

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       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	SwinConfig()
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	swin_name.split("_"   )

						SCREAMING_SNAKE_CASE_:			str  		=	name_split[1]
						SCREAMING_SNAKE_CASE_:			int  		=	int(name_split[4]   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	int(name_split[3][-1]   )

						if model_size == "tiny":
												SCREAMING_SNAKE_CASE_:			Dict  		=	96
												SCREAMING_SNAKE_CASE_:			List[str]  		=	(2, 2, 6, 2)
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	(3, 6, 12, 24)
						elif model_size == "small":
												SCREAMING_SNAKE_CASE_:			str  		=	96
												SCREAMING_SNAKE_CASE_:			Any  		=	(2, 2, 18, 2)
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	(3, 6, 12, 24)
						elif model_size == "base":
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	1_28
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	(2, 2, 18, 2)
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	(4, 8, 16, 32)
						else:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	1_92
												SCREAMING_SNAKE_CASE_:			Any  		=	(2, 2, 18, 2)
												SCREAMING_SNAKE_CASE_:			List[str]  		=	(6, 12, 24, 48)

						if "in22k" in swin_name:
												SCREAMING_SNAKE_CASE_:			Dict  		=	2_18_41
						else:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	10_00
												SCREAMING_SNAKE_CASE_:			Dict  		=	"huggingface/label-files"
												SCREAMING_SNAKE_CASE_:			Any  		=	"imagenet-1k-id2label.json"
												SCREAMING_SNAKE_CASE_:			Tuple  		=	json.load(open(hf_hub_download(_UpperCAmelCase    ,     _UpperCAmelCase    ,     repo_type="dataset"   )    ,     "r"   )   )
												SCREAMING_SNAKE_CASE_:			str  		=	{int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	idalabel
												SCREAMING_SNAKE_CASE_:			Dict  		=	{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE_:			List[Any]  		=	img_size
						SCREAMING_SNAKE_CASE_:			List[str]  		=	num_classes
						SCREAMING_SNAKE_CASE_:			List[str]  		=	embed_dim
						SCREAMING_SNAKE_CASE_:			Dict  		=	depths
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	num_heads
						SCREAMING_SNAKE_CASE_:			List[str]  		=	window_size

						return config







def       A_				(   _UpperCAmelCase   ):
						if "patch_embed.proj" in name:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	name.replace("patch_embed.proj"    ,     "embeddings.patch_embeddings.projection"   )
						if "patch_embed.norm" in name:
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	name.replace("patch_embed.norm"    ,     "embeddings.norm"   )
						if "layers" in name:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	"encoder." + name
						if "attn.proj" in name:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	name.replace("attn.proj"    ,     "attention.output.dense"   )
						if "attn" in name:
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	name.replace("attn"    ,     "attention.self"   )
						if "norm1" in name:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	name.replace("norm1"    ,     "layernorm_before"   )
						if "norm2" in name:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	name.replace("norm2"    ,     "layernorm_after"   )
						if "mlp.fc1" in name:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	name.replace("mlp.fc1"    ,     "intermediate.dense"   )
						if "mlp.fc2" in name:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	name.replace("mlp.fc2"    ,     "output.dense"   )

						if name == "norm.weight":
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	"layernorm.weight"
						if name == "norm.bias":
												SCREAMING_SNAKE_CASE_:			Any  		=	"layernorm.bias"

						if "head" in name:
												SCREAMING_SNAKE_CASE_:			Any  		=	name.replace("head"    ,     "classifier"   )
						else:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	"swin." + name

						return name







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	orig_state_dict.pop(_UpperCAmelCase   )

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

																		if "weight" in key:
																								SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	val[:dim, :]
																								SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	val[
																								    dim : dim * 2, :
																								]
																								SCREAMING_SNAKE_CASE_:			Dict  		=	val[-dim:, :]
																		else:
																								SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	val[
																								    :dim
																								]
																								SCREAMING_SNAKE_CASE_:			Any  		=	val[
																								    dim : dim * 2
																								]
																								SCREAMING_SNAKE_CASE_:			Dict  		=	val[
																								    -dim:
																								]
												else:
																		SCREAMING_SNAKE_CASE_:			Optional[int]  		=	val

						return orig_state_dict







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	timm.create_model(_UpperCAmelCase    ,     pretrained=_UpperCAmelCase   )
						timm_model.eval()

						SCREAMING_SNAKE_CASE_:			Any  		=	get_swin_config(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	SwinForImageClassification(_UpperCAmelCase   )
						model.eval()

						SCREAMING_SNAKE_CASE_:			str  		=	convert_state_dict(timm_model.state_dict()    ,     _UpperCAmelCase   )
						model.load_state_dict(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"http://images.cocodataset.org/val2017/000000039769.jpg"

						SCREAMING_SNAKE_CASE_:			Any  		=	AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,     "-"   )   )   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	Image.open(requests.get(_UpperCAmelCase    ,     stream=_UpperCAmelCase   ).raw   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	image_processor(images=_UpperCAmelCase    ,     return_tensors="pt"   )

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	timm_model(inputs["pixel_values"]   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	model(**_UpperCAmelCase   ).logits

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

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

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


if __name__ == "__main__":
						lowerCAmelCase							:  Union[str, 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							:  Dict            =						parser.parse_args()
						convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








import os

# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase							:  Optional[int]            =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	os.path.dirname(os.path.realpath(_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	os.path.join(_UpperCAmelCase    ,     "words.txt"   )

						SCREAMING_SNAKE_CASE_:			Dict  		=	""
						with open(_UpperCAmelCase   ) as f:
												SCREAMING_SNAKE_CASE_:			int  		=	f.readline()

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[word.strip("\""   ) for word in words.strip("\r\n"   ).split(","   )]
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	[
						    word
						    for word in [sum(ord(_UpperCAmelCase   ) - 64 for x in word   ) for word in words]
						    if word in TRIANGULAR_NUMBERS
						]
						return len(_UpperCAmelCase   )


if __name__ == "__main__":
						print(solution())






