"""simple docstring"""




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any     )	->				int:
  _lowerCAmelCase						:	Any        =					384
  _lowerCAmelCase						:	Dict        =					7
  if "tiny" in model_name:
    _lowerCAmelCase						:	Optional[int]        =					96
    _lowerCAmelCase						:	Optional[Any]        =					(2, 2, 6, 2)
    _lowerCAmelCase						:	List[str]        =					(3, 6, 12, 24)
  elif "small" in model_name:
    _lowerCAmelCase						:	List[str]        =					96
    _lowerCAmelCase						:	Union[str, Any]        =					(2, 2, 18, 2)
    _lowerCAmelCase						:	List[str]        =					(3, 6, 12, 24)
  elif "base" in model_name:
    _lowerCAmelCase						:	List[str]        =					128
    _lowerCAmelCase						:	Union[str, Any]        =					(2, 2, 18, 2)
    _lowerCAmelCase						:	str        =					(4, 8, 16, 32)
    _lowerCAmelCase						:	Optional[Any]        =					12
    _lowerCAmelCase						:	List[str]        =					512
  elif "large" in model_name:
    _lowerCAmelCase						:	Any        =					192
    _lowerCAmelCase						:	Optional[Any]        =					(2, 2, 18, 2)
    _lowerCAmelCase						:	List[Any]        =					(6, 12, 24, 48)
    _lowerCAmelCase						:	str        =					12
    _lowerCAmelCase						:	List[Any]        =					768

  # set label information
  _lowerCAmelCase						:	str        =					150
  _lowerCAmelCase						:	Union[str, Any]        =					"""huggingface/label-files"""
  _lowerCAmelCase						:	Dict        =					"""ade20k-id2label.json"""
  _lowerCAmelCase						:	Tuple        =					json.load(open(hf_hub_download(_lowerCamelCase      ,_lowerCamelCase      ,repo_type="""dataset"""     )      ,"""r"""     )     )
  _lowerCAmelCase						:	Tuple        =					{int(_lowerCamelCase     ): v for k, v in idalabel.items()}
  _lowerCAmelCase						:	int        =					{v: k for k, v in idalabel.items()}

  _lowerCAmelCase						:	Dict        =					SwinConfig(
      embed_dim=_lowerCamelCase      ,depths=_lowerCamelCase      ,num_heads=_lowerCamelCase      ,window_size=_lowerCamelCase      ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]      ,)
  _lowerCAmelCase						:	Dict        =					UperNetConfig(
      backbone_config=_lowerCamelCase      ,auxiliary_in_channels=_lowerCamelCase      ,num_labels=_lowerCamelCase      ,idalabel=_lowerCamelCase      ,labelaid=_lowerCamelCase      ,)

  return config





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict     )	->				List[Any]:
  _lowerCAmelCase						:	Tuple        =					[]

  # fmt: off
  # stem
  rename_keys.append(("""backbone.patch_embed.projection.weight""", """backbone.embeddings.patch_embeddings.projection.weight""")     )
  rename_keys.append(("""backbone.patch_embed.projection.bias""", """backbone.embeddings.patch_embeddings.projection.bias""")     )
  rename_keys.append(("""backbone.patch_embed.norm.weight""", """backbone.embeddings.norm.weight""")     )
  rename_keys.append(("""backbone.patch_embed.norm.bias""", """backbone.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.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")     )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")     )

    if i < 3:
      rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")     )
      rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")     )
      rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")     )
    rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")     )
    rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")     )

  # decode head
  rename_keys.extend(
      [
          ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
          ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
          ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
          ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
      ]     )
  # fmt: on

  return rename_keys





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	List[Any]     )	->				int:
  _lowerCAmelCase						:	str        =					dct.pop(_lowerCamelCase     )
  _lowerCAmelCase						:	List[Any]        =					val





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	List[Any]     )	->				Any:
  _lowerCAmelCase						:	int        =					[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
  for i in range(len(backbone_config.depths     )     ):
    _lowerCAmelCase						:	Optional[int]        =					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)
      _lowerCAmelCase						:	Dict        =					state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"     )
      _lowerCAmelCase						:	Dict        =					state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"     )
      # next, add query, keys and values (in that order) to the state dict
      _lowerCAmelCase						:	List[str]        =					in_proj_weight[:dim, :]
      _lowerCAmelCase						:	str        =					in_proj_bias[: dim]
      _lowerCAmelCase						:	Optional[Any]        =					in_proj_weight[
          dim : dim * 2, :
      ]
      _lowerCAmelCase						:	Optional[Any]        =					in_proj_bias[
          dim : dim * 2
      ]
      _lowerCAmelCase						:	Optional[int]        =					in_proj_weight[
          -dim :, :
      ]
      _lowerCAmelCase						:	Dict        =					in_proj_bias[-dim :]
      # fmt: on





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				Optional[Any]:
  _lowerCAmelCase						:	int        =					x.shape
  _lowerCAmelCase						:	Tuple        =					x.reshape(_lowerCamelCase      ,4      ,in_channel // 4     )
  _lowerCAmelCase						:	Optional[int]        =					x[:, [0, 2, 1, 3], :].transpose(1      ,2     ).reshape(_lowerCamelCase      ,_lowerCamelCase     )
  return x





