

'''simple docstring'''




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()
_lowerCAmelCase         =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase         =						[]
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 						UpperCamelCase	(			a		,						a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    state_dict.pop(a			)
     __magic_name__						       =    val




def 						UpperCamelCase	(			a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__						       =    OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               __magic_name__						       =    key.replace('''backbone.0.body'''		,						'''backbone.conv_encoder.model'''			)
               __magic_name__						       =    value
          else:
               __magic_name__						       =    value

     return new_state_dict




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    ''''''

     # 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)
          __magic_name__						       =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[:256, :]
          __magic_name__						       =    in_proj_bias[:256]
          __magic_name__						       =    in_proj_weight[256:512, :]
          __magic_name__						       =    in_proj_bias[256:512]
          __magic_name__						       =    in_proj_weight[-256:, :]
          __magic_name__						       =    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
          __magic_name__						       =    state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight[:256, :]
          __magic_name__						       =    in_proj_bias[:256]
          __magic_name__						       =    in_proj_weight[256:512, :]
          __magic_name__						       =    in_proj_bias[256:512]
          __magic_name__						       =    in_proj_weight[-256:, :]
          __magic_name__						       =    in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          __magic_name__						       =    state_dict.pop(
              F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''			)
          __magic_name__						       =    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
          __magic_name__						       =    in_proj_weight_cross_attn[:256, :]
          __magic_name__						       =    in_proj_bias_cross_attn[:256]
          __magic_name__						       =    in_proj_weight_cross_attn[256:512, :]
          __magic_name__						       =    in_proj_bias_cross_attn[256:512]
          __magic_name__						       =    in_proj_weight_cross_attn[-256:, :]
          __magic_name__						       =    in_proj_bias_cross_attn[-256:]




def 						UpperCamelCase	(			a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    image.size
     __magic_name__						       =    max(a		,						a			)
     __magic_name__						       =    800 if '''detection''' in checkpoint_url else 1000
     __magic_name__						       =    target_max_size / current_max_size
     __magic_name__						       =    image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

     return resized_image




def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    F.to_tensor(a			)
     __magic_name__						       =    F.normalize(a		,						mean=[0.4_85, 0.4_56, 0.4_06]		,						std=[0.2_29, 0.2_24, 0.2_25]			)
     return image




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a		,						a			)				->     List[str]:






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

     # load original state dict
     __magic_name__						       =    torch.hub.load_state_dict_from_url(a		,						map_location='''cpu'''			)
     # rename keys
     for src, dest in rename_keys:
          rename_key(a		,						a		,						a			)
     __magic_name__						       =    rename_backbone_keys(a			)
     # query, key and value matrices need special treatment
     read_in_q_k_v(a			)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     __magic_name__						       =    '''model.'''
     for key in state_dict.copy().keys():
          if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
               __magic_name__						       =    state_dict.pop(a			)
               __magic_name__						       =    val
    # create HuggingFace model and load state dict
     __magic_name__						       =    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:
          __magic_name__						       =    15
          __magic_name__						       =    2
          __magic_name__						       =    {0: '''table''', 1: '''table rotated'''}
          __magic_name__						       =    idalabel
          __magic_name__						       =    {v: k for k, v in idalabel.items()}
     else:
          __magic_name__						       =    125
          __magic_name__						       =    6
          __magic_name__						       =    {
              0: '''table''',
              1: '''table column''',
              2: '''table row''',
              3: '''table column header''',
              4: '''table projected row header''',
              5: '''table spanning cell''',
          }
          __magic_name__						       =    idalabel
          __magic_name__						       =    {v: k for k, v in idalabel.items()}

     __magic_name__						       =    DetrImageProcessor(
         format='''coco_detection'''		,						max_size=800 if '''detection''' in checkpoint_url else 1000			)
     __magic_name__						       =    TableTransformerForObjectDetection(a			)
     model.load_state_dict(a			)
     model.eval()

