




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

					if is_vision_available():
										from transformers import MaskFormerImageProcessor

if is_vision_available():
					from PIL import Image

class   __a:






				"""simple docstring"""





				def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=10       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=32 * 4       ,_SCREAMING_SNAKE_CASE=32 * 6       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=32       ,)			->		Tuple:
									UpperCAmelCase_      :							Optional[int]       =      parent
									UpperCAmelCase_      :							str       =      batch_size
									UpperCAmelCase_      :							str       =      is_training
									UpperCAmelCase_      :							Optional[int]       =      use_auxiliary_loss
									UpperCAmelCase_      :							Any       =      num_queries
									UpperCAmelCase_      :							List[Any]       =      num_channels
									UpperCAmelCase_      :							Any       =      min_size
									UpperCAmelCase_      :							List[str]       =      max_size
									UpperCAmelCase_      :							Tuple       =      num_labels
									UpperCAmelCase_      :							str       =      mask_feature_size





				def 							a__				(				self  )			->		Tuple:
									UpperCAmelCase_      :							Union[str, Any]       =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]  ).to(
									    lowercase_  )

									UpperCAmelCase_      :							Optional[int]       =      torch.ones([self.batch_size, self.min_size, self.max_size]       ,device=lowercase_  )

									UpperCAmelCase_      :							Optional[Any]       =      (
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,device=lowercase_  ) > 0.5
									).float()
									UpperCAmelCase_      :							Tuple       =      (torch.rand((self.batch_size, self.num_labels)       ,device=lowercase_  ) > 0.5).long()

									UpperCAmelCase_      :							Optional[int]       =      self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels





				def 							a__				(				self  )			->		Tuple:
									return MaskFormerConfig.from_backbone_and_decoder_configs(
									    backbone_config=SwinConfig(
									        depths=[1, 1, 1, 1]       ,)       ,decoder_config=DetrConfig(
									        decoder_ffn_dim=128       ,num_queries=self.num_queries       ,decoder_attention_heads=2       ,d_model=self.mask_feature_size       ,)       ,mask_feature_size=self.mask_feature_size       ,fpn_feature_size=self.mask_feature_size       ,num_channels=self.num_channels       ,num_labels=self.num_labels       ,)





				def 							a__				(				self  )			->		Optional[Any]:
									UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							Optional[int]       =      self.prepare_config_and_inputs()
									UpperCAmelCase_      :							Optional[Any]       =      {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
									return config, inputs_dict





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
									UpperCAmelCase_      :							Optional[Any]       =      output.encoder_hidden_states
									UpperCAmelCase_      :							List[str]       =      output.pixel_decoder_hidden_states
									UpperCAmelCase_      :							int       =      output.transformer_decoder_hidden_states

									self.parent.assertTrue(len(lowercase_  )       ,len(config.backbone_config.depths  )  )
									self.parent.assertTrue(len(lowercase_  )       ,len(config.backbone_config.depths  )  )
									self.parent.assertTrue(len(lowercase_  )       ,config.decoder_config.decoder_layers  )





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		Dict:
									with torch.no_grad():
														UpperCAmelCase_      :							Dict       =      MaskFormerModel(config=lowercase_  )
														model.to(lowercase_  )
														model.eval()

														UpperCAmelCase_      :							Any       =      model(pixel_values=lowercase_       ,pixel_mask=lowercase_  )
														UpperCAmelCase_      :							Optional[Any]       =      model(lowercase_       ,output_hidden_states=lowercase_  )
									# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
									# encoder and pixel decoder
									self.parent.assertEqual(
									    output.transformer_decoder_last_hidden_state.shape       ,(self.batch_size, self.num_queries, self.mask_feature_size)       ,)
									# let's ensure the other two hidden state exists
									self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None  )
									self.parent.assertTrue(output.encoder_last_hidden_state is not None  )

									if output_hidden_states:
														self.check_output_hidden_state(lowercase_       ,lowercase_  )





				def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
									UpperCAmelCase_      :							str       =      MaskFormerForInstanceSegmentation(config=lowercase_  )
									model.to(lowercase_  )
									model.eval()

