"""simple docstring"""
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 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]   )   ->			int:





						"""simple docstring"""






						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        =     9_6
												snake_case        =     (2, 2, 6, 2)
												snake_case        =     (3, 6, 1_2, 2_4)
						elif model_size == "small":
												snake_case        =     9_6
												snake_case        =     (2, 2, 1_8, 2)
												snake_case        =     (3, 6, 1_2, 2_4)
						elif model_size == "base":
												snake_case        =     1_2_8
												snake_case        =     (2, 2, 1_8, 2)
												snake_case        =     (4, 8, 1_6, 3_2)
						else:
												snake_case        =     1_9_2
												snake_case        =     (2, 2, 1_8, 2)
												snake_case        =     (6, 1_2, 2_4, 4_8)

						if "in22k" in swin_name:
												snake_case        =     2_1_8_4_1
						else:
												snake_case        =     1_0_0_0
												snake_case        =     'huggingface/label-files'
												snake_case        =     'imagenet-1k-id2label.json'
												snake_case        =     json.load(open(hf_hub_download(_UpperCamelCase	,     _UpperCamelCase	,     repo_type='dataset'   )	,     'r'   )   )
												snake_case        =     {int(_UpperCamelCase   ): 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 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						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 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Dict   )   ->			str:





						"""simple docstring"""






						for key in orig_state_dict.copy().keys():
												snake_case        =     orig_state_dict.pop(_UpperCamelCase   )

												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 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Optional[int]   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     timm.create_model(_UpperCamelCase	,     pretrained=_UpperCamelCase   )
						timm_model.eval()

						snake_case        =     get_swin_config(_UpperCamelCase   )
						snake_case        =     SwinForImageClassification(_UpperCamelCase   )
						model.eval()

						snake_case        =     convert_state_dict(timm_model.state_dict()	,     _UpperCamelCase   )
						model.load_state_dict(_UpperCamelCase   )

						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(_UpperCamelCase	,     stream=_UpperCamelCase   ).raw   )
						snake_case        =     image_processor(images=_UpperCamelCase	,     return_tensors='pt'   )

						snake_case        =     timm_model(inputs['pixel_values']   )
						snake_case        =     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__":
					SCREAMING_SNAKE_CASE__   =					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."
					)

					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



"""simple docstring"""
import os





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str = "matrix.txt"   )   ->			int:





						"""simple docstring"""






						with open(os.path.join(os.path.dirname(_UpperCamelCase   )	,     _UpperCamelCase   )   ) as in_file:
												snake_case        =     in_file.read()

						snake_case        =     [[int(_UpperCamelCase   ) for cell in row.split(','   )] for row in data.strip().splitlines()]
						snake_case        =     [[0 for cell in row] for row in grid]
						snake_case        =     len(grid[0]   )

						snake_case        =     [[0 for i in range(_UpperCamelCase   )] for j in range(_UpperCamelCase   )]
						snake_case        =     grid[0][0]
						for i in range(1	,     _UpperCamelCase   ):
												snake_case        =     grid[0][i] + dp[0][i - 1]
						for i in range(1	,     _UpperCamelCase   ):
												snake_case        =     grid[i][0] + dp[i - 1][0]

						for i in range(1	,     _UpperCamelCase   ):
												for j in range(1	,     _UpperCamelCase   ):
																		snake_case        =     grid[i][j] + min(dp[i - 1][j]	,     dp[i][j - 1]   )

						return dp[-1][-1]


if __name__ == "__main__":
					print(f"""{solution() = }""")


