





import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase	:					str			  =					logging.get_logger(__name__)



def 		A_    (					_lowerCAmelCase					)						->			Tuple:
 # initialize config
 if "resnet-50" in model_name:
  UpperCamelCase     : List[str]	     =       ResNetConfig.from_pretrained("microsoft/resnet-50"					)
 elif "resnet-101" in model_name:
  UpperCamelCase     : List[str]	     =       ResNetConfig.from_pretrained("microsoft/resnet-101"					)
 else:
  raise ValueError("Model name should include either resnet50 or resnet101"					)

 UpperCamelCase     : Any	     =       DetrConfig(use_timm_backbone=_lowerCAmelCase							,       backbone_config=_lowerCAmelCase					)

 # set label attributes
 UpperCamelCase     : str	     =       "panoptic" in model_name
 if is_panoptic:
  UpperCamelCase     : Dict	     =       250
 else:
  UpperCamelCase     : Optional[Any]	     =       91
  UpperCamelCase     : Union[str, Any]	     =       "huggingface/label-files"
  UpperCamelCase     : Optional[int]	     =       "coco-detection-id2label.json"
  UpperCamelCase     : Optional[Any]	     =       json.load(open(hf_hub_download(_lowerCAmelCase							,       _lowerCAmelCase							,       repo_type="dataset"					)							,       "r"					)					)
  UpperCamelCase     : List[str]	     =       {int(_lowerCAmelCase					): v for k, v in idalabel.items()}
  UpperCamelCase     : List[str]	     =       idalabel
  UpperCamelCase     : Optional[int]	     =       {v: k for k, v in idalabel.items()}

 return config, is_panoptic



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

 # stem
 # fmt: off
 rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")					)
 rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")					)
 rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")					)
 rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")					)
 rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
        )					)
    # fmt: on

 for i in range(config.encoder_layers					):
  # 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 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.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"),
     ]					)

 return rename_keys



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Optional[int]:
 UpperCamelCase     : Optional[Any]	     =       state_dict.pop(_lowerCAmelCase					)
 UpperCamelCase     : str	     =       val



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=False					)						->			Dict:
 UpperCamelCase     : str	     =       ""
 if is_panoptic:
  UpperCamelCase     : int	     =       "detr."

 # 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     : Tuple	     =       state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
  UpperCamelCase     : int	     =       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     : Optional[int]	     =       in_proj_weight[:256, :]
  UpperCamelCase     : List[Any]	     =       in_proj_bias[:256]
  UpperCamelCase     : Any	     =       in_proj_weight[256:512, :]
  UpperCamelCase     : str	     =       in_proj_bias[256:512]
  UpperCamelCase     : Any	     =       in_proj_weight[-256:, :]
  UpperCamelCase     : Optional[Any]	     =       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     : Union[str, Any]	     =       state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
  UpperCamelCase     : Dict	     =       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     : Optional[Any]	     =       in_proj_weight[:256, :]
  UpperCamelCase     : Any	     =       in_proj_bias[:256]
  UpperCamelCase     : str	     =       in_proj_weight[256:512, :]
  UpperCamelCase     : Union[str, Any]	     =       in_proj_bias[256:512]
  UpperCamelCase     : Any	     =       in_proj_weight[-256:, :]
  UpperCamelCase     : Optional[Any]	     =       in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  UpperCamelCase     : List[Any]	     =       state_dict.pop(
      F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
  UpperCamelCase     : Union[str, 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     : Any	     =       in_proj_weight_cross_attn[:256, :]
  UpperCamelCase     : Dict	     =       in_proj_bias_cross_attn[:256]
  UpperCamelCase     : Any	     =       in_proj_weight_cross_attn[256:512, :]
  UpperCamelCase     : int	     =       in_proj_bias_cross_attn[256:512]
  UpperCamelCase     : Dict	     =       in_proj_weight_cross_attn[-256:, :]
  UpperCamelCase     : Optional[Any]	     =       in_proj_bias_cross_attn[-256:]



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

 return im



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=False					)						->			int:
 UpperCamelCase     ,   UpperCamelCase     : int	     =       get_detr_config(_lowerCAmelCase					)