									def comm_check_on_output(_SCREAMING_SNAKE_CASE  ):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None  )
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None  )
														self.parent.assertTrue(result.encoder_last_hidden_state is not None  )
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape       ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       ,)
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape       ,(self.batch_size, self.num_queries, self.num_labels + 1)  )

									with torch.no_grad():
														UpperCAmelCase_      :							Union[str, Any]       =      model(pixel_values=lowercase_       ,pixel_mask=lowercase_  )
														UpperCAmelCase_      :							int       =      model(lowercase_  )

														comm_check_on_output(lowercase_  )

														UpperCAmelCase_      :							List[Any]       =      model(
														    pixel_values=lowercase_       ,pixel_mask=lowercase_       ,mask_labels=lowercase_       ,class_labels=lowercase_  )

									comm_check_on_output(lowercase_  )

									self.parent.assertTrue(result.loss is not None  )
									self.parent.assertEqual(result.loss.shape       ,torch.Size([1]  )  )


@require_torch
class   __a(					_lowerCamelCase    ,						_lowerCamelCase    ,						unittest.TestCase       ):






				"""simple docstring"""





				lowerCAmelCase        =				(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
				lowerCAmelCase        =				(
				    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
				    if is_torch_available()
				    else {}
				)

				lowerCAmelCase        =				False
				lowerCAmelCase        =				False
				lowerCAmelCase        =				False
				lowerCAmelCase        =				False





				def 							a__				(				self  )			->		str:
									UpperCAmelCase_      :							int       =      MaskFormerModelTester(self  )
									UpperCAmelCase_      :							Optional[int]       =      ConfigTester(self       ,config_class=lowercase_       ,has_text_modality=lowercase_  )





				def 							a__				(				self  )			->		List[Any]:
									self.config_tester.run_common_tests()





				def 							a__				(				self  )			->		str:
									UpperCAmelCase_,   UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(lowercase_       ,**lowercase_       ,output_hidden_states=lowercase_  )





				def 							a__				(				self  )			->		Any:
									UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowercase_  )





				@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''  )
				def 							a__				(				self  )			->		int:
									pass





				@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''  )
				def 							a__				(				self  )			->		Tuple:
									pass





				@unittest.skip(reason='''MaskFormer is not a generative model'''  )
				def 							a__				(				self  )			->		Tuple:
									pass





				@unittest.skip(reason='''MaskFormer does not use token embeddings'''  )
				def 							a__				(				self  )			->		List[str]:
									pass





				@require_torch_multi_gpu
				@unittest.skip(
				    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''  )
				def 							a__				(				self  )			->		Optional[int]:
									pass





				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def 							a__				(				self  )			->		Tuple:
									pass





				def 							a__				(				self  )			->		Tuple:
									UpperCAmelCase_,   UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCAmelCase_      :							str       =      ['''pixel_values''']
														self.assertListEqual(arg_names[:1]       ,lowercase_  )





				@slow
				def 							a__				(				self  )			->		Dict:
									for model_name in ["facebook/maskformer-swin-small-coco"]:
														UpperCAmelCase_      :							List[str]       =      MaskFormerModel.from_pretrained(lowercase_  )
														self.assertIsNotNone(lowercase_  )





				def 							a__				(				self  )			->		Tuple:
									UpperCAmelCase_      :							int       =      (self.model_tester.min_size,) * 2
									UpperCAmelCase_      :							int       =      {
									    '''pixel_values''': torch.randn((2, 3, *size)       ,device=lowercase_  ),
									    '''mask_labels''': torch.randn((2, 10, *size)       ,device=lowercase_  ),
									    '''class_labels''': torch.zeros(2       ,10       ,device=lowercase_  ).long(),
									}

