
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()
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str       ) ->       str:

				"""simple docstring"""







				A__  :	Dict         =							SwinConfig.from_pretrained(
				    '''microsoft/swin-tiny-patch4-window7-224'''							,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
				A__  :	Any         =							MaskFormerConfig(backbone_config=__UpperCamelCase       )

				A__  :	Optional[Any]         =							'''huggingface/label-files'''
				if "ade20k-full" in model_name:
								# this should be ok
								A__  :	Optional[int]         =							8_47
								A__  :	Optional[int]         =							'''maskformer-ade20k-full-id2label.json'''
				elif "ade" in model_name:
								# this should be ok
								A__  :	List[Any]         =							1_50
								A__  :	Union[str, Any]         =							'''ade20k-id2label.json'''
				elif "coco-stuff" in model_name:
								# this should be ok
								A__  :	Any         =							1_71
								A__  :	Dict         =							'''maskformer-coco-stuff-id2label.json'''
				elif "coco" in model_name:
								# TODO
								A__  :	int         =							1_33
								A__  :	Any         =							'''coco-panoptic-id2label.json'''
				elif "cityscapes" in model_name:
								# this should be ok
								A__  :	Tuple         =							19
								A__  :	Union[str, Any]         =							'''cityscapes-id2label.json'''
				elif "vistas" in model_name:
								# this should be ok
								A__  :	Dict         =							65
								A__  :	int         =							'''mapillary-vistas-id2label.json'''

				A__  :	Optional[int]         =							json.load(open(hf_hub_download(__UpperCamelCase							,						__UpperCamelCase							,						repo_type='''dataset'''       )							,						'''r'''       )       )
				A__  :	Tuple         =							{int(__UpperCamelCase       ): v for k, v in idalabel.items()}

				return config

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	Optional[int]         =							[]
				# 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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Tuple							,						__UpperCamelCase :						int       ) ->       str:

				"""simple docstring"""







				A__  :	List[Any]         =							dct.pop(__UpperCamelCase       )
				A__  :	List[str]         =							val

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						List[str]       ) ->       Optional[Any]:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						str       ) ->       Optional[Any]:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Optional[int]         =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				A__  :	List[str]         =							Image.open(requests.get(__UpperCamelCase							,						stream=__UpperCamelCase       ).raw       )
				return im

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Optional[int] = False       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	str         =							get_maskformer_config(__UpperCamelCase       )

				# load original state_dict
				with open(__UpperCamelCase							,						'''rb'''       ) as f:
								A__  :	str         =							pickle.load(__UpperCamelCase       )
				A__  :	List[str]         =							data['''model''']

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

				# rename keys
				A__  :	Optional[Any]         =							create_rename_keys(__UpperCamelCase       )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )
				read_in_swin_q_k_v(__UpperCamelCase							,						config.backbone_config       )
				read_in_decoder_q_k_v(__UpperCamelCase							,						__UpperCamelCase       )

				# update to torch tensors
				for key, value in state_dict.items():
								A__  :	Tuple         =							torch.from_numpy(__UpperCamelCase       )

				# load 🤗 model
				A__  :	Any         =							MaskFormerForInstanceSegmentation(__UpperCamelCase       )
				model.eval()

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

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

				# verify results
				A__  :	List[str]         =							prepare_img()
				if "vistas" in model_name:
								A__  :	Any         =							65
				elif "cityscapes" in model_name:
								A__  :	Dict         =							6_55_35
				else:
								A__  :	Dict         =							2_55
				A__  :	Optional[Any]         =							True if '''ade''' in model_name else False
				A__  :	Optional[int]         =							MaskFormerImageProcessor(ignore_index=__UpperCamelCase							,						reduce_labels=__UpperCamelCase       )

				A__  :	Any         =							image_processor(__UpperCamelCase							,						return_tensors='''pt'''       )

				A__  :	Union[str, Any]         =							model(**__UpperCamelCase       )

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

				if model_name == "maskformer-swin-tiny-ade":
								A__  :	Optional[int]         =							torch.tensor(
								    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]       )
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]							,						__UpperCamelCase							,						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(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
								model.save_pretrained(__UpperCamelCase       )
								image_processor.save_pretrained(__UpperCamelCase       )

				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__":
	_SCREAMING_SNAKE_CASE    :      Tuple          =			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.'
	)

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				if n == 1 or not isinstance(__UpperCamelCase							,						__UpperCamelCase       ):
								return 0
				elif n == 2:
								return 1
				else:
								A__  :	Any         =							[0, 1]
								for i in range(2							,						n + 1       ):
												sequence.append(sequence[i - 1] + sequence[i - 2]       )

								return sequence[n]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				A__  :	Dict         =							0
				A__  :	Optional[int]         =							2

				while digits < n:
								index += 1
								A__  :	Dict         =							len(str(fibonacci(__UpperCamelCase       )       )       )

				return index

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int = 10_00       ) ->       int:

				"""simple docstring"""







				return fibonacci_digits_index(__UpperCamelCase       )


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))