 # load original model from torch hub
 UpperCamelCase     : Union[str, Any]	     =       {
     "detr-resnet-50": "detr_resnet50",
     "detr-resnet-101": "detr_resnet101",
 }
 logger.info(F"""Converting model {model_name}..."""					)
 UpperCamelCase     : Optional[int]	     =       torch.hub.load("facebookresearch/detr"							,       model_name_to_original_name[model_name]							,       pretrained=_lowerCAmelCase					).eval()
 UpperCamelCase     : Union[str, Any]	     =       detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(_lowerCAmelCase					):
  if is_panoptic:
   UpperCamelCase     : Union[str, Any]	     =       "detr." + src
  rename_key(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
 # query, key and value matrices need special treatment
 read_in_q_k_v(_lowerCAmelCase							,       is_panoptic=_lowerCAmelCase					)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 UpperCamelCase     : List[Any]	     =       "detr.model." if is_panoptic else "model."
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith("detr"					)
       and not key.startswith("class_labels_classifier"					)
       and not key.startswith("bbox_predictor"					)
   ):
    UpperCamelCase     : List[str]	     =       state_dict.pop(_lowerCAmelCase					)
    UpperCamelCase     : List[str]	     =       val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    UpperCamelCase     : Optional[int]	     =       state_dict.pop(_lowerCAmelCase					)
    UpperCamelCase     : Tuple	     =       val
   elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
    continue
   else:
    UpperCamelCase     : Dict	     =       state_dict.pop(_lowerCAmelCase					)
    UpperCamelCase     : Optional[int]	     =       val
  else:
   if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
    UpperCamelCase     : Union[str, Any]	     =       state_dict.pop(_lowerCAmelCase					)
    UpperCamelCase     : int	     =       val

    # finally, create HuggingFace model and load state dict
 UpperCamelCase     : str	     =       DetrForSegmentation(_lowerCAmelCase					) if is_panoptic else DetrForObjectDetection(_lowerCAmelCase					)
 model.load_state_dict(_lowerCAmelCase					)
 model.eval()

 # verify our conversion on an image
 UpperCamelCase     : Optional[Any]	     =       "coco_panoptic" if is_panoptic else "coco_detection"
 UpperCamelCase     : Optional[Any]	     =       DetrImageProcessor(format=_lowerCAmelCase					)

 UpperCamelCase     : int	     =       processor(images=prepare_img()							,       return_tensors="pt"					)
 UpperCamelCase     : Optional[int]	     =       encoding["pixel_values"]

 UpperCamelCase     : Optional[int]	     =       detr(_lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       model(_lowerCAmelCase					)

 assert torch.allclose(outputs.logits							,       original_outputs["pred_logits"]							,       atol=1e-3					)
 assert torch.allclose(outputs.pred_boxes							,       original_outputs["pred_boxes"]							,       atol=1e-3					)
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks							,       original_outputs["pred_masks"]							,       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(_lowerCAmelCase					).mkdir(exist_ok=_lowerCAmelCase					)
  model.save_pretrained(_lowerCAmelCase					)
  processor.save_pretrained(_lowerCAmelCase					)

 if push_to_hub:
  # Upload model and image processor to the hub
  logger.info("Uploading PyTorch model and image processor to the hub..."					)
  model.push_to_hub(F"""nielsr/{model_name}"""					)
  processor.push_to_hub(F"""nielsr/{model_name}"""					)


if __name__ == "__main__":
    __lowerCamelCase	:					Tuple			  =					argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        default="""detr-resnet-50""",
        type=str,
        choices=["""detr-resnet-50""", """detr-resnet-101"""],
        help="""Name of the DETR 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 to push the model to the hub or not.""")
    __lowerCamelCase	:					int			  =					parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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 ViTImageProcessor



class  A__       (	unittest.TestCase      ):





      def __init__(			self	,							A_	,							A_=13	,							A_=3	,							A_=224	,							A_=30	,							A_=400	,							A_=True	,							A_=None	,							A_=True	,							A_=[0.5, 0.5, 0.5]	,							A_=[0.5, 0.5, 0.5]	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase     : Tuple	     =       parent
       UpperCamelCase     : Tuple	     =       batch_size
       UpperCamelCase     : Tuple	     =       num_channels
       UpperCamelCase     : str	     =       image_size
       UpperCamelCase     : Optional[int]	     =       min_resolution
       UpperCamelCase     : List[Any]	     =       max_resolution
       UpperCamelCase     : Union[str, Any]	     =       do_resize
       UpperCamelCase     : str	     =       size
       UpperCamelCase     : List[str]	     =       do_normalize
       UpperCamelCase     : Any	     =       image_mean
       UpperCamelCase     : int	     =       image_std





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




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       ViTImageProcessor if is_vision_available() else None





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




       UpperCamelCase     : int	     =       EfficientFormerImageProcessorTester(self       )





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




       return self.image_proc_tester.prepare_image_processor_dict()





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




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(A_	,							"image_mean"       )       )
       self.assertTrue(hasattr(A_	,							"image_std"       )       )
       self.assertTrue(hasattr(A_	,							"do_normalize"       )       )
       self.assertTrue(hasattr(A_	,							"do_resize"       )       )
       self.assertTrue(hasattr(A_	,							"size"       )       )





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




       pass





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




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       UpperCamelCase     : Union[str, Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							Image.Image       )

       # Test not batched input
       UpperCamelCase     : Optional[Any]	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Tuple	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





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




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       UpperCamelCase     : List[Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							numpify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							np.ndarray       )

       # Test not batched input
       UpperCamelCase     : Dict	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Optional[Any]	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





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




       UpperCamelCase     : Dict	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       UpperCamelCase     : Any	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							torchify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							torch.Tensor       )

       # Test not batched input
       UpperCamelCase     : Any	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Any	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)