									UpperCAmelCase_      :							Optional[int]       =      MaskFormerForInstanceSegmentation(MaskFormerConfig()  ).to(lowercase_  )
									UpperCAmelCase_      :							Tuple       =      model(**lowercase_  )
									self.assertTrue(outputs.loss is not None  )





				def 							a__				(				self  )			->		str:
									UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(lowercase_       ,**lowercase_       ,output_hidden_states=lowercase_  )





				def 							a__				(				self  )			->		Dict:
									UpperCAmelCase_,   UpperCAmelCase_      :							Dict       =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														UpperCAmelCase_      :							List[str]       =      model_class(lowercase_  ).to(lowercase_  )
														UpperCAmelCase_      :							str       =      model(**lowercase_       ,output_attentions=lowercase_  )
														self.assertTrue(outputs.attentions is not None  )





				def 							a__				(				self  )			->		Optional[Any]:
									if not self.model_tester.is_training:
														return
									# only MaskFormerForInstanceSegmentation has the loss
									UpperCAmelCase_      :							int       =      self.all_model_classes[1]
									UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							Union[str, Any]       =      self.model_tester.prepare_config_and_inputs()

									UpperCAmelCase_      :							Optional[Any]       =      model_class(lowercase_  )
									model.to(lowercase_  )
									model.train()

									UpperCAmelCase_      :							Optional[int]       =      model(lowercase_       ,mask_labels=lowercase_       ,class_labels=lowercase_  ).loss
									loss.backward()





				def 							a__				(				self  )			->		List[Any]:
									# only MaskFormerForInstanceSegmentation has the loss
									UpperCAmelCase_      :							Tuple       =      self.all_model_classes[1]
									UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							Union[str, Any]       =      self.model_tester.prepare_config_and_inputs()
									UpperCAmelCase_      :							Tuple       =      True
									UpperCAmelCase_      :							Optional[Any]       =      True

									UpperCAmelCase_      :							Union[str, Any]       =      model_class(lowercase_  )
									model.to(lowercase_  )
									model.train()

									UpperCAmelCase_      :							List[Any]       =      model(lowercase_       ,mask_labels=lowercase_       ,class_labels=lowercase_  )

									UpperCAmelCase_      :							Tuple       =      outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									UpperCAmelCase_      :							str       =      outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()
									# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
									UpperCAmelCase_      :							Optional[Any]       =      outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									UpperCAmelCase_      :							Any       =      outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=lowercase_  )

									self.assertIsNotNone(encoder_hidden_states.grad  )
									self.assertIsNotNone(pixel_decoder_hidden_states.grad  )
									self.assertIsNotNone(transformer_decoder_hidden_states.grad  )
									self.assertIsNotNone(attentions.grad  )


__a          =			1E-4







def lowerCamelCase__       (      ):

					'''simple docstring'''


					UpperCAmelCase_      :							Any       =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
					return image



@require_vision
@slow
class   __a(					unittest.TestCase       ):






				"""simple docstring"""





				@cached_property
				def 							a__				(				self  )			->		Any:
									return (
									    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''  )
									    if is_vision_available()
									    else None
									)





				def 							a__				(				self  )			->		Dict:
									UpperCAmelCase_      :							Union[str, Any]       =      MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''  ).to(lowercase_  )
									UpperCAmelCase_      :							Dict       =      self.default_image_processor
									UpperCAmelCase_      :							Optional[int]       =      prepare_img()
									UpperCAmelCase_      :							Optional[int]       =      image_processor(lowercase_       ,return_tensors='''pt'''  ).to(lowercase_  )
									UpperCAmelCase_      :							Union[str, Any]       =      inputs['''pixel_values'''].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0  )
									# check size
									self.assertEqual(lowercase_       ,(1, 3, 800, 1_088)  )

									with torch.no_grad():
														UpperCAmelCase_      :							Any       =      model(**lowercase_  )

									UpperCAmelCase_      :							Any       =      torch.tensor(
									    [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]  ).to(lowercase_  )
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,lowercase_       ,atol=lowercase_  )  )

									UpperCAmelCase_      :							Any       =      torch.tensor(
									    [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]  ).to(lowercase_  )
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,lowercase_       ,atol=lowercase_  )  )

