



'''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()
snake_case_ :	str         =  logging.get_logger(__name__)






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Tuple 				=   SwinConfig.from_pretrained(
						    'microsoft/swin-tiny-patch4-window7-224'  ,  out_features=['stage1', 'stage2', 'stage3', 'stage4']							)
						_UpperCamelCase					:		List[Any] 				=   MaskFormerConfig(backbone_config=__A							)

						_UpperCamelCase					:		Union[str, Any] 				=   'huggingface/label-files'
						if "ade20k-full" in model_name:
												# this should be ok
												_UpperCamelCase					:		int 				=   8_4_7
												_UpperCamelCase					:		List[Any] 				=   'maskformer-ade20k-full-id2label.json'
						elif "ade" in model_name:
												# this should be ok
												_UpperCamelCase					:		Optional[Any] 				=   1_5_0
												_UpperCamelCase					:		Dict 				=   'ade20k-id2label.json'
						elif "coco-stuff" in model_name:
												# this should be ok
												_UpperCamelCase					:		Union[str, Any] 				=   1_7_1
												_UpperCamelCase					:		Optional[Any] 				=   'maskformer-coco-stuff-id2label.json'
						elif "coco" in model_name:
												# TODO
												_UpperCamelCase					:		Any 				=   1_3_3
												_UpperCamelCase					:		Optional[Any] 				=   'coco-panoptic-id2label.json'
						elif "cityscapes" in model_name:
												# this should be ok
												_UpperCamelCase					:		Optional[int] 				=   1_9
												_UpperCamelCase					:		Optional[Any] 				=   'cityscapes-id2label.json'
						elif "vistas" in model_name:
												# this should be ok
												_UpperCamelCase					:		Tuple 				=   6_5
												_UpperCamelCase					:		Optional[Any] 				=   'mapillary-vistas-id2label.json'

						_UpperCamelCase					:		str 				=   json.load(open(hf_hub_download(__A  ,  __A  ,  repo_type='dataset'							)  ,  'r'							)							)
						_UpperCamelCase					:		str 				=   {int(__A							): v for k, v in idalabel.items()}

						return config






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		List[Any] 				=   []
						# 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__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		List[Any] 				=   dct.pop(__A							)
						_UpperCamelCase					:		Optional[Any] 				=   val






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Union[str, Any] 				=   [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
						for i in range(len(backbone_config.depths							)							):
												_UpperCamelCase					:		Dict 				=   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)
																		_UpperCamelCase					:		Optional[int] 				=   state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'							)
																		_UpperCamelCase					:		Dict 				=   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
																		_UpperCamelCase					:		Optional[int] 				=   in_proj_weight[:dim, :]
																		_UpperCamelCase					:		Tuple 				=   in_proj_bias[: dim]
																		_UpperCamelCase					:		Dict 				=   in_proj_weight[
																		    dim : dim * 2, :
																		]
																		_UpperCamelCase					:		Union[str, Any] 				=   in_proj_bias[
																		    dim : dim * 2
																		]
																		_UpperCamelCase					:		int 				=   in_proj_weight[
																		    -dim :, :
																		]
																		_UpperCamelCase					:		int 				=   in_proj_bias[-dim :]
																		# fmt: on






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						# fmt: off
						_UpperCamelCase					:		Union[str, Any] 				=   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)
												_UpperCamelCase					:		Dict 				=   state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'							)
												_UpperCamelCase					:		Tuple 				=   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
												_UpperCamelCase					:		Optional[Any] 				=   in_proj_weight[: hidden_size, :]
												_UpperCamelCase					:		Optional[int] 				=   in_proj_bias[:config.hidden_size]
												_UpperCamelCase					:		List[str] 				=   in_proj_weight[hidden_size : hidden_size * 2, :]
												_UpperCamelCase					:		Optional[Any] 				=   in_proj_bias[hidden_size : hidden_size * 2]
												_UpperCamelCase					:		Union[str, Any] 				=   in_proj_weight[-hidden_size :, :]
												_UpperCamelCase					:		List[str] 				=   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)
												_UpperCamelCase					:		str 				=   state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'							)
												_UpperCamelCase					:		Dict 				=   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
												_UpperCamelCase					:		Union[str, Any] 				=   in_proj_weight[: hidden_size, :]
												_UpperCamelCase					:		Tuple 				=   in_proj_bias[:config.hidden_size]
												_UpperCamelCase					:		Any 				=   in_proj_weight[hidden_size : hidden_size * 2, :]
												_UpperCamelCase					:		Optional[Any] 				=   in_proj_bias[hidden_size : hidden_size * 2]
												_UpperCamelCase					:		List[str] 				=   in_proj_weight[-hidden_size :, :]
												_UpperCamelCase					:		List[str] 				=   in_proj_bias[-hidden_size :]
						# fmt: on






