




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       (     _snake_case							):
							lowerCAmelCase   =		SwinConfig()
							lowerCAmelCase   =		swin_name.split('''_'''							)

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

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

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

							lowerCAmelCase   =		img_size
							lowerCAmelCase   =		num_classes
							lowerCAmelCase   =		embed_dim
							lowerCAmelCase   =		depths
							lowerCAmelCase   =		num_heads
							lowerCAmelCase   =		window_size

							return config



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

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

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

							return name



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							for key in orig_state_dict.copy().keys():
														lowerCAmelCase   =		orig_state_dict.pop(_snake_case							)

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

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

							return orig_state_dict



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							lowerCAmelCase   =		timm.create_model(_snake_case						, pretrained=_snake_case							)
							timm_model.eval()

							lowerCAmelCase   =		get_swin_config(_snake_case							)
							lowerCAmelCase   =		SwinForImageClassification(_snake_case							)
							model.eval()

							lowerCAmelCase   =		convert_state_dict(timm_model.state_dict()						, _snake_case							)
							model.load_state_dict(_snake_case							)

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

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

							lowerCAmelCase   =		timm_model(inputs['''pixel_values''']							)
							lowerCAmelCase   =		model(**_snake_case							).logits

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

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

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


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)









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item							) for item in array							)

							return count_of_possible_combinations(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

							for i in range(1						, target + 1							):
														for j in range(_snake_case							):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



