




"""simple docstring"""




import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:    int  							=							logging.get_logger(__name__)



def 					a__				(       snake_case__       )		->      List[str]:
			lowerCamelCase       =      SwinConfig.from_pretrained(
			    """microsoft/swin-tiny-patch4-window7-224""" ,				out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
			lowerCamelCase       =      MaskFormerConfig(backbone_config=snake_case__       )

			lowerCamelCase       =      """huggingface/label-files"""
			if "ade20k-full" in model_name:
						# this should be ok
						lowerCamelCase       =      8_47
						lowerCamelCase       =      """maskformer-ade20k-full-id2label.json"""
			elif "ade" in model_name:
						# this should be ok
						lowerCamelCase       =      1_50
						lowerCamelCase       =      """ade20k-id2label.json"""
			elif "coco-stuff" in model_name:
						# this should be ok
						lowerCamelCase       =      1_71
						lowerCamelCase       =      """maskformer-coco-stuff-id2label.json"""
			elif "coco" in model_name:
						# TODO
						lowerCamelCase       =      1_33
						lowerCamelCase       =      """coco-panoptic-id2label.json"""
			elif "cityscapes" in model_name:
						# this should be ok
						lowerCamelCase       =      19
						lowerCamelCase       =      """cityscapes-id2label.json"""
			elif "vistas" in model_name:
						# this should be ok
						lowerCamelCase       =      65
						lowerCamelCase       =      """mapillary-vistas-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()}

			return config



def 					a__				(       snake_case__       )		->      Any:
			lowerCamelCase       =      []
			# stem
			# fmt: off
			rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")       )
			rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")       )
			rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")       )
			rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')       )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')       )

						if i < 3:
									rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')       )
									rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')       )
									rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')       )
						rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')       )
						rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')       )

			# FPN
			rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")       )
			rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")       )
			rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")       )
			for source_index, target_index in zip(range(3 ,				0 ,				-1       ) ,				range(0 ,				3       )       ):
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')       )
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')       )
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')       )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')       )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')       )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')       )
			rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")       )
			rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")       )

			# Transformer decoder
			for idx in range(config.decoder_config.decoder_layers       ):
						# self-attention out projection
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')       )
						# cross-attention out projection
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')       )
						# MLP 1
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')       )
						# MLP 2
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')       )
						# layernorm 1 (self-attention layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')       )
						# layernorm 2 (cross-attention layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')       )
						# layernorm 3 (final layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')       )

			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")       )
			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")       )

			# heads on top
			rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")       )

			rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")       )
			rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")       )

			rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")       )
			rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")       )

			for i in range(3       ):
						rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')       )
						rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')       )
			# fmt: on

			return rename_keys



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[Any]:
			lowerCamelCase       =      dct.pop(snake_case__       )
			lowerCamelCase       =      val



def 					a__				(       snake_case__ ,				snake_case__       )		->      Optional[Any]:
			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.layers.{i}.blocks.{j}.attn.qkv.weight'       )
									lowerCamelCase       =      state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.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 					a__				(       snake_case__ ,				snake_case__       )		->      int:
			# fmt: off
			lowerCamelCase       =      config.decoder_config.hidden_size
			for idx in range(config.decoder_config.decoder_layers       ):
						# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
						lowerCamelCase       =      state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'       )
						lowerCamelCase       =      state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'       )
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase       =      in_proj_weight[: hidden_size, :]
						lowerCamelCase       =      in_proj_bias[:config.hidden_size]
						lowerCamelCase       =      in_proj_weight[hidden_size : hidden_size * 2, :]
						lowerCamelCase       =      in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase       =      in_proj_weight[-hidden_size :, :]
						lowerCamelCase       =      in_proj_bias[-hidden_size :]
						# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
						lowerCamelCase       =      state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'       )
						lowerCamelCase       =      state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'       )
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase       =      in_proj_weight[: hidden_size, :]
						lowerCamelCase       =      in_proj_bias[:config.hidden_size]
						lowerCamelCase       =      in_proj_weight[hidden_size : hidden_size * 2, :]
						lowerCamelCase       =      in_proj_bias[hidden_size : hidden_size * 2]
						lowerCamelCase       =      in_proj_weight[-hidden_size :, :]
						lowerCamelCase       =      in_proj_bias[-hidden_size :]
			# fmt: on



def 					a__				(       )		->      torch.Tensor:
			lowerCamelCase       =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase       =      Image.open(requests.get(snake_case__ ,				stream=snake_case__       ).raw       )
			return im



