

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	SwinConfig()
						snake_case_   =	swin_name.split('''_'''       )

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

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

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

						snake_case_   =	img_size
						snake_case_   =	num_classes
						snake_case_   =	embed_dim
						snake_case_   =	depths
						snake_case_   =	num_heads
						snake_case_   =	window_size

						return config


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

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

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

						return name


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for key in orig_state_dict.copy().keys():
												snake_case_   =	orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

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

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

						return orig_state_dict


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	timm.create_model(SCREAMING_SNAKE_CASE__  ,		pretrained=SCREAMING_SNAKE_CASE__       )
						timm_model.eval()

						snake_case_   =	get_swin_config(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	SwinForImageClassification(SCREAMING_SNAKE_CASE__       )
						model.eval()

						snake_case_   =	convert_state_dict(timm_model.state_dict()  ,		SCREAMING_SNAKE_CASE__       )
						model.load_state_dict(SCREAMING_SNAKE_CASE__       )

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

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

						snake_case_   =	timm_model(inputs['''pixel_values''']       )
						snake_case_   =	model(**SCREAMING_SNAKE_CASE__       ).logits

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

						print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''       )
						model.save_pretrained(SCREAMING_SNAKE_CASE__       )

						print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
					lowerCAmelCase_       	=	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_       	=	parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]
						if len(SCREAMING_SNAKE_CASE__       ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	nums.pop(0       )
												snake_case_   =	permute(SCREAMING_SNAKE_CASE__       )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__       )
												result.extend(SCREAMING_SNAKE_CASE__       )
												nums.append(SCREAMING_SNAKE_CASE__       )
						return result


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						def backtrack(SCREAMING_SNAKE_CASE__       ):
												if start == len(SCREAMING_SNAKE_CASE__       ) - 1:
																		output.append(nums[:]       )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__  ,		len(SCREAMING_SNAKE_CASE__       )       ):
																								snake_case_,    snake_case_   =	nums[i], nums[start]
																								backtrack(start + 1       )
																								snake_case_,    snake_case_   =	nums[i], nums[start]  # backtrack

						snake_case_   =	[]
						backtrack(0       )
						return output


if __name__ == "__main__":
					import doctest

					# use res to print the data in permute2 function
					lowerCAmelCase_       	=	permutea([1, 2, 3])
					print(res)
					doctest.testmod()