									UpperCAmelCase_      :							List[str]       =      torch.tensor(
									    [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]  ).to(lowercase_  )
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,lowercase_       ,atol=lowercase_  )  )





				def 							a__				(				self  )			->		Tuple:
									UpperCAmelCase_      :							Union[str, Any]       =      (
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''  )
									    .to(lowercase_  )
									    .eval()
									)
									UpperCAmelCase_      :							Optional[int]       =      self.default_image_processor
									UpperCAmelCase_      :							int       =      prepare_img()
									UpperCAmelCase_      :							Union[str, Any]       =      image_processor(lowercase_       ,return_tensors='''pt'''  ).to(lowercase_  )
									UpperCAmelCase_      :							str       =      inputs['''pixel_values'''].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0  )
									# check size
									self.assertEqual(lowercase_       ,(1, 3, 800, 1_088)  )

									with torch.no_grad():
														UpperCAmelCase_      :							Any       =      model(**lowercase_  )
									# masks_queries_logits
									UpperCAmelCase_      :							int       =      outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape       ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,)
									UpperCAmelCase_      :							str       =      [
									    [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
									    [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
									    [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
									]
									UpperCAmelCase_      :							Dict       =      torch.tensor(lowercase_  ).to(lowercase_  )
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,lowercase_       ,atol=lowercase_  )  )
									# class_queries_logits
									UpperCAmelCase_      :							Any       =      outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape       ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)  )
									UpperCAmelCase_      :							str       =      torch.tensor(
									    [
									        [1.65_12e00, -5.25_72e00, -3.35_19e00],
									        [3.61_69e-02, -5.90_25e00, -2.93_13e00],
									        [1.07_66e-04, -7.76_30e00, -5.12_63e00],
									    ]  ).to(lowercase_  )
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,lowercase_       ,atol=lowercase_  )  )





				def 							a__				(				self  )			->		Optional[int]:
									UpperCAmelCase_      :							Dict       =      (
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''  )
									    .to(lowercase_  )
									    .eval()
									)
									UpperCAmelCase_      :							Union[str, Any]       =      self.default_image_processor
									UpperCAmelCase_      :							Optional[Any]       =      prepare_img()
									UpperCAmelCase_      :							Any       =      image_processor(lowercase_       ,return_tensors='''pt'''  ).to(lowercase_  )
									UpperCAmelCase_      :							Dict       =      inputs['''pixel_values'''].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0  )
									# check size
									self.assertEqual(lowercase_       ,(1, 3, 800, 1_088)  )

									with torch.no_grad():
														UpperCAmelCase_      :							str       =      model(**lowercase_  )
									# masks_queries_logits
									UpperCAmelCase_      :							int       =      outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape       ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,)
									UpperCAmelCase_      :							str       =      [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
									UpperCAmelCase_      :							Tuple       =      torch.tensor(lowercase_  ).to(lowercase_  )
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,lowercase_       ,atol=lowercase_  )  )
									# class_queries_logits
									UpperCAmelCase_      :							List[str]       =      outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape       ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)  )
									UpperCAmelCase_      :							Dict       =      torch.tensor(
									    [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]  ).to(lowercase_  )
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,lowercase_       ,atol=lowercase_  )  )





				def 							a__				(				self  )			->		int:
									UpperCAmelCase_      :							Tuple       =      (
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''  )
									    .to(lowercase_  )
									    .eval()
									)
									UpperCAmelCase_      :							Tuple       =      self.default_image_processor

									UpperCAmelCase_      :							Dict       =      image_processor(
									    [np.zeros((3, 800, 1_333)  ), np.zeros((3, 800, 1_333)  )]       ,segmentation_maps=[np.zeros((384, 384)  ).astype(np.floataa  ), np.zeros((384, 384)  ).astype(np.floataa  )]       ,return_tensors='''pt'''       ,)

