






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTMAEForPreTraining, ViTMAEModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor


class        __UpperCamelCase    :







	def __init__(  self      :							Any       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							List[Any]=13       ,				_lowerCAmelCase      :							Tuple=30       ,				_lowerCAmelCase      :							int=2       ,				_lowerCAmelCase      :							List[Any]=3       ,				_lowerCAmelCase      :							str=True       ,				_lowerCAmelCase      :							Tuple=True       ,				_lowerCAmelCase      :							Tuple=32       ,				_lowerCAmelCase      :							List[str]=5       ,				_lowerCAmelCase      :							Optional[Any]=4       ,				_lowerCAmelCase      :							Dict=37       ,				_lowerCAmelCase      :							Optional[int]="gelu"       ,				_lowerCAmelCase      :							Optional[int]=0.1       ,				_lowerCAmelCase      :							List[str]=0.1       ,				_lowerCAmelCase      :							List[str]=10       ,				_lowerCAmelCase      :							Tuple=0.02       ,				_lowerCAmelCase      :							Union[str, Any]=3       ,				_lowerCAmelCase      :							Tuple=0.6       ,				_lowerCAmelCase      :							List[Any]=None       ,				)		-> Dict:
		"""simple docstring"""







		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  image_size
		__lowercase             =  patch_size
		__lowercase             =  num_channels
		__lowercase             =  is_training
		__lowercase             =  use_labels
		__lowercase             =  hidden_size
		__lowercase             =  num_hidden_layers
		__lowercase             =  num_attention_heads
		__lowercase             =  intermediate_size
		__lowercase             =  hidden_act
		__lowercase             =  hidden_dropout_prob
		__lowercase             =  attention_probs_dropout_prob
		__lowercase             =  type_sequence_label_size
		__lowercase             =  initializer_range
		__lowercase             =  mask_ratio
		__lowercase             =  scope

		# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
		# (we add 1 for the [CLS] token)
		__lowercase             =  (image_size // patch_size) ** 2
		__lowercase             =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)







	def  _a   (  self      :							Any			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

		__lowercase             =  None
		if self.use_labels:
			__lowercase             =  ids_tensor([self.batch_size]       ,				self.type_sequence_label_size			)

		__lowercase             =  self.get_config()

		return config, pixel_values, labels







	def  _a   (  self      :							List[Any]			)		-> Tuple:
		"""simple docstring"""







		return ViTMAEConfig(
		    image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				is_decoder=_lowerCAmelCase       ,				initializer_range=self.initializer_range       ,				mask_ratio=self.mask_ratio       ,				)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							List[Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  ViTMAEModel(config=_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase			)
		self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)			)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  ViTMAEForPreTraining(_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase			)
		__lowercase             =  (self.image_size // self.patch_size) ** 2
		__lowercase             =  self.patch_size**2 * self.num_channels
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, num_patches, expected_num_channels)			)

		# test greyscale images
		__lowercase             =  1
		__lowercase             =  ViTMAEForPreTraining(_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
		__lowercase             =  model(_lowerCAmelCase			)
		__lowercase             =  self.patch_size**2
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, num_patches, expected_num_channels)			)







	def  _a   (  self      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		__lowercase       ,						__lowercase       ,						__lowercase             =  config_and_inputs
		__lowercase             =  {"""pixel_values""": pixel_values}
		return config, inputs_dict


@require_torch
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Tuple   =       (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	__snake_case	:Optional[int]   =       {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

	__snake_case	:Optional[int]   =       False
	__snake_case	:str   =       False
	__snake_case	:Dict   =       False
	__snake_case	:Any   =       False







	def  _a   (  self      :							List[Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  ViTMAEModelTester(self			)
		__lowercase             =  ConfigTester(self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase       ,				hidden_size=37			)







	def  _a   (  self      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		self.config_tester.run_common_tests()







	@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""			)
	def  _a   (  self      :							Union[str, Any]			)		-> int:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			self.assertIsInstance(model.get_input_embeddings()       ,				(nn.Module)			)
			__lowercase             =  model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(_lowerCAmelCase       ,				nn.Linear			)			)







	def  _a   (  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			__lowercase             =  inspect.signature(model.forward			)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			__lowercase             =  [*signature.parameters.keys()]

			__lowercase             =  ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Tuple			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_lowerCAmelCase			)







	def  _a   (  self      :							int			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase			)







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		np.random.seed(2			)

		__lowercase             =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2			)
		__lowercase             =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
		__lowercase             =  torch.from_numpy(_lowerCAmelCase			)

		# Add `noise` argument.
		# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
		__lowercase             =  pt_noise

		super().check_pt_tf_models(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							int			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			model.to(_lowerCAmelCase			)
			model.eval()
			# make random mask reproducible
			torch.manual_seed(2			)
			with torch.no_grad():
				__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)			)

			__lowercase             =  outputs[0].cpu().numpy()
			__lowercase             =  0

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_pretrained(_lowerCAmelCase			)
				__lowercase             =  model_class.from_pretrained(_lowerCAmelCase			)
				model.to(_lowerCAmelCase			)
				# make random mask reproducible
				torch.manual_seed(2			)
				with torch.no_grad():
					__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)			)

				# Make sure we don't have nans
				__lowercase             =  after_outputs[0].cpu().numpy()
				__lowercase             =  0
				__lowercase             =  np.amax(np.abs(out_a - out_a			)			)
				self.assertLessEqual(_lowerCAmelCase       ,				1e-5			)







	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""			)
	def  _a   (  self      :							List[str]			)		-> int:
		"""simple docstring"""







		pass







	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""			)
	def  _a   (  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		pass







	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""			)
	def  _a   (  self      :							Optional[Any]			)		-> int:
		"""simple docstring"""







		pass







	@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""			)
	def  _a   (  self      :							int			)		-> Optional[Any]:
		"""simple docstring"""







		pass







	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
	def  _a   (  self      :							Optional[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		pass







	@slow
	def  _a   (  self      :							List[str]			)		-> Dict:
		"""simple docstring"""







		for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__lowercase             =  ViTMAEModel.from_pretrained(_lowerCAmelCase			)
			self.assertIsNotNone(_lowerCAmelCase			)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image




@require_torch
@require_vision
class        __UpperCamelCase    (     unittest.TestCase   ):







	@cached_property
	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""			) if is_vision_available() else None







	@slow
	def  _a   (  self      :							Optional[Any]			)		-> int:
		"""simple docstring"""







		np.random.seed(2			)

		__lowercase             =  ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""			).to(_lowerCAmelCase			)

		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""pt"""			).to(_lowerCAmelCase			)

		# prepare a noise vector that will be also used for testing the TF model
		# (this way we can ensure that the PT and TF models operate on the same inputs)
		__lowercase             =  ViTMAEConfig()
		__lowercase             =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
		__lowercase             =  np.random.uniform(size=(1, num_patches)			)

		# forward pass
		with torch.no_grad():
			__lowercase             =  model(**_lowerCAmelCase       ,				noise=torch.from_numpy(_lowerCAmelCase			).to(device=_lowerCAmelCase			)			)

		# verify the logits
		__lowercase             =  torch.Size((1, 196, 768)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)

		__lowercase             =  torch.tensor(
		    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]			)

		self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,				expected_slice.to(_lowerCAmelCase			)       ,				atol=1e-4			)			)









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
						)