def A__    (     ):
						_UpperCamelCase					:		Union[str, Any] 				=   'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCamelCase					:		Dict 				=   Image.open(requests.get(__A  ,  stream=__A							).raw							)
						return im






@torch.no_grad()
def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_ = False							):
						_UpperCamelCase					:		str 				=   get_maskformer_config(__A							)

						# load original state_dict
						with open(__A  ,  'rb'							) as f:
												_UpperCamelCase					:		int 				=   pickle.load(__A							)
						_UpperCamelCase					:		List[Any] 				=   data['model']

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

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

						# update to torch tensors
						for key, value in state_dict.items():
												_UpperCamelCase					:		List[Any] 				=   torch.from_numpy(__A							)

						# load 🤗 model
						_UpperCamelCase					:		Dict 				=   MaskFormerForInstanceSegmentation(__A							)
						model.eval()

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

						_UpperCamelCase				,			_UpperCamelCase					:		Optional[Any] 				=   model.load_state_dict(__A  ,  strict=__A							)
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(__A							) == 0, f'Unexpected keys: {unexpected_keys}'

						# verify results
						_UpperCamelCase					:		Union[str, Any] 				=   prepare_img()
						if "vistas" in model_name:
												_UpperCamelCase					:		int 				=   6_5
						elif "cityscapes" in model_name:
												_UpperCamelCase					:		Dict 				=   6_5_5_3_5
						else:
												_UpperCamelCase					:		Tuple 				=   2_5_5
						_UpperCamelCase					:		Tuple 				=   True if 'ade' in model_name else False
						_UpperCamelCase					:		Any 				=   MaskFormerImageProcessor(ignore_index=__A  ,  reduce_labels=__A							)

						_UpperCamelCase					:		Tuple 				=   image_processor(__A  ,  return_tensors='pt'							)

						_UpperCamelCase					:		List[Any] 				=   model(**__A							)

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

						if model_name == "maskformer-swin-tiny-ade":
												_UpperCamelCase					:		Any 				=   torch.tensor(
												    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]							)
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,  __A  ,  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(__A							).mkdir(exist_ok=__A							)
												model.save_pretrained(__A							)
												image_processor.save_pretrained(__A							)

						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__":
					snake_case_ :	Optional[int]         =  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.'
					)

					snake_case_ :	str         =  parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)




'''simple docstring'''







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase    :							str      =					(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase    :							list[int]      =					[ord(letter) for letter in string.ascii_lowercase]
lowercase    :							set[int]      =					{ord(char) for char in VALID_CHARS}

lowercase    :							list[str]      =					["the", "be", "to", "of", "and", "in", "that", "have"]







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str | None:
	_snake_case   						=		""
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42

	for keychar, cipherchar in zip(cycle(__A	)			,				__A	):
		_snake_case   						=		cipherchar ^ keychar
		if decodedchar not in VALID_INTS:
			return None
		decoded += chr(__A	)

	return decoded







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	list[str]:
	_snake_case   						=		[]
	for key in product(__A			,				repeat=3	):
		_snake_case   						=		try_key(__A			,				__A	)
		if encoded is not None:
			possibles.append(__A	)
	return possibles







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	list[str]:
	return [possible for possible in possibles if common_word in possible.lower()]







def 				SCREAMING_SNAKE_CASE__	(   __A = "p059_cipher.txt"	)       ->	int:
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		Path(__A	).parent.joinpath(__A	).read_text(encoding='utf-8'	)

	_snake_case   						=		[int(__A	) for number in data.strip().split(','	)]

	_snake_case   						=		filter_valid_chars(__A	)
	for common_word in COMMON_WORDS:
		_snake_case   						=		filter_common_word(__A			,				__A	)
		if len(__A	) == 1:
			break

	_snake_case   						=		possibles[0]
	return sum(ord(__A	) for char in decoded_text	)


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