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

     __magic_name__						       =    model(a			)

     if "detection" in checkpoint_url:
          __magic_name__						       =    (1, 15, 3)
          __magic_name__						       =    torch.tensor(
              [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]			)
          __magic_name__						       =    torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]			)

     else:
          __magic_name__						       =    (1, 125, 7)
          __magic_name__						       =    torch.tensor(
              [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]			)
          __magic_name__						       =    torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]			)

     assert outputs.logits.shape == expected_shape
     assert torch.allclose(outputs.logits[0, :3, :3]		,						a		,						atol=1e-4			)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,						a		,						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(a			).mkdir(exist_ok=a			)
          model.save_pretrained(a			)
          image_processor.save_pretrained(a			)

     if push_to_hub:
          # Push model to HF hub
          logger.info('''Pushing model to the hub...'''			)
          __magic_name__						       =    (
              '''microsoft/table-transformer-detection'''
              if '''detection''' in checkpoint_url
              else '''microsoft/table-transformer-structure-recognition'''
          )
          model.push_to_hub(a			)
          image_processor.push_to_hub(a			)


if __name__ == "__main__":
      _lowerCAmelCase         =						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."
      )
      _lowerCAmelCase         =						parser.parse_args()
      convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def __init__(   self		:   Tuple			,				a__		:   Union[str, Any]			,				a__		:   Union[str, Any]=7			,				a__		:   Dict=3			,				a__		:   Optional[Any]=18			,				a__		:   Optional[Any]=30			,				a__		:   Tuple=400			,				a__		:   Optional[int]=True			,				a__		:   int=None			,				a__		:   Union[str, Any]=True			,				a__		:   Optional[Any]=None			,				a__		:   str=True			,				a__		:   List[Any]=[0.5, 0.5, 0.5]			,				a__		:   Tuple=[0.5, 0.5, 0.5]			,				a__		:   Union[str, Any]=False			,				):
      __magic_name__						       =    size if size is not None else {'''height''': 20, '''width''': 20}
      __magic_name__						       =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      __magic_name__						       =    parent
      __magic_name__						       =    batch_size
      __magic_name__						       =    num_channels
      __magic_name__						       =    image_size
      __magic_name__						       =    min_resolution
      __magic_name__						       =    max_resolution
      __magic_name__						       =    do_resize
      __magic_name__						       =    size
      __magic_name__						       =    do_center_crop
      __magic_name__						       =    crop_size
      __magic_name__						       =    do_normalize
      __magic_name__						       =    image_mean
      __magic_name__						       =    image_std
      __magic_name__						       =    do_reduce_labels



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }




def 						UpperCamelCase	(			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(dataset[0]['''file''']			)
     __magic_name__						       =    Image.open(dataset[1]['''file''']			)

     return image, map




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(ds[0]['''file''']			)
     __magic_name__						       =    Image.open(ds[1]['''file''']			)
     __magic_name__						       =    Image.open(ds[2]['''file''']			)
     __magic_name__						       =    Image.open(ds[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					BeitImageProcessor if is_vision_available() else None



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    BeitImageProcessingTester(self      )



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return self.image_processor_tester.prepare_image_processor_dict()



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(a__			,				'''do_resize'''      )      )
      self.assertTrue(hasattr(a__			,				'''size'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_normalize'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_mean'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_std'''      )      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size			,				{'''height''': 20, '''width''': 20}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 18, '''width''': 18}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )

      __magic_name__						       =    self.image_processing_class.from_dict(
          self.image_processor_dict			,				size=42			,				crop_size=84			,				reduce_labels=a__      )
      self.assertEqual(image_processor.size			,				{'''height''': 42, '''width''': 42}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 84, '''width''': 84}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      pass



 def 			snake_case__  (   self		:   Dict      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				Image.Image      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Optional[int]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				numpify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				np.ndarray      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   List[str]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      __magic_name__						       =    []
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )
           maps.append(torch.zeros(image.shape[-2:]      ).long()      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				maps[0]			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test not batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_batch_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              2,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )



 def 			snake_case__  (   self		:   Any      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 150      )

      __magic_name__						       =    True
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )






