





'''simple docstring'''







import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_       :    Union[str, Any]          =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_       :    Optional[int]          =					[]
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 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"),
    ]
)





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Dict,     SCREAMING_SNAKE_CASE__ :     Dict,     SCREAMING_SNAKE_CASE__ :     str				)	->		Dict:
						UpperCAmelCase_     : List[str]         =					state_dict.pop(SCREAMING_SNAKE_CASE__				)
						UpperCAmelCase_     : List[str]         =					val





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Tuple				)	->		List[Any]:
						UpperCAmelCase_     : Dict         =					OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		UpperCAmelCase_     : Optional[int]         =					key.replace('''backbone.0.body''',     '''backbone.conv_encoder.model'''				)
																		UpperCAmelCase_     : int         =					value
												else:
																		UpperCAmelCase_     : Union[str, Any]         =					value

						return new_state_dict





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int,     SCREAMING_SNAKE_CASE__ :     Union[str, Any]=False				)	->		List[Any]:
						UpperCAmelCase_     : Tuple         =					''''''
						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_     : Union[str, Any]         =					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
												UpperCAmelCase_     : Optional[Any]         =					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase_     : List[str]         =					in_proj_weight[:256, :]
												UpperCAmelCase_     : str         =					in_proj_bias[:256]
												UpperCAmelCase_     : Union[str, Any]         =					in_proj_weight[256:512, :]
												UpperCAmelCase_     : int         =					in_proj_bias[256:512]
												UpperCAmelCase_     : Dict         =					in_proj_weight[-256:, :]
												UpperCAmelCase_     : Union[str, 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_     : int         =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
												UpperCAmelCase_     : Union[str, Any]         =					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_     : List[str]         =					in_proj_bias[:256]
												UpperCAmelCase_     : Dict         =					in_proj_weight[256:512, :]
												UpperCAmelCase_     : Dict         =					in_proj_bias[256:512]
												UpperCAmelCase_     : Any         =					in_proj_weight[-256:, :]
												UpperCAmelCase_     : Any         =					in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												UpperCAmelCase_     : Optional[int]         =					state_dict.pop(
												    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
												UpperCAmelCase_     : int         =					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_     : Union[str, Any]         =					in_proj_weight_cross_attn[:256, :]
												UpperCAmelCase_     : Any         =					in_proj_bias_cross_attn[:256]
												UpperCAmelCase_     : Any         =					in_proj_weight_cross_attn[256:512, :]
												UpperCAmelCase_     : Dict         =					in_proj_bias_cross_attn[256:512]
												UpperCAmelCase_     : Dict         =					in_proj_weight_cross_attn[-256:, :]
												UpperCAmelCase_     : List[str]         =					in_proj_bias_cross_attn[-256:]





def     lowerCamelCase_ (					)	->		Optional[int]:
						UpperCAmelCase_     : str         =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase_     : int         =					Image.open(requests.get(SCREAMING_SNAKE_CASE__,     stream=SCREAMING_SNAKE_CASE__				).raw				)

						return im





@torch.no_grad()
def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Optional[int],     SCREAMING_SNAKE_CASE__ :     str				)	->		Dict:
						UpperCAmelCase_     : List[Any]         =					DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												UpperCAmelCase_     : Optional[Any]         =					'''resnet101'''
						if "dc5" in model_name:
												UpperCAmelCase_     : Optional[int]         =					True
						UpperCAmelCase_     : Any         =					'''panoptic''' in model_name
						if is_panoptic:
												UpperCAmelCase_     : str         =					250
						else:
												UpperCAmelCase_     : Union[str, Any]         =					91
												UpperCAmelCase_     : List[Any]         =					'''huggingface/label-files'''
												UpperCAmelCase_     : Any         =					'''coco-detection-id2label.json'''
												UpperCAmelCase_     : List[Any]         =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__,     repo_type='''dataset'''				),     '''r'''				)				)
												UpperCAmelCase_     : List[Any]         =					{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
												UpperCAmelCase_     : int         =					idalabel
												UpperCAmelCase_     : Union[str, Any]         =					{v: k for k, v in idalabel.items()}

						# load image processor
						UpperCAmelCase_     : Union[str, Any]         =					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
						UpperCAmelCase_     : Optional[int]         =					DetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

						# prepare image
						UpperCAmelCase_     : Union[str, Any]         =					prepare_img()
						UpperCAmelCase_     : Optional[Any]         =					image_processor(images=SCREAMING_SNAKE_CASE__,     return_tensors='''pt'''				)
						UpperCAmelCase_     : Union[str, Any]         =					encoding['''pixel_values''']

						logger.info(F"""Converting model {model_name}..."""				)

