




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()
UpperCAmelCase_		:				List[str]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# initialize config
				if "resnet-50" in model_name:
								__magic_name__	:	int								=ResNetConfig.from_pretrained("""microsoft/resnet-50"""   )
				elif "resnet-101" in model_name:
								__magic_name__	:	Any								=ResNetConfig.from_pretrained("""microsoft/resnet-101"""   )
				else:
								raise ValueError("""Model name should include either resnet50 or resnet101"""   )

				__magic_name__	:	Union[str, Any]								=DetrConfig(use_timm_backbone=lowerCamelCase	,     backbone_config=lowerCamelCase   )

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

				return config, is_panoptic




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				__magic_name__	:	List[str]								=[]

				# 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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	List[str]								=state_dict.pop(lowerCamelCase   )
				__magic_name__	:	Dict								=val




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




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Optional[Any]								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__	:	Optional[Any]								=Image.open(requests.get(lowerCamelCase	,     stream=lowerCamelCase   ).raw   )

				return im




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	int								=get_detr_config(lowerCamelCase   )

				# load original model from torch hub
				__magic_name__	:	str								={
				    """detr-resnet-50""": """detr_resnet50""",
				    """detr-resnet-101""": """detr_resnet101""",
				}
				logger.info(F"Converting model {model_name}..."   )
				__magic_name__	:	Any								=torch.hub.load("""facebookresearch/detr"""	,     model_name_to_original_name[model_name]	,     pretrained=lowerCamelCase   ).eval()
				__magic_name__	:	Union[str, Any]								=detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(lowerCamelCase   ):
								if is_panoptic:
												__magic_name__	:	List[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
				__magic_name__	:	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"""   )
												):
																__magic_name__	:	Any								=state_dict.pop(lowerCamelCase   )
																__magic_name__	:	List[Any]								=val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																__magic_name__	:	str								=state_dict.pop(lowerCamelCase   )
																__magic_name__	:	Dict								=val
												elif key.startswith("""bbox_attention"""   ) or key.startswith("""mask_head"""   ):
																continue
												else:
																__magic_name__	:	int								=state_dict.pop(lowerCamelCase   )
																__magic_name__	:	Tuple								=val
								else:
												if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
																__magic_name__	:	Any								=state_dict.pop(lowerCamelCase   )
																__magic_name__	:	List[str]								=val

    # finally, create HuggingFace model and load state dict
				__magic_name__	:	int								=DetrForSegmentation(lowerCamelCase   ) if is_panoptic else DetrForObjectDetection(lowerCamelCase   )
				model.load_state_dict(lowerCamelCase   )
				model.eval()

				# verify our conversion on an image
				__magic_name__	:	Union[str, Any]								="""coco_panoptic""" if is_panoptic else """coco_detection"""
				__magic_name__	:	str								=DetrImageProcessor(format=lowerCamelCase   )

				__magic_name__	:	List[Any]								=processor(images=prepare_img()	,     return_tensors="""pt"""   )
				__magic_name__	:	Optional[Any]								=encoding["""pixel_values"""]

				__magic_name__	:	Union[str, Any]								=detr(lowerCamelCase   )
				__magic_name__	:	Tuple								=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__":
							UpperCAmelCase_		:				List[str]									=     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.")
							UpperCAmelCase_		:				List[str]									=     parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor




class 		__A      (			unittest.TestCase			):

						def __init__(		self   :List[str]    , __snake_case   :str    , __snake_case   :List[Any]=7    , __snake_case   :List[str]=3    , __snake_case   :int=30    , __snake_case   :int=4_00    , __snake_case   :List[str]=True    , __snake_case   :List[str]=None    , __snake_case   :Tuple=True    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Union[str, Any]=True    , __snake_case   :str=1 / 2_55    , __snake_case   :int=True    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__magic_name__	:	str								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=num_channels
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	Optional[Any]								=max_resolution
										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Any								=size
										__magic_name__	:	str								=do_normalize
										__magic_name__	:	str								=image_mean
										__magic_name__	:	List[Any]								=image_std
										__magic_name__	:	Optional[int]								=do_rescale
										__magic_name__	:	List[str]								=rescale_factor
										__magic_name__	:	Optional[int]								=do_pad

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

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


						def     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

										if not batched:
														__magic_name__	:	Any								=image_inputs[0]
														if isinstance(__snake_case    , Image.Image ):
																		__magic_name__				, __magic_name__	:	Dict								=image.size
														else:
																		__magic_name__				, __magic_name__	:	Dict								=image.shape[1], image.shape[2]
														if w < h:
																		__magic_name__	:	Any								=int(self.size["""shortest_edge"""] * h / w )
																		__magic_name__	:	Dict								=self.size["""shortest_edge"""]
														elif w > h:
																		__magic_name__	:	Union[str, Any]								=self.size["""shortest_edge"""]
																		__magic_name__	:	Optional[int]								=int(self.size["""shortest_edge"""] * w / h )
														else:
																		__magic_name__	:	Any								=self.size["""shortest_edge"""]
																		__magic_name__	:	List[str]								=self.size["""shortest_edge"""]

										else:
														__magic_name__	:	List[str]								=[]
														for image in image_inputs:
																		__magic_name__				, __magic_name__	:	Tuple								=self.get_expected_values([image] )
																		expected_values.append((expected_height, expected_width) )
														__magic_name__	:	Optional[int]								=max(__snake_case    , key=lambda __snake_case : item[0] )[0]
														__magic_name__	:	Tuple								=max(__snake_case    , key=lambda __snake_case : item[1] )[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					DetaImageProcessor if is_vision_available() else None

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=DetaImageProcessingTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(__snake_case    , """image_mean""" ) )
										self.assertTrue(hasattr(__snake_case    , """image_std""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_normalize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_rescale""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_pad""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size    , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
										self.assertEqual(image_processor.do_pad    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										__magic_name__	:	Tuple								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , Image.Image )

										# Test not batched input
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	str								=self.image_processor_tester.get_expected_values(__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

										# Test batched
										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    , )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

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

										# Test not batched input
										__magic_name__	:	str								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

										# Test batched
										__magic_name__	:	List[str]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Any								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    , )

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	Dict								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , torchify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , torch.Tensor )

										# Test not batched input
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

										# Test batched
										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Tuple								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    , )

						@slow
						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	Optional[int]								=json.loads(f.read() )

										__magic_name__	:	str								={"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__magic_name__	:	Optional[Any]								=DetaImageProcessor()
										__magic_name__	:	Any								=image_processing(images=__snake_case    , annotations=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Tuple								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	Optional[int]								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	Dict								=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Optional[int]								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	int								=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Any								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	Optional[Any]								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[str]								=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify orig_size
										__magic_name__	:	Optional[Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	Tuple								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	str								=json.loads(f.read() )

										__magic_name__	:	Optional[Any]								={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__magic_name__	:	List[str]								=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

										# encode them
										__magic_name__	:	int								=DetaImageProcessor(format="""coco_panoptic""" )
										__magic_name__	:	Tuple								=image_processing(images=__snake_case    , annotations=__snake_case    , masks_path=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Union[str, Any]								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	str								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	str								=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Dict								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	Optional[Any]								=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Dict								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	int								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[Any]								=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify masks
										__magic_name__	:	List[Any]								=82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    , __snake_case )
										# verify orig_size
										__magic_name__	:	Union[str, Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	List[Any]								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )




