




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


def 		__magic_name__    (	__UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				SwinConfig.from_pretrained(
					    '''microsoft/swin-tiny-patch4-window7-224''',      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
					snake_case_									=				MaskFormerConfig(backbone_config=__UpperCAmelCase	)

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

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

					return config


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				[]
					# 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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				dct.pop(__UpperCAmelCase	)
					snake_case_									=				val


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				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)
										snake_case_									=				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"	)
										snake_case_									=				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
										snake_case_									=				in_proj_weight[: hidden_size, :]
										snake_case_									=				in_proj_bias[:config.hidden_size]
										snake_case_									=				in_proj_weight[hidden_size : hidden_size * 2, :]
										snake_case_									=				in_proj_bias[hidden_size : hidden_size * 2]
										snake_case_									=				in_proj_weight[-hidden_size :, :]
										snake_case_									=				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)
										snake_case_									=				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"	)
										snake_case_									=				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
										snake_case_									=				in_proj_weight[: hidden_size, :]
										snake_case_									=				in_proj_bias[:config.hidden_size]
										snake_case_									=				in_proj_weight[hidden_size : hidden_size * 2, :]
										snake_case_									=				in_proj_bias[hidden_size : hidden_size * 2]
										snake_case_									=				in_proj_weight[-hidden_size :, :]
										snake_case_									=				in_proj_bias[-hidden_size :]
					# fmt: on


def 		__magic_name__    (	)		->					torch.Tensor:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(__UpperCAmelCase,      stream=__UpperCAmelCase	).raw	)
					return im


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = False	)		->					int:

					'''simple docstring'''

					snake_case_									=				get_maskformer_config(__UpperCAmelCase	)

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

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

					# rename keys
					snake_case_									=				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():
										snake_case_									=				torch.from_numpy(__UpperCAmelCase	)

					# load 🤗 model
					snake_case_									=				MaskFormerForInstanceSegmentation(__UpperCAmelCase	)
					model.eval()

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

					snake_case_			,snake_case_									=				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
					snake_case_									=				prepare_img()
					if "vistas" in model_name:
										snake_case_									=				65
					elif "cityscapes" in model_name:
										snake_case_									=				6_5535
					else:
										snake_case_									=				255
					snake_case_									=				True if '''ade''' in model_name else False
					snake_case_									=				MaskFormerImageProcessor(ignore_index=__UpperCAmelCase,      reduce_labels=__UpperCAmelCase	)

					snake_case_									=				image_processor(__UpperCAmelCase,      return_tensors='''pt'''	)

					snake_case_									=				model(**__UpperCAmelCase	)

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

					if model_name == "maskformer-swin-tiny-ade":
										snake_case_									=				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__":
							a  :				List[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.'
							)

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







'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							a  :				Union[str, Any]									=  True
except (ImportError, ModuleNotFoundError):
							a  :				Any									=  False

if NLTK_AVAILABLE:
							with FileLock('.lock') as lock:
														nltk.download('punkt', quiet=True)


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					re.sub('''<n>''',      '''''',      __UpperCAmelCase	)  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__UpperCAmelCase	)	)