									UpperCAmelCase_      :							str       =      inputs['''pixel_values'''].to(lowercase_  )
									UpperCAmelCase_      :							List[str]       =      [el.to(lowercase_  ) for el in inputs['''mask_labels''']]
									UpperCAmelCase_      :							Optional[Any]       =      [el.to(lowercase_  ) for el in inputs['''class_labels''']]

									with torch.no_grad():
														UpperCAmelCase_      :							str       =      model(**lowercase_  )

									self.assertTrue(outputs.loss is not None  )





import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
__a          =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__a          =			[]
for i in range(6):
     # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
     rename_keys.append(
         (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
     )
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
     rename_keys.append(
         (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
     )
     rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
     # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
     )
     rename_keys.append(
         (
             F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
             F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
         )
     )
     rename_keys.append(
         (
             F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
             F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
         )
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      state_dict.pop(_lowercase				)
     UpperCAmelCase_      :							Optional[int]       =      val







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCAmelCase_      :							List[Any]       =      key.replace('''backbone.0.body'''			,						'''backbone.conv_encoder.model'''				)
               UpperCAmelCase_      :							Optional[Any]       =      value
          else:
               UpperCAmelCase_      :							Union[str, Any]       =      value

     return new_state_dict







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      ''''''

     # first: transformer encoder
     for i in range(6				):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          UpperCAmelCase_      :							Dict       =      state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
          UpperCAmelCase_      :							Any       =      state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''				)
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_      :							Union[str, Any]       =      in_proj_weight[:256, :]
          UpperCAmelCase_      :							Optional[int]       =      in_proj_bias[:256]
          UpperCAmelCase_      :							Tuple       =      in_proj_weight[256:512, :]
          UpperCAmelCase_      :							List[Any]       =      in_proj_bias[256:512]
          UpperCAmelCase_      :							Union[str, Any]       =      in_proj_weight[-256:, :]
          UpperCAmelCase_      :							str       =      in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6				):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase_      :							Any       =      state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
          UpperCAmelCase_      :							Optional[int]       =      state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''				)
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_      :							Union[str, Any]       =      in_proj_weight[:256, :]
          UpperCAmelCase_      :							List[str]       =      in_proj_bias[:256]
          UpperCAmelCase_      :							Optional[int]       =      in_proj_weight[256:512, :]
          UpperCAmelCase_      :							str       =      in_proj_bias[256:512]
          UpperCAmelCase_      :							Optional[Any]       =      in_proj_weight[-256:, :]
          UpperCAmelCase_      :							Union[str, Any]       =      in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          UpperCAmelCase_      :							List[str]       =      state_dict.pop(
              f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
          UpperCAmelCase_      :							List[Any]       =      state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''				)
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          UpperCAmelCase_      :							List[str]       =      in_proj_weight_cross_attn[:256, :]
          UpperCAmelCase_      :							str       =      in_proj_bias_cross_attn[:256]
          UpperCAmelCase_      :							int       =      in_proj_weight_cross_attn[256:512, :]
          UpperCAmelCase_      :							Tuple       =      in_proj_bias_cross_attn[256:512]
          UpperCAmelCase_      :							List[str]       =      in_proj_weight_cross_attn[-256:, :]
          UpperCAmelCase_      :							Dict       =      in_proj_bias_cross_attn[-256:]







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      image.size
     UpperCAmelCase_      :							List[Any]       =      max(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      800 if '''detection''' in checkpoint_url else 1000
     UpperCAmelCase_      :							Any       =      target_max_size / current_max_size
     UpperCAmelCase_      :							Any       =      image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

     return resized_image







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      F.to_tensor(_lowercase				)
     UpperCAmelCase_      :							Optional[Any]       =      F.normalize(_lowercase			,						mean=[0.485, 0.456, 0.406]			,						std=[0.229, 0.224, 0.225]				)
     return image







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     logger.info('''Converting model...'''				)