@torch.no_grad()
def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ = False       )		->      Tuple:
			lowerCamelCase       =      get_maskformer_config(snake_case__       )

			# load original state_dict
			with open(snake_case__ ,				"""rb"""       ) as f:
						lowerCamelCase       =      pickle.load(snake_case__       )
			lowerCamelCase       =      data["""model"""]

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

			# rename keys
			lowerCamelCase       =      create_rename_keys(snake_case__       )
			for src, dest in rename_keys:
						rename_key(snake_case__ ,				snake_case__ ,				snake_case__       )
			read_in_swin_q_k_v(snake_case__ ,				config.backbone_config       )
			read_in_decoder_q_k_v(snake_case__ ,				snake_case__       )

			# update to torch tensors
			for key, value in state_dict.items():
						lowerCamelCase       =      torch.from_numpy(snake_case__       )

			# load 🤗 model
			lowerCamelCase       =      MaskFormerForInstanceSegmentation(snake_case__       )
			model.eval()

			for name, param in model.named_parameters():
						print(snake_case__ ,				param.shape       )

			lowerCamelCase   ,     lowerCamelCase       =      model.load_state_dict(snake_case__ ,				strict=snake_case__       )
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(snake_case__       ) == 0, F'Unexpected keys: {unexpected_keys}'

			# verify results
			lowerCamelCase       =      prepare_img()
			if "vistas" in model_name:
						lowerCamelCase       =      65
			elif "cityscapes" in model_name:
						lowerCamelCase       =      6_55_35
			else:
						lowerCamelCase       =      2_55
			lowerCamelCase       =      True if """ade""" in model_name else False
			lowerCamelCase       =      MaskFormerImageProcessor(ignore_index=snake_case__ ,				reduce_labels=snake_case__       )

			lowerCamelCase       =      image_processor(snake_case__ ,				return_tensors="""pt"""       )

			lowerCamelCase       =      model(**snake_case__       )

			print("""Logits:""" ,				outputs.class_queries_logits[0, :3, :3]       )

			if model_name == "maskformer-swin-tiny-ade":
						lowerCamelCase       =      torch.tensor(
						    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]       )
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,				snake_case__ ,				atol=1E-4       )
			print("""Looks ok!"""       )

			if pytorch_dump_folder_path is not None:
						print(F'Saving model and image processor to {pytorch_dump_folder_path}'       )
						Path(snake_case__       ).mkdir(exist_ok=snake_case__       )
						model.save_pretrained(snake_case__       )
						image_processor.save_pretrained(snake_case__       )

			if push_to_hub:
						print("""Pushing model and image processor to the hub..."""       )
						model.push_to_hub(F'nielsr/{model_name}'       )
						image_processor.push_to_hub(F'nielsr/{model_name}'       )


if __name__ == "__main__":
			lowerCAmelCase							:    str  							=							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default="""maskformer-swin-tiny-ade""",
			    type=str,
			    help=("""Name of the MaskFormer model you'd like to convert""",),
			)
			parser.add_argument(
			    """--checkpoint_path""",
			    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
			    type=str,
			    help="""Path to the original state dict (.pth file).""",
			)
			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."""
			)

			lowerCAmelCase							:    List[Any]  							=							parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)









"""simple docstring"""




from collections.abc import Sequence



def 					a__				(       snake_case__ ,				snake_case__ = False       )		->      float:
			if not arr:
						return 0

			lowerCamelCase       =      0 if allow_empty_subarrays else float("""-inf"""       )
			lowerCamelCase       =      0.0
			for num in arr:
						lowerCamelCase       =      max(0 if allow_empty_subarrays else num ,				curr_sum + num       )
						lowerCamelCase       =      max(snake_case__ ,				snake_case__       )

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase							:    Optional[int]  							=							[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



