

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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






def        __lowercase   (  )  ->    List[str]:
      SCREAMING_SNAKE_CASE			:   Tuple	  = DetaConfig(
          num_queries=900    ,     encoder_ffn_dim=2048    ,     decoder_ffn_dim=2048    ,     num_feature_levels=5    ,     assign_first_stage=_A    ,     with_box_refine=_A    ,     two_stage=_A    ,     )

      # set labels
      SCREAMING_SNAKE_CASE			:   Dict	  = 91
      SCREAMING_SNAKE_CASE			:   Tuple	  = """huggingface/label-files"""
      SCREAMING_SNAKE_CASE			:   Dict	  = """coco-detection-id2label.json"""
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = json.load(open(cached_download(hf_hub_url(_A    ,     _A    ,     repo_type="""dataset"""      )      )    ,     """r"""      )      )
      SCREAMING_SNAKE_CASE			:   Tuple	  = {int(_A      ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE			:   Any	  = idalabel
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = {v: k for k, v in idalabel.items()}

      return config






def        __lowercase   (  _A      )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   int	  = []

      # stem
      # fmt: off
      rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")      )
      rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")      )
      rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")      )
      rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")      )
      rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")      )
      # stages
      for stage_idx in range(len(config.backbone_config.depths      )      ):
            for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
                            )      )
                  # 3 convs
                  for i in range(3      ):
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
                            )      )
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
                                F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                            )      )
    # transformer encoder
      for i in range(config.encoder_layers      ):
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight")      )
            rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias")      )

      # transformer decoder
      for i in range(config.decoder_layers      ):
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight")      )
            rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias")      )

      # fmt: on

      return rename_keys






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Union[str, Any]:
      SCREAMING_SNAKE_CASE			:   List[Any]	  = dct.pop(_A      )
      SCREAMING_SNAKE_CASE			:   Dict	  = val






def        __lowercase   (  _A    ,     _A      )  ->    Any:
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = config.d_model
      for i in range(config.decoder_layers      ):
            # read in weights + bias of input projection layer of self-attention
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
            SCREAMING_SNAKE_CASE			:   List[str]	  = state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias"      )
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE			:   Dict	  = in_proj_weight[:hidden_size, :]
            SCREAMING_SNAKE_CASE			:   Tuple	  = in_proj_bias[:hidden_size]
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE			:   int	  = in_proj_bias[hidden_size : hidden_size * 2]
            SCREAMING_SNAKE_CASE			:   Tuple	  = in_proj_weight[-hidden_size:, :]
            SCREAMING_SNAKE_CASE			:   List[str]	  = in_proj_bias[-hidden_size:]






def        __lowercase   (  )  ->    List[str]:
      SCREAMING_SNAKE_CASE			:   str	  = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE			:   List[Any]	  = Image.open(requests.get(_A    ,     stream=_A      ).raw      )

      return im






@torch.no_grad()
def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   Tuple	  = get_deta_config()

      # load original state dict
      if model_name == "deta-resnet-50":
            SCREAMING_SNAKE_CASE			:   Tuple	  = """adet_checkpoint0011.pth"""
      elif model_name == "deta-resnet-50-24-epochs":
            SCREAMING_SNAKE_CASE			:   str	  = """adet_2x_checkpoint0023.pth"""
      else:
            raise ValueError(F"Model name {model_name} not supported"      )
      SCREAMING_SNAKE_CASE			:   Dict	  = hf_hub_download(repo_id="""nielsr/deta-checkpoints"""    ,     filename=_A      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = torch.load(_A    ,     map_location="""cpu"""      )["""model"""]

      # rename keys
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = create_rename_keys(_A      )
      for src, dest in rename_keys:
            rename_key(_A    ,     _A    ,     _A      )
      read_in_decoder_q_k_v(_A    ,     _A      )

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = state_dict.pop(_A      )
                  SCREAMING_SNAKE_CASE			:   Any	  = val
            if "input_proj" in key:
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = state_dict.pop(_A      )
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = state_dict.pop(_A      )
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = val

    # finally, create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = DetaForObjectDetection(_A      )
      model.load_state_dict(_A      )
      model.eval()