     # load original state dict
     UpperCAmelCase_      :							Any       =      torch.hub.load_state_dict_from_url(_lowercase			,						map_location='''cpu'''				)
     # rename keys
     for src, dest in rename_keys:
          rename_key(_lowercase			,						_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							Optional[int]       =      rename_backbone_keys(_lowercase				)
     # query, key and value matrices need special treatment
     read_in_q_k_v(_lowercase				)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCAmelCase_      :							int       =      '''model.'''
     for key in state_dict.copy().keys():
          if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
               UpperCAmelCase_      :							int       =      state_dict.pop(_lowercase				)
               UpperCAmelCase_      :							Dict       =      val
    # create HuggingFace model and load state dict
     UpperCAmelCase_      :							str       =      TableTransformerConfig(
         backbone='''resnet18'''			,						mask_loss_coefficient=1			,						dice_loss_coefficient=1			,						ce_loss_coefficient=1			,						bbox_loss_coefficient=5			,						giou_loss_coefficient=2			,						eos_coefficient=0.4			,						class_cost=1			,						bbox_cost=5			,						giou_cost=2			,						)

     if "detection" in checkpoint_url:
          UpperCAmelCase_      :							Any       =      15
          UpperCAmelCase_      :							List[str]       =      2
          UpperCAmelCase_      :							Union[str, Any]       =      {0: '''table''', 1: '''table rotated'''}
          UpperCAmelCase_      :							str       =      idalabel
          UpperCAmelCase_      :							Optional[int]       =      {v: k for k, v in idalabel.items()}
     else:
          UpperCAmelCase_      :							Optional[Any]       =      125
          UpperCAmelCase_      :							Optional[Any]       =      6
          UpperCAmelCase_      :							Dict       =      {
              0: '''table''',
              1: '''table column''',
              2: '''table row''',
              3: '''table column header''',
              4: '''table projected row header''',
              5: '''table spanning cell''',
          }
          UpperCAmelCase_      :							Optional[Any]       =      idalabel
          UpperCAmelCase_      :							Optional[int]       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Union[str, Any]       =      DetrImageProcessor(
         format='''coco_detection'''			,						max_size=800 if '''detection''' in checkpoint_url else 1000				)
     UpperCAmelCase_      :							Union[str, Any]       =      TableTransformerForObjectDetection(_lowercase				)
     model.load_state_dict(_lowercase				)
     model.eval()

     # verify our conversion
     UpperCAmelCase_      :							str       =      '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
     UpperCAmelCase_      :							Dict       =      hf_hub_download(repo_id='''nielsr/example-pdf'''			,						repo_type='''dataset'''			,						filename=_lowercase				)
     UpperCAmelCase_      :							Dict       =      Image.open(_lowercase				).convert('''RGB'''				)
     UpperCAmelCase_      :							Any       =      normalize(resize(_lowercase			,						_lowercase				)				).unsqueeze(0				)

     UpperCAmelCase_      :							Dict       =      model(_lowercase				)

     if "detection" in checkpoint_url:
          UpperCAmelCase_      :							Any       =      (1, 15, 3)
          UpperCAmelCase_      :							Optional[int]       =      torch.tensor(
              [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]				)
          UpperCAmelCase_      :							Any       =      torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]				)

     else:
          UpperCAmelCase_      :							Any       =      (1, 125, 7)
          UpperCAmelCase_      :							Any       =      torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]				)
          UpperCAmelCase_      :							str       =      torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]				)

     assert outputs.logits.shape == expected_shape
     assert torch.allclose(outputs.logits[0, :3, :3]			,						_lowercase			,						atol=1E-4				)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,						_lowercase			,						atol=1E-4				)
     print('''Looks ok!'''				)

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
          Path(_lowercase				).mkdir(exist_ok=_lowercase				)
          model.save_pretrained(_lowercase				)
          image_processor.save_pretrained(_lowercase				)

     if push_to_hub:
          # Push model to HF hub
          logger.info('''Pushing model to the hub...'''				)
          UpperCAmelCase_      :							List[Any]       =      (
              '''microsoft/table-transformer-detection'''
              if '''detection''' in checkpoint_url
              else '''microsoft/table-transformer-structure-recognition'''
          )
          model.push_to_hub(_lowercase				)
          image_processor.push_to_hub(_lowercase				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_url',
         default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
         type=str,
         choices=[
             'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
             'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
         ],
         help='URL of the Table Transformer checkpoint you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     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_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)