

a__												=						'''Input must be a string of 8 numbers plus letter'''
a__												=						'''TRWAGMYFPDXBNJZSQVHLCKE'''



def   __UpperCAmelCase					(      __a :     str      )     ->	bool:

					"""simple docstring"""

					if not isinstance(__a						,__a      ):
										_a      :       List[str]   =						F"""Expected string as input, found {type(__a      ).__name__}"""
										raise TypeError(__a      )

					_a      :       List[Any]   =						spanish_id.replace('''-'''						,''''''      ).upper()
					if len(__a      ) != 9:
										raise ValueError(__a      )

					try:
										_a      :       Any   =						int(spanish_id_clean[0:8]      )
										_a      :       str   =						spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__a      ) from ex

					if letter.isdigit():
										raise ValueError(__a      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






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()
a__												=						logging.get_logger(__name__)



def   __UpperCAmelCase					(      __a :     str      )     ->	List[Any]:

					"""simple docstring"""

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

					_a      :       Optional[Any]   =						'''huggingface/label-files'''
					if "ade20k-full" in model_name:
										# this should be ok
										_a      :       Optional[Any]   =						847
										_a      :       List[Any]   =						'''maskformer-ade20k-full-id2label.json'''
					elif "ade" in model_name:
										# this should be ok
										_a      :       Union[str, Any]   =						150
										_a      :       Any   =						'''ade20k-id2label.json'''
					elif "coco-stuff" in model_name:
										# this should be ok
										_a      :       int   =						171
										_a      :       List[str]   =						'''maskformer-coco-stuff-id2label.json'''
					elif "coco" in model_name:
										# TODO
										_a      :       Dict   =						133
										_a      :       Optional[Any]   =						'''coco-panoptic-id2label.json'''
					elif "cityscapes" in model_name:
										# this should be ok
										_a      :       List[Any]   =						19
										_a      :       Optional[Any]   =						'''cityscapes-id2label.json'''
					elif "vistas" in model_name:
										# this should be ok
										_a      :       List[Any]   =						65
										_a      :       Dict   =						'''mapillary-vistas-id2label.json'''

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

					return config



def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Optional[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   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]						,__a :     Optional[Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       str   =						dct.pop(__a      )
					_a      :       str   =						val



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Union[str, Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       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      )      ):
										_a      :       Optional[Any]   =						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      :       List[Any]   =						state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""      )
															_a      :       Optional[int]   =						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      :       List[Any]   =						in_proj_bias[: dim]
															_a      :       Optional[int]   =						in_proj_weight[
															    dim : dim * 2, :
															]
															_a      :       Tuple   =						in_proj_bias[
															    dim : dim * 2
															]
															_a      :       int   =						in_proj_weight[
															    -dim :, :
															]
															_a      :       Optional[int]   =						in_proj_bias[-dim :]
															# fmt: on



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]      )     ->	List[Any]:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      )     ->	torch.Tensor:

					"""simple docstring"""

					_a      :       str   =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a      :       Dict   =						Image.open(requests.get(__a						,stream=__a      ).raw      )
					return im



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     str						,__a :     str						,__a :     str						,__a :     bool = False      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						get_maskformer_config(__a      )

					# load original state_dict
					with open(__a						,'''rb'''      ) as f:
										_a      :       str   =						pickle.load(__a      )
					_a      :       Union[str, Any]   =						data['''model''']

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

					# rename keys
					_a      :       Any   =						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():
										_a      :       Optional[int]   =						torch.from_numpy(__a      )

					# load 🤗 model
					_a      :       Dict   =						MaskFormerForInstanceSegmentation(__a      )
					model.eval()

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

					_a ,   _a      :       Tuple   =						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
					_a      :       Union[str, Any]   =						prepare_img()
					if "vistas" in model_name:
										_a      :       int   =						65
					elif "cityscapes" in model_name:
										_a      :       Tuple   =						65_535
					else:
										_a      :       str   =						255
					_a      :       Dict   =						True if '''ade''' in model_name else False
					_a      :       Optional[Any]   =						MaskFormerImageProcessor(ignore_index=__a						,reduce_labels=__a      )

					_a      :       Optional[Any]   =						image_processor(__a						,return_tensors='''pt'''      )

					_a      :       int   =						model(**__a      )

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

					if model_name == "maskformer-swin-tiny-ade":
										_a      :       Union[str, Any]   =						torch.tensor(
										    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]      )
					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__":
			a__												=						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.'''
			)

			a__												=						parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)



