'''simple docstring'''




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation




def 							lowerCamelCase	(      UpperCAmelCase__       :					List[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       384
							if "tiny" in model_name:
														lowercase_					:			List[str]										=       [3, 3, 9, 3]
														lowercase_					:			Tuple										=       [96, 192, 384, 768]
							if "small" in model_name:
														lowercase_					:			str										=       [3, 3, 27, 3]
														lowercase_					:			Dict										=       [96, 192, 384, 768]
							if "base" in model_name:
														lowercase_					:			int										=       [3, 3, 27, 3]
														lowercase_					:			Union[str, Any]										=       [128, 256, 512, 1024]
														lowercase_					:			Optional[Any]										=       512
							if "large" in model_name:
														lowercase_					:			List[Any]										=       [3, 3, 27, 3]
														lowercase_					:			Tuple										=       [192, 384, 768, 1536]
														lowercase_					:			Union[str, Any]										=       768
							if "xlarge" in model_name:
														lowercase_					:			Optional[int]										=       [3, 3, 27, 3]
														lowercase_					:			List[Any]										=       [256, 512, 1024, 2048]
														lowercase_					:			List[str]										=       1024

							# set label information
							lowercase_					:			str										=       150
							lowercase_					:			str										=       """huggingface/label-files"""
							lowercase_					:			Tuple										=       """ade20k-id2label.json"""
							lowercase_					:			List[Any]										=       json.load(open(hf_hub_download(UpperCAmelCase__      ,      UpperCAmelCase__      ,      repo_type="""dataset""" )      ,      """r""" ) )
							lowercase_					:			str										=       {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
							lowercase_					:			List[Any]										=       {v: k for k, v in idalabel.items()}

							lowercase_					:			Union[str, Any]										=       ConvNextConfig(
							    depths=UpperCAmelCase__      ,      hidden_sizes=UpperCAmelCase__      ,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
							lowercase_					:			Optional[int]										=       UperNetConfig(
							    backbone_config=UpperCAmelCase__      ,      auxiliary_in_channels=UpperCAmelCase__      ,      num_labels=UpperCAmelCase__      ,      idalabel=UpperCAmelCase__      ,      labelaid=UpperCAmelCase__      ,      )

							return config




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      List[str]:
							lowercase_					:			Optional[Any]										=       []

							# fmt: off
							# stem
							rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
							rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
							rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
							rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
							# stages
							for i in range(len(config.backbone_config.depths ) ):
														for j in range(config.backbone_config.depths[i] ):
																					rename_keys.append((F'''backbone.stages.{i}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''') )
																					rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''') )
														if i > 0:
																					rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''') )
																					rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''') )
																					rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''') )
																					rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''') )

														rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''') )
														rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''') )

							# decode head
							rename_keys.extend(
							    [
							        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
							        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
							        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
							        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
							    ] )
							# fmt: on

							return rename_keys




def 							lowerCamelCase	(      UpperCAmelCase__       :					Union[str, Any]      ,      UpperCAmelCase__       :					List[Any]      ,      UpperCAmelCase__       :					Union[str, Any] )			->      int:
							lowercase_					:			List[Any]										=       dct.pop(UpperCAmelCase__ )
							lowercase_					:			List[Any]										=       val




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[int]      ,      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					List[Any] )			->      Union[str, Any]:
							lowercase_					:			Dict										=       {
							    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
							    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
							    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
							    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
							    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
							}
							lowercase_					:			Optional[Any]										=       model_name_to_url[model_name]
							lowercase_					:			Any										=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      ,      map_location="""cpu""" )["""state_dict"""]

							lowercase_					:			str										=       get_upernet_config(UpperCAmelCase__ )
							lowercase_					:			int										=       UperNetForSemanticSegmentation(UpperCAmelCase__ )
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														lowercase_					:			Dict										=       state_dict.pop(UpperCAmelCase__ )
														if "bn" in key:
																					lowercase_					:			Optional[int]										=       key.replace("""bn"""      ,      """batch_norm""" )
														lowercase_					:			List[str]										=       val

							# rename keys
							lowercase_					:			Optional[int]										=       create_rename_keys(UpperCAmelCase__ )
							for src, dest in rename_keys:
														rename_key(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )

							model.load_state_dict(UpperCAmelCase__ )

							# verify on image
							lowercase_					:			List[Any]										=       """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
							lowercase_					:			int										=       Image.open(requests.get(UpperCAmelCase__      ,      stream=UpperCAmelCase__ ).raw ).convert("""RGB""" )

							lowercase_					:			Tuple										=       SegformerImageProcessor()
							lowercase_					:			Tuple										=       processor(UpperCAmelCase__      ,      return_tensors="""pt""" ).pixel_values

							with torch.no_grad():
														lowercase_					:			int										=       model(UpperCAmelCase__ )

							if model_name == "upernet-convnext-tiny":
														lowercase_					:			Dict										=       torch.tensor(
														    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] )
							elif model_name == "upernet-convnext-small":
														lowercase_					:			Optional[int]										=       torch.tensor(
														    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] )
							elif model_name == "upernet-convnext-base":
														lowercase_					:			List[str]										=       torch.tensor(
														    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] )
							elif model_name == "upernet-convnext-large":
														lowercase_					:			Optional[Any]										=       torch.tensor(
														    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] )
							elif model_name == "upernet-convnext-xlarge":
														lowercase_					:			Tuple										=       torch.tensor(
														    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] )
							print("""Logits:"""      ,      outputs.logits[0, 0, :3, :3] )
							assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,      UpperCAmelCase__      ,      atol=1e-4 )
							print("""Looks ok!""" )

							if pytorch_dump_folder_path is not None:
														print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
														model.save_pretrained(UpperCAmelCase__ )
														print(F'''Saving processor to {pytorch_dump_folder_path}''' )
														processor.save_pretrained(UpperCAmelCase__ )

							if push_to_hub:
														print(F'''Pushing model and processor for {model_name} to hub''' )
														model.push_to_hub(F'''openmmlab/{model_name}''' )
														processor.push_to_hub(F'''openmmlab/{model_name}''' )


if __name__ == "__main__":
						_lowercase						: Optional[Any]						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="upernet-convnext-tiny",
						    type=str,
						    choices=[f"""upernet-convnext-{size}""" for size in ["tiny", "small", "base", "large", "xlarge"]],
						    help="Name of the ConvNext UperNet 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."
						)
						parser.add_argument(
						    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
						)

						_lowercase						: List[str]						=						parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

							with open(os.path.join(os.path.dirname(UpperCAmelCase__ )      ,      UpperCAmelCase__ ) ) as input_file:
														lowercase_					:			str										=       [
														    [int(UpperCAmelCase__ ) for element in line.split(""",""" )]
														    for line in input_file.readlines()
														]

							lowercase_					:			Optional[Any]										=       len(UpperCAmelCase__ )
							lowercase_					:			Any										=       len(matrix[0] )

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

							for j in range(1      ,      UpperCAmelCase__ ):
														for i in range(UpperCAmelCase__ ):
																					lowercase_					:			Union[str, Any]										=       minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1      ,      UpperCAmelCase__ ):
																					lowercase_					:			Tuple										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i - 1][j] + matrix[i][j] )

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i + 1][j] + matrix[i][j] )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )


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





