
import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      384
			lowerCAmelCase_         =      7
			if "tiny" in model_name:
						lowerCAmelCase_         =      96
						lowerCAmelCase_         =      (2, 2, 6, 2)
						lowerCAmelCase_         =      (3, 6, 12, 24)
			elif "small" in model_name:
						lowerCAmelCase_         =      96
						lowerCAmelCase_         =      (2, 2, 18, 2)
						lowerCAmelCase_         =      (3, 6, 12, 24)
			elif "base" in model_name:
						lowerCAmelCase_         =      128
						lowerCAmelCase_         =      (2, 2, 18, 2)
						lowerCAmelCase_         =      (4, 8, 16, 32)
						lowerCAmelCase_         =      12
						lowerCAmelCase_         =      512
			elif "large" in model_name:
						lowerCAmelCase_         =      192
						lowerCAmelCase_         =      (2, 2, 18, 2)
						lowerCAmelCase_         =      (6, 12, 24, 48)
						lowerCAmelCase_         =      12
						lowerCAmelCase_         =      768

			# set label information
			lowerCAmelCase_         =      150
			lowerCAmelCase_         =      "huggingface/label-files"
			lowerCAmelCase_         =      "ade20k-id2label.json"
			lowerCAmelCase_         =      json.load(open(hf_hub_download(__lowerCAmelCase       ,				__lowerCAmelCase       ,				repo_type="dataset"							)       ,				"r"							)							)
			lowerCAmelCase_         =      {int(__lowerCAmelCase							): v for k, v in idalabel.items()}
			lowerCAmelCase_         =      {v: k for k, v in idalabel.items()}

			lowerCAmelCase_         =      SwinConfig(
			    embed_dim=__lowerCAmelCase       ,				depths=__lowerCAmelCase       ,				num_heads=__lowerCAmelCase       ,				window_size=__lowerCAmelCase       ,				out_features=["stage1", "stage2", "stage3", "stage4"]       ,				)
			lowerCAmelCase_         =      UperNetConfig(
			    backbone_config=__lowerCAmelCase       ,				auxiliary_in_channels=__lowerCAmelCase       ,				num_labels=__lowerCAmelCase       ,				idalabel=__lowerCAmelCase       ,				labelaid=__lowerCAmelCase       ,				)

			return config






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      []

			# fmt: off
			# stem
			rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")							)
			rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")							)
			rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")							)
			rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.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}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")							)
									rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")							)

						if i < 3:
									rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")							)
									rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")							)
									rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.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__							(							__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      dct.pop(__lowerCAmelCase							)
			lowerCAmelCase_         =      val






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
			for i in range(len(backbone_config.depths							)							):
						lowerCAmelCase_         =      num_features[i]
						for j in range(backbone_config.depths[i]							):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									lowerCAmelCase_         =      state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""							)
									lowerCAmelCase_         =      state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""							)
									# next, add query, keys and values (in that order) to the state dict
									lowerCAmelCase_         =      in_proj_weight[:dim, :]
									lowerCAmelCase_         =      in_proj_bias[: dim]
									lowerCAmelCase_         =      in_proj_weight[
									    dim : dim * 2, :
									]
									lowerCAmelCase_         =      in_proj_bias[
									    dim : dim * 2
									]
									lowerCAmelCase_         =      in_proj_weight[
									    -dim :, :
									]
									lowerCAmelCase_         =      in_proj_bias[-dim :]
									# fmt: on






def   lowerCamelCase__							(							__lowerCAmelCase    :    str							):




			"""simple docstring"""


			lowerCAmelCase_      ,    lowerCAmelCase_         =      x.shape
			lowerCAmelCase_         =      x.reshape(__lowerCAmelCase       ,				4       ,				in_channel // 4							)
			lowerCAmelCase_         =      x[:, [0, 2, 1, 3], :].transpose(1       ,				2							).reshape(__lowerCAmelCase       ,				__lowerCAmelCase							)
			return x






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]							):




			"""simple docstring"""


			lowerCAmelCase_      ,    lowerCAmelCase_         =      x.shape
			lowerCAmelCase_         =      x.reshape(__lowerCAmelCase       ,				in_channel // 4       ,				4							)
			lowerCAmelCase_         =      x[:, :, [0, 2, 1, 3]].transpose(1       ,				2							).reshape(__lowerCAmelCase       ,				__lowerCAmelCase							)

			return x






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      x.shape[0]
			lowerCAmelCase_         =      x.reshape(4       ,				in_channel // 4							)
			lowerCAmelCase_         =      x[[0, 2, 1, 3], :].transpose(0       ,				1							).reshape(__lowerCAmelCase							)
			return x






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      x.shape[0]
			lowerCAmelCase_         =      x.reshape(in_channel // 4       ,				4							)
			lowerCAmelCase_         =      x[:, [0, 2, 1, 3]].transpose(0       ,				1							).reshape(__lowerCAmelCase							)
			return x






def   lowerCamelCase__							(							__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      {
			    "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
			    "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
			    "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
			    "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
			}
			lowerCAmelCase_         =      model_name_to_url[model_name]
			lowerCAmelCase_         =      torch.hub.load_state_dict_from_url(__lowerCAmelCase       ,				map_location="cpu"       ,				file_name=__lowerCAmelCase							)[
			    "state_dict"
			]

			for name, param in state_dict.items():
						print(__lowerCAmelCase       ,				param.shape							)

			lowerCAmelCase_         =      get_upernet_config(__lowerCAmelCase							)
			lowerCAmelCase_         =      UperNetForSemanticSegmentation(__lowerCAmelCase							)
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						lowerCAmelCase_         =      state_dict.pop(__lowerCAmelCase							)
						if "bn" in key:
									lowerCAmelCase_         =      key.replace("bn"       ,				"batch_norm"							)
						lowerCAmelCase_         =      val

