
'''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					:    List[Any]           = logging.get_logger(__name__)







def 		A_(     A						:		Union[str, Any]):
							UpperCamelCase   			=     SwinConfig.from_pretrained(
							    'microsoft/swin-tiny-patch4-window7-224'		,     out_features=['stage1', 'stage2', 'stage3', 'stage4'])
							UpperCamelCase   			=     MaskFormerConfig(backbone_config=__lowerCamelCase)

							UpperCamelCase   			=     'huggingface/label-files'
							if "ade20k-full" in model_name:
														# this should be ok
														UpperCamelCase   			=     847
														UpperCamelCase   			=     'maskformer-ade20k-full-id2label.json'
							elif "ade" in model_name:
														# this should be ok
														UpperCamelCase   			=     150
														UpperCamelCase   			=     'ade20k-id2label.json'
							elif "coco-stuff" in model_name:
														# this should be ok
														UpperCamelCase   			=     171
														UpperCamelCase   			=     'maskformer-coco-stuff-id2label.json'
							elif "coco" in model_name:
														# TODO
														UpperCamelCase   			=     133
														UpperCamelCase   			=     'coco-panoptic-id2label.json'
							elif "cityscapes" in model_name:
														# this should be ok
														UpperCamelCase   			=     19
														UpperCamelCase   			=     'cityscapes-id2label.json'
							elif "vistas" in model_name:
														# this should be ok
														UpperCamelCase   			=     65
														UpperCamelCase   			=     'mapillary-vistas-id2label.json'

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

							return config







def 		A_(     A						:		Dict):
							UpperCamelCase   			=     []
							# 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_(     A						:		Optional[int]		,     A						:		List[Any]		,     A						:		List[Any]):
							UpperCamelCase   			=     dct.pop(__lowerCamelCase)
							UpperCamelCase   			=     val







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







def 		A_(     A						:		Tuple		,     A						:		Optional[int]):
							# fmt: off
							UpperCamelCase   			=     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   			=     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight''')
														UpperCamelCase   			=     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   			=     in_proj_weight[: hidden_size, :]
														UpperCamelCase   			=     in_proj_bias[:config.hidden_size]
														UpperCamelCase   			=     in_proj_weight[hidden_size : hidden_size * 2, :]
														UpperCamelCase   			=     in_proj_bias[hidden_size : hidden_size * 2]
														UpperCamelCase   			=     in_proj_weight[-hidden_size :, :]
														UpperCamelCase   			=     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   			=     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight''')
														UpperCamelCase   			=     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   			=     in_proj_weight[: hidden_size, :]
														UpperCamelCase   			=     in_proj_bias[:config.hidden_size]
														UpperCamelCase   			=     in_proj_weight[hidden_size : hidden_size * 2, :]
														UpperCamelCase   			=     in_proj_bias[hidden_size : hidden_size * 2]
														UpperCamelCase   			=     in_proj_weight[-hidden_size :, :]
														UpperCamelCase   			=     in_proj_bias[-hidden_size :]
							# fmt: on







def 		A_(     ):
							UpperCamelCase   			=     'http://images.cocodataset.org/val2017/000000039769.jpg'
							UpperCamelCase   			=     Image.open(requests.get(__lowerCamelCase		,     stream=__lowerCamelCase).raw)
							return im







@torch.no_grad()
def 		A_(     A						:		List[Any]		,     A						:		List[str]		,     A						:		str		,     A						:		List[str] = False):
							UpperCamelCase   			=     get_maskformer_config(__lowerCamelCase)

							# load original state_dict
							with open(__lowerCamelCase		,     'rb') as f:
														UpperCamelCase   			=     pickle.load(__lowerCamelCase)
							UpperCamelCase   			=     data['model']

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

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

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

							# load 🤗 model
							UpperCamelCase   			=     MaskFormerForInstanceSegmentation(__lowerCamelCase)
							model.eval()

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

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

							# verify results
							UpperCamelCase   			=     prepare_img()
							if "vistas" in model_name:
														UpperCamelCase   			=     65
							elif "cityscapes" in model_name:
														UpperCamelCase   			=     6_5535
							else:
														UpperCamelCase   			=     255
							UpperCamelCase   			=     True if 'ade' in model_name else False
							UpperCamelCase   			=     MaskFormerImageProcessor(ignore_index=__lowerCamelCase		,     reduce_labels=__lowerCamelCase)

							UpperCamelCase   			=     image_processor(__lowerCamelCase		,     return_tensors='pt')

							UpperCamelCase   			=     model(**__lowerCamelCase)

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

							if model_name == "maskformer-swin-tiny-ade":
														UpperCamelCase   			=     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]		,     __lowerCamelCase		,     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(__lowerCamelCase).mkdir(exist_ok=__lowerCamelCase)
														model.save_pretrained(__lowerCamelCase)
														image_processor.save_pretrained(__lowerCamelCase)

							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					:    Any           = 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					:    int           = parser.parse_args()
						convert_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)










from math import factorial, radians





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = 18				,     __lowerCamelCase = 10       ) ->			float:
	a									=				angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

	# Converting from degrees to radians
	a									=				radians(__lowerCamelCase       )

	a									=				angle_in_radians
	a									=				3
	a									=				-1

	for _ in range(__lowerCamelCase       ):
		result += (b * (angle_in_radians**a)) / factorial(__lowerCamelCase       )

		a									=				-b  # One positive term and the next will be negative and so on...
		a += 2  # Increased by 2 for every term.

	return round(__lowerCamelCase				,     __lowerCamelCase       )


if __name__ == "__main__":
		__import__("doctest").testmod()

