorm_before'			)
							if "norm2" in name:
														A         =					name.replace('norm2'				,      'layernorm_after'			)
							if "mlp.fc1" in name:
														A         =					name.replace('mlp.fc1'				,      'intermediate.dense'			)
							if "mlp.fc2" in name:
														A         =					name.replace('mlp.fc2'				,      'output.dense'			)

							if name == "norm.weight":
														A         =					'layernorm.weight'
							if name == "norm.bias":
														A         =					'layernorm.bias'

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

							return name

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Any:
							for key in orig_state_dict.copy().keys():
														A         =					orig_state_dict.pop(lowerCamelCase__			)

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

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

							return orig_state_dict

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Union[str, Any]:
							A         =					timm.create_model(lowerCamelCase__				,      pretrained=lowerCamelCase__			)
							timm_model.eval()

							A         =					get_swin_config(lowerCamelCase__			)
							A         =					SwinForImageClassification(lowerCamelCase__			)
							model.eval()

							A         =					convert_state_dict(timm_model.state_dict()				,      lowerCamelCase__			)
							model.load_state_dict(lowerCamelCase__			)

							A         =					'http://images.cocodataset.org/val2017/000000039769.jpg'

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

							A         =					timm_model(inputs['pixel_values']			)
							A         =					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    =			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    =			parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








"""simple docstring"""







import re

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							list:
							return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]'				,      str_			)]

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							A         =					split_input(str_			)
							return "".join(
							    [''.join([char.capitalize() for char in sub_str]			) for sub_str in string_split]			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							try:
														A         =					split_input(lowerCamelCase__			)
														if upper:
																					A         =					''.join(
																					    [
																					        separator.join([char.upper() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														else:
																					A         =					''.join(
																					    [
																					        separator.join([char.lower() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														return res_str
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							return to_simple_case(lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							try:
														A         =					to_simple_case(lowerCamelCase__			)
														return res_str[0].lower() + res_str[1:]
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '_'			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '-'			)


if __name__ == "__main__":
							__import__('doctest').testmod()