      SCREAMING_SNAKE_CASE			:   List[Any]	  = """cuda""" if torch.cuda.is_available() else """cpu"""
      model.to(_A      )

      # load image processor
      SCREAMING_SNAKE_CASE			:   List[Any]	  = DetaImageProcessor(format="""coco_detection"""      )

      # verify our conversion on image
      SCREAMING_SNAKE_CASE			:   Dict	  = prepare_img()
      SCREAMING_SNAKE_CASE			:   Tuple	  = processor(images=_A    ,     return_tensors="""pt"""      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = encoding["""pixel_values"""]
      SCREAMING_SNAKE_CASE			:   str	  = model(pixel_values.to(_A      )      )

      # verify logits
      if model_name == "deta-resnet-50":
            SCREAMING_SNAKE_CASE			:   Dict	  = torch.tensor(
                [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]      )
            SCREAMING_SNAKE_CASE			:   int	  = torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]      )
      elif model_name == "deta-resnet-50-24-epochs":
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = torch.tensor(
                [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]      )
            SCREAMING_SNAKE_CASE			:   int	  = torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]      )

      assert torch.allclose(outputs.logits[0, :3, :3]    ,     expected_logits.to(_A      )    ,     atol=1e-4      )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,     expected_boxes.to(_A      )    ,     atol=1e-4      )
      print("""Everything ok!"""      )

      if pytorch_dump_folder_path:
            # Save model and processor
            logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."      )
            Path(_A      ).mkdir(exist_ok=_A      )
            model.save_pretrained(_A      )
            processor.save_pretrained(_A      )

      # Push to hub
      if push_to_hub:
            print("""Pushing model and processor to hub..."""      )
            model.push_to_hub(F"jozhang97/{model_name}"      )
            processor.push_to_hub(F"jozhang97/{model_name}"      )


if __name__ == "__main__":
  UpperCAmelCase__			:  Any					     =     argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      type=str,
      default="""deta-resnet-50""",
      choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
      help="""Name of the model 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."""
  )
  UpperCAmelCase__			:  Union[str, Any]					     =     parser.parse_args()
  convert_deta_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 OwlViTImageProcessor






class  a__     ( unittest.TestCase						):


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=7,					_UpperCAmelCase=3,					_UpperCAmelCase=18,					_UpperCAmelCase=30,					_UpperCAmelCase=400,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073],					_UpperCAmelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711],					_UpperCAmelCase=True,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				num_channels
        lowercase__								=				image_size
        lowercase__								=				min_resolution
        lowercase__								=				max_resolution
        lowercase__								=				do_resize
        lowercase__								=				size if size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_center_crop
        lowercase__								=				crop_size if crop_size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_normalize
        lowercase__								=				image_mean
        lowercase__								=				image_std
        lowercase__								=				do_convert_rgb


   def 						snake_case__						(  self	):
        '''simple docstring'''



        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_convert_rgb": self.do_convert_rgb,
        }






@require_torch
@require_vision
class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   OwlViTImageProcessor if is_vision_available() else None


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				OwlViTImageProcessingTester(self	)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_resize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"size"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_normalize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_mean"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_std"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_convert_rgb"	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size,					{"height": 18, "width": 18}	)
        self.assertEqual(image_processor.crop_size,					{"height": 18, "width": 18}	)

        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict,					size=42,					crop_size=84	)
        self.assertEqual(image_processor.size,					{"height": 42, "width": 42}	)
        self.assertEqual(image_processor.crop_size,					{"height": 84, "width": 84}	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase	)

        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					Image.Image	)

        # Test not batched input
        lowercase__								=				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
        lowercase__								=				image_processing(_UpperCAmelCase,					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	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase,					numpify=_UpperCAmelCase	)
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					np.ndarray	)

        # Test not batched input
        lowercase__								=				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
        lowercase__								=				image_processing(_UpperCAmelCase,					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	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase,					torchify=_UpperCAmelCase	)
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					torch.Tensor	)

        # Test not batched input
        lowercase__								=				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
        lowercase__								=				image_processing(_UpperCAmelCase,					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"],
            ),					)