						# load original model from torch hub
						UpperCAmelCase_     : Any         =					torch.hub.load('''facebookresearch/detr''',     SCREAMING_SNAKE_CASE__,     pretrained=SCREAMING_SNAKE_CASE__				).eval()
						UpperCAmelCase_     : Optional[int]         =					detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		UpperCAmelCase_     : str         =					'''detr.''' + src
												rename_key(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)
						UpperCAmelCase_     : int         =					rename_backbone_keys(SCREAMING_SNAKE_CASE__				)
						# query, key and value matrices need special treatment
						read_in_q_k_v(SCREAMING_SNAKE_CASE__,     is_panoptic=SCREAMING_SNAKE_CASE__				)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						UpperCAmelCase_     : Dict         =					'''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[Any]         =					state_dict.pop(SCREAMING_SNAKE_CASE__				)
																								UpperCAmelCase_     : str         =					val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								UpperCAmelCase_     : List[str]         =					state_dict.pop(SCREAMING_SNAKE_CASE__				)
																								UpperCAmelCase_     : Optional[int]         =					val
																		elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
																								continue
																		else:
																								UpperCAmelCase_     : Any         =					state_dict.pop(SCREAMING_SNAKE_CASE__				)
																								UpperCAmelCase_     : List[Any]         =					val
												else:
																		if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
																								UpperCAmelCase_     : Dict         =					state_dict.pop(SCREAMING_SNAKE_CASE__				)
																								UpperCAmelCase_     : List[str]         =					val
    # finally, create HuggingFace model and load state dict
						UpperCAmelCase_     : Union[str, Any]         =					DetrForSegmentation(SCREAMING_SNAKE_CASE__				) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__				)
						model.load_state_dict(SCREAMING_SNAKE_CASE__				)
						model.eval()
						# verify our conversion
						UpperCAmelCase_     : Tuple         =					detr(SCREAMING_SNAKE_CASE__				)
						UpperCAmelCase_     : Dict         =					model(SCREAMING_SNAKE_CASE__				)
						assert torch.allclose(outputs.logits,     original_outputs['''pred_logits'''],     atol=1E-4				)
						assert torch.allclose(outputs.pred_boxes,     original_outputs['''pred_boxes'''],     atol=1E-4				)
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks,     original_outputs['''pred_masks'''],     atol=1E-4				)

						# Save model and image processor
						logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
						Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
						model.save_pretrained(SCREAMING_SNAKE_CASE__				)
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


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

					parser.add_argument(
					    "--model_name", default="detr_resnet50", type=str, 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."
					)
					snake_case_       :    List[Any]          =					parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








'''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 ConvNextImageProcessor


class    __a (unittest.TestCase       ):

							def __init__(							self							:       Dict    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       str=7    ,       __magic_name__							:       Optional[int]=3    ,       __magic_name__							:       Union[str, Any]=18    ,       __magic_name__							:       Dict=30    ,       __magic_name__							:       List[Any]=4_00    ,       __magic_name__							:       Dict=True    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       Optional[Any]=0.8_7_5    ,       __magic_name__							:       List[Any]=True    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					size if size is not None else {'''shortest_edge''': 20}
													UpperCAmelCase_     : Optional[Any]         =					parent
													UpperCAmelCase_     : Any         =					batch_size
													UpperCAmelCase_     : Optional[Any]         =					num_channels
													UpperCAmelCase_     : Optional[Any]         =					image_size
													UpperCAmelCase_     : Optional[int]         =					min_resolution
													UpperCAmelCase_     : Optional[Any]         =					max_resolution
													UpperCAmelCase_     : Optional[int]         =					do_resize
													UpperCAmelCase_     : Optional[Any]         =					size
													UpperCAmelCase_     : int         =					crop_pct
													UpperCAmelCase_     : Optional[int]         =					do_normalize
													UpperCAmelCase_     : Dict         =					image_mean
													UpperCAmelCase_     : int         =					image_std



							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    int:







													"""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,
													    "crop_pct": self.crop_pct,
													}





@require_torch
@require_vision
class    __a (lowerCamelCase    ,						unittest.TestCase       ):
							__a   :				Dict       =    ConvNextImageProcessor if is_vision_available() else None

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					ConvNextImageProcessingTester(self				)

							@property
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Any:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_resize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''size'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''crop_pct'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_normalize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_mean'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_std'''				)				)

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 20}				)

													UpperCAmelCase_     : List[Any]         =					self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 42}				)

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													pass

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : int         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       Image.Image				)

													# Test not batched input
													UpperCAmelCase_     : int         =					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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Tuple         =					image_processing(__magic_name__    ,       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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Optional[Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													UpperCAmelCase_     : Tuple         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       numpify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       np.ndarray				)

													# Test not batched input
													UpperCAmelCase_     : int         =					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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Union[str, Any]         =					image_processing(__magic_name__    ,       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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )



							def       UpperCAmelCase__		(							self							:       str				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       torchify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       torch.Tensor				)

													# Test not batched input
													UpperCAmelCase_     : List[Any]         =					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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : str         =					image_processing(__magic_name__    ,       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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )





