


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__    :							Optional[int]            =					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__    :							List[Any]            =					parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




"""simple docstring"""







from __future__ import annotations




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					if not nums:
										return 0
					__SCREAMING_SNAKE_CASE						       =    nums[0]
					__SCREAMING_SNAKE_CASE						       =    0
					for num in nums[1:]:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (
										    max_excluding + num,
										    max(lowerCAmelCase_  ,						lowerCAmelCase_       ),
										)
					return max(lowerCAmelCase_  ,						lowerCAmelCase_       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