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				Optional[Any]:
  _lowerCAmelCase						:	Optional[int]        =					x.shape
  _lowerCAmelCase						:	Optional[int]        =					x.reshape(_lowerCamelCase      ,in_channel // 4      ,4     )
  _lowerCAmelCase						:	Union[str, Any]        =					x[:, :, [0, 2, 1, 3]].transpose(1      ,2     ).reshape(_lowerCamelCase      ,_lowerCamelCase     )

  return x





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]     )	->				Any:
  _lowerCAmelCase						:	str        =					x.shape[0]
  _lowerCAmelCase						:	Any        =					x.reshape(4      ,in_channel // 4     )
  _lowerCAmelCase						:	List[Any]        =					x[[0, 2, 1, 3], :].transpose(0      ,1     ).reshape(_lowerCamelCase     )
  return x





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]     )	->				Union[str, Any]:
  _lowerCAmelCase						:	Any        =					x.shape[0]
  _lowerCAmelCase						:	Optional[int]        =					x.reshape(in_channel // 4      ,4     )
  _lowerCAmelCase						:	Optional[Any]        =					x[:, [0, 2, 1, 3]].transpose(0      ,1     ).reshape(_lowerCamelCase     )
  return x





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	str     )	->				Tuple:
  _lowerCAmelCase						:	int        =					{
      """upernet-swin-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth""",
      """upernet-swin-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth""",
      """upernet-swin-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth""",
      """upernet-swin-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth""",
  }
  _lowerCAmelCase						:	int        =					model_name_to_url[model_name]
  _lowerCAmelCase						:	Dict        =					torch.hub.load_state_dict_from_url(_lowerCamelCase      ,map_location="""cpu"""      ,file_name=_lowerCamelCase     )[
      """state_dict"""
  ]

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

  _lowerCAmelCase						:	List[Any]        =					get_upernet_config(_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					UperNetForSemanticSegmentation(_lowerCamelCase     )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    _lowerCAmelCase						:	str        =					state_dict.pop(_lowerCamelCase     )
    if "bn" in key:
      _lowerCAmelCase						:	str        =					key.replace("""bn"""      ,"""batch_norm"""     )
    _lowerCAmelCase						:	str        =					val

  # rename keys
  _lowerCAmelCase						:	Any        =					create_rename_keys(_lowerCamelCase     )
  for src, dest in rename_keys:
    rename_key(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
  read_in_q_k_v(_lowerCamelCase      ,config.backbone_config     )

  # fix downsample parameters
  for key, value in state_dict.items():
    if "downsample" in key:
      if "reduction" in key:
        _lowerCAmelCase						:	Optional[Any]        =					reverse_correct_unfold_reduction_order(_lowerCamelCase     )
      if "norm" in key:
        _lowerCAmelCase						:	Dict        =					reverse_correct_unfold_norm_order(_lowerCamelCase     )

  model.load_state_dict(_lowerCamelCase     )

  # verify on image
  _lowerCAmelCase						:	List[str]        =					"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
  _lowerCAmelCase						:	Optional[int]        =					Image.open(requests.get(_lowerCamelCase      ,stream=_lowerCamelCase     ).raw     ).convert("""RGB"""     )

  _lowerCAmelCase						:	Tuple        =					SegformerImageProcessor()
  _lowerCAmelCase						:	List[Any]        =					processor(_lowerCamelCase      ,return_tensors="""pt"""     ).pixel_values

  with torch.no_grad():
    _lowerCAmelCase						:	Optional[Any]        =					model(_lowerCamelCase     )
    _lowerCAmelCase						:	Tuple        =					outputs.logits

  print(logits.shape     )
  print("""First values of logits:"""      ,logits[0, 0, :3, :3]     )
  # assert values
  if model_name == "upernet-swin-tiny":
    _lowerCAmelCase						:	List[str]        =					torch.tensor(
        [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]     )
  elif model_name == "upernet-swin-small":
    _lowerCAmelCase						:	List[Any]        =					torch.tensor(
        [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]     )
  elif model_name == "upernet-swin-base":
    _lowerCAmelCase						:	Any        =					torch.tensor(
        [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]     )
  elif model_name == "upernet-swin-large":
    _lowerCAmelCase						:	Any        =					torch.tensor(
        [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]     )
  print("""Logits:"""      ,outputs.logits[0, 0, :3, :3]     )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,_lowerCamelCase      ,atol=1e-4     )
  print("""Looks ok!"""     )

  if pytorch_dump_folder_path is not None:
    print(f"Saving model {model_name} to {pytorch_dump_folder_path}"     )
    model.save_pretrained(_lowerCamelCase     )
    print(f"Saving processor to {pytorch_dump_folder_path}"     )
    processor.save_pretrained(_lowerCamelCase     )

  if push_to_hub:
    print(f"Pushing model and processor for {model_name} to hub"     )
    model.push_to_hub(f"openmmlab/{model_name}"     )
    processor.push_to_hub(f"openmmlab/{model_name}"     )


if __name__ == "__main__":
     _a				:		str	   =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='upernet-swin-tiny',
         type=str,
         choices=[F"""upernet-swin-{size}""" for size in ['tiny', 'small', 'base', 'large']],
         help='Name of the Swin + UperNet model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     _a				:		List[Any]	   =       parser.parse_args()
     convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""




import unittest

import numpy as np

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 PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	Tuple        =					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
       _lowerCAmelCase						:	List[str]        =					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    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					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
       _lowerCAmelCase						:	Dict        =					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
       _lowerCAmelCase						:	int        =					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    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					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
       _lowerCAmelCase						:	str        =					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
       _lowerCAmelCase						:	List[str]        =					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"""],
           )    ,					)




