






import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  LxmertConfig.from_json_file(lowerCamelCase					)
	print(F'Building PyTorch model from configuration: {config}'					)
	__lowercase             =  LxmertForPreTraining(lowerCamelCase					)

	# Load weights from tf checkpoint
	load_tf_weights_in_lxmert(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)

	# Save pytorch-model
	print(F'Save PyTorch model to {pytorch_dump_path}'					)
	torch.save(model.state_dict()				,  lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      Optional[Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						__UpperCamelCase      :      Optional[Any]     						=						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









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





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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

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

	__lowercase             =  json.load(open(hf_hub_download(lowerCamelCase				,  lowerCamelCase				,  repo_type="""dataset"""					)				,  """r"""					)					)
	__lowercase             =  {int(lowerCamelCase					): v for k, v in idalabel.items()}

	return config





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  []
	# 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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  dct.pop(lowerCamelCase					)
	__lowercase             =  val





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
	__lowercase             =  Image.open(requests.get(lowerCamelCase				,  stream=lowerCamelCase					).raw					)
	return im





@torch.no_grad()
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = False					):




	'''simple docstring'''







	__lowercase             =  get_maskformer_config(lowerCamelCase					)

	# load original state_dict
	with open(lowerCamelCase				,  """rb"""					) as f:
		__lowercase             =  pickle.load(lowerCamelCase					)
	__lowercase             =  data["""model"""]

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

	# rename keys
	__lowercase             =  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():
		__lowercase             =  torch.from_numpy(lowerCamelCase					)

	# load 🤗 model
	__lowercase             =  MaskFormerForInstanceSegmentation(lowerCamelCase					)
	model.eval()

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

	__lowercase       ,						__lowercase             =  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
	__lowercase             =  prepare_img()
	if "vistas" in model_name:
		__lowercase             =  65
	elif "cityscapes" in model_name:
		__lowercase             =  65_535
	else:
		__lowercase             =  255
	__lowercase             =  True if """ade""" in model_name else False
	__lowercase             =  MaskFormerImageProcessor(ignore_index=lowerCamelCase				,  reduce_labels=lowerCamelCase					)

	__lowercase             =  image_processor(lowerCamelCase				,  return_tensors="""pt"""					)

	__lowercase             =  model(**lowerCamelCase					)

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

	if model_name == "maskformer-swin-tiny-ade":
		__lowercase             =  torch.tensor(
		    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
	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__":
						__UpperCamelCase      :      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."""
						)

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