			# rename keys
			lowerCAmelCase_         =      create_rename_keys(__lowerCAmelCase							)
			for src, dest in rename_keys:
						rename_key(__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)
			read_in_q_k_v(__lowerCAmelCase       ,				config.backbone_config							)

			# fix downsample parameters
			for key, value in state_dict.items():
						if "downsample" in key:
									if "reduction" in key:
												lowerCAmelCase_         =      reverse_correct_unfold_reduction_order(__lowerCAmelCase							)
									if "norm" in key:
												lowerCAmelCase_         =      reverse_correct_unfold_norm_order(__lowerCAmelCase							)

			model.load_state_dict(__lowerCAmelCase							)

			# verify on image
			lowerCAmelCase_         =      "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
			lowerCAmelCase_         =      Image.open(requests.get(__lowerCAmelCase       ,				stream=__lowerCAmelCase							).raw							).convert("RGB"							)

			lowerCAmelCase_         =      SegformerImageProcessor()
			lowerCAmelCase_         =      processor(__lowerCAmelCase       ,				return_tensors="pt"							).pixel_values

			with torch.no_grad():
						lowerCAmelCase_         =      model(__lowerCAmelCase							)
						lowerCAmelCase_         =      outputs.logits

			print(logits.shape							)
			print("First values of logits:"       ,				logits[0, 0, :3, :3]							)
			# assert values
			if model_name == "upernet-swin-tiny":
						lowerCAmelCase_         =      torch.tensor(
						    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]							)
			elif model_name == "upernet-swin-small":
						lowerCAmelCase_         =      torch.tensor(
						    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]							)
			elif model_name == "upernet-swin-base":
						lowerCAmelCase_         =      torch.tensor(
						    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]							)
			elif model_name == "upernet-swin-large":
						lowerCAmelCase_         =      torch.tensor(
						    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]							)
			print("Logits:"       ,				outputs.logits[0, 0, :3, :3]							)
			assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,				__lowerCAmelCase       ,				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(__lowerCAmelCase							)
						print(F"""Saving processor to {pytorch_dump_folder_path}"""							)
						processor.save_pretrained(__lowerCAmelCase							)

			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__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="upernet-swin-tiny",
			    type=str,
			    choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
			    help="Name of the Swin + 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."
			)

			_A  =	parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations

_A  =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the reference grid
			lowerCAmelCase_         =      1
			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the action grid

			lowerCAmelCase_         =      init[0]
			lowerCAmelCase_         =      init[1]
			lowerCAmelCase_         =      0
			lowerCAmelCase_         =      g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_         =      [[f, g, x, y]]

			lowerCAmelCase_         =      False  # flag that is set when search is complete
			lowerCAmelCase_         =      False  # flag set if we can't find expand

			while not found and not resign:
						if len(__lowerCAmelCase							) == 0:
									raise ValueError("Algorithm is unable to find solution"							)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									lowerCAmelCase_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

									if x == goal[0] and y == goal[1]:
												lowerCAmelCase_         =      True
									else:
												for i in range(len(__lowerCAmelCase							)							):  # to try out different valid actions
															lowerCAmelCase_         =      x + DIRECTIONS[i][0]
															lowerCAmelCase_         =      y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(__lowerCAmelCase							) and ya >= 0 and ya < len(grid[0]							):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					lowerCAmelCase_         =      g + cost
																					lowerCAmelCase_         =      ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]							)
																					lowerCAmelCase_         =      1
																					lowerCAmelCase_         =      i
			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      goal[0]
			lowerCAmelCase_         =      goal[1]
			invpath.append([x, y]							)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						lowerCAmelCase_         =      x - DIRECTIONS[action[x][y]][0]
						lowerCAmelCase_         =      y - DIRECTIONS[action[x][y]][1]
						lowerCAmelCase_         =      xa
						lowerCAmelCase_         =      ya
						invpath.append([x, y]							)

			lowerCAmelCase_         =      []
			for i in range(len(__lowerCAmelCase							)							):
						path.append(invpath[len(__lowerCAmelCase							) - 1 - i]							)
			return path, action


if __name__ == "__main__":
			_A  =	[
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 1, 0],
			    [0, 0, 0, 0, 1, 0],
			]

			_A  =	[0, 0]
			# all coordinates are given in format [y,x]
			_A  =	[len(grid) - 1, len(grid[0]) - 1]
			_A  =	1

			# the cost map which pushes the path closer to the goal
			_A  =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
			for i in range(len(grid)):
						for j in range(len(grid[0])):
									_A  =	abs(i - goal[0]) + abs(j - goal[1])
									if grid[i][j] == 1:
												# added extra penalty in the heuristic map
												_A  =	99

			_A, _A  =	search(grid, init, goal, cost, heuristic)

			print("ACTION MAP")
			for i in range(len(action)):
						print(action[i])

			for i in range(len(path)):
						print(path[i])



