


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(					__a				:						Dict     ):
							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__      =2_1841
							else:
														a__      =1000
														a__      ='huggingface/label-files'
														a__      ='imagenet-1k-id2label.json'
														a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
														a__      ={int(__a     ): 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   _lowercase(					__a				:						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   _lowercase(					__a				:						List[str]    ,     __a				:						List[str]     ):
							for key in orig_state_dict.copy().keys():
														a__      =orig_state_dict.pop(__a     )

														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   _lowercase(					__a				:						List[str]    ,     __a				:						List[Any]     ):
							a__      =timm.create_model(__a    ,     pretrained=__a     )
							timm_model.eval()

							a__      =get_swin_config(__a     )
							a__      =SwinForImageClassification(__a     )
							model.eval()

							a__      =convert_state_dict(timm_model.state_dict()    ,     __a     )
							model.load_state_dict(__a     )

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

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

							a__      =timm_model(inputs['pixel_values']     )
							a__      =model(**__a     ).logits

							assert torch.allclose(__a    ,     __a    ,     atol=1e-3     )

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

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


if __name__ == "__main__":
	_lowerCAmelCase:						Any   				=       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.'
	)

	_lowerCAmelCase:						Tuple   				=       parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






'''simple docstring'''

def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    Optional[int]      							=len(__UpperCAmelCase					)
			snake_case:    Optional[int]      							=[[0] * n for i in range(__UpperCAmelCase					)]
			for i in range(__UpperCAmelCase					):
						snake_case:    Optional[int]      							=y_points[i]

			for i in range(2				,							__UpperCAmelCase					):
						for j in range(__UpperCAmelCase				,							__UpperCAmelCase					):
									snake_case:    List[str]      							=(
									    (xa - x_points[j - i + 1]) * q[j][i - 1]
									    - (xa - x_points[j]) * q[j - 1][i - 1]
									) / (x_points[j] - x_points[j - i + 1])

			return [q[n - 1][n - 1], q]


if __name__ == "__main__":
	import doctest

	doctest.testmod()




