




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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase     							=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase     							=						[]
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}.cross_attn.out_proj.weight',
		        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
		    )
		)
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.cross_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'))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
		)

		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
		rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)




def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							List[str]					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						state_dict.pop(_a  )
					UpperCAmelCase_   :				Union[str, Any]						=						val




def 					lowercase__				(					__snake_case   :							List[str]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															UpperCAmelCase_   :				List[str]						=						key.replace('backbone.0.body'					,	'backbone.conv_encoder.model'  )
															UpperCAmelCase_   :				Any						=						value
										else:
															UpperCAmelCase_   :				List[str]						=						value

					return new_state_dict




def 					lowercase__				(					__snake_case   :							int					,	__snake_case   :							Union[str, Any]=False  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						""""""
					if is_panoptic:
										UpperCAmelCase_   :				int						=						"""conditional_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_   :				Dict						=						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_   :				Dict						=						in_proj_weight[:256, :]
										UpperCAmelCase_   :				Tuple						=						in_proj_bias[:256]
										UpperCAmelCase_   :				Union[str, Any]						=						in_proj_weight[256:512, :]
										UpperCAmelCase_   :				List[str]						=						in_proj_bias[256:512]
										UpperCAmelCase_   :				List[str]						=						in_proj_weight[-256:, :]
										UpperCAmelCase_   :				Union[str, Any]						=						in_proj_bias[-256:]




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					UpperCAmelCase_   :				List[Any]						=						Image.open(requests.get(_a					,	stream=_a  ).raw  )

					return im




@torch.no_grad()
def 					lowercase__				(					__snake_case   :							Dict					,	__snake_case   :							Optional[int]  ):


					'''simple docstring'''


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

					# load image processor
					UpperCAmelCase_   :				Optional[Any]						=						"""coco_panoptic""" if is_panoptic else """coco_detection"""
					UpperCAmelCase_   :				Tuple						=						ConditionalDetrImageProcessor(format=_a  )

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

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

					# load original model from torch hub
					UpperCAmelCase_   :				Any						=						torch.hub.load('DeppMeng/ConditionalDETR'					,	_a					,	pretrained=_a  ).eval()
					UpperCAmelCase_   :				List[Any]						=						conditional_detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															UpperCAmelCase_   :				Any						=						"""conditional_detr.""" + src
										rename_key(_a					,	_a					,	_a  )
					UpperCAmelCase_   :				int						=						rename_backbone_keys(_a  )
					# query, key and value matrices need special treatment
					read_in_q_k_v(_a					,	is_panoptic=_a  )
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					UpperCAmelCase_   :				str						=						"""conditional_detr.model.""" if is_panoptic else """model."""
					for key in state_dict.copy().keys():
										if is_panoptic:
															if (
															    key.startswith('conditional_detr'  )
															    and not key.startswith('class_labels_classifier'  )
															    and not key.startswith('bbox_predictor'  )
															):
																				UpperCAmelCase_   :				Tuple						=						state_dict.pop(_a  )
																				UpperCAmelCase_   :				Tuple						=						val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				UpperCAmelCase_   :				List[Any]						=						state_dict.pop(_a  )
																				UpperCAmelCase_   :				List[Any]						=						val
															elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
																				continue
															else:
																				UpperCAmelCase_   :				Any						=						state_dict.pop(_a  )
																				UpperCAmelCase_   :				List[Any]						=						val
										else:
															if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
																				UpperCAmelCase_   :				List[Any]						=						state_dict.pop(_a  )
																				UpperCAmelCase_   :				Any						=						val
    # finally, create HuggingFace model and load state dict
					UpperCAmelCase_   :				int						=						ConditionalDetrForSegmentation(_a  ) if is_panoptic else ConditionalDetrForObjectDetection(_a  )
					model.load_state_dict(_a  )
					model.eval()
					model.push_to_hub(repo_id=_a					,	organization='DepuMeng'					,	commit_message='Add model'  )
					# verify our conversion
					UpperCAmelCase_   :				List[Any]						=						conditional_detr(_a  )
					UpperCAmelCase_   :				Any						=						model(_a  )
					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(_a  ).mkdir(exist_ok=_a  )
					model.save_pretrained(_a  )
					image_processor.save_pretrained(_a  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						argparse.ArgumentParser()

		parser.add_argument(
		    '--model_name',
		    default='conditional_detr_resnet50',
		    type=str,
		    help='Name of the CONDITIONAL_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.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)














from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from ..image_utils import load_image


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__UpperCAmelCase     							=						logging.get_logger(__name__)


__UpperCAmelCase     							=						Dict[str, Any]
__UpperCAmelCase     							=						List[Prediction]



@add_end_docstrings(_snake_case      )
class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Optional[int]:
							super().__init__(*_UpperCamelCase    ,		**_UpperCamelCase       )

							if self.framework == "tf":
												raise ValueError(f"The {self.__class__} is only available in PyTorch."       )

							requires_backends(self    ,		'vision'       )
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()       )       )



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						{}
							if "threshold" in kwargs:
												UpperCAmelCase_   :				Tuple						=						kwargs['threshold']
							return {}, {}, postprocess_kwargs



		def __call__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Union[Predictions, List[Prediction]]:
							return super().__call__(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Any						=						load_image(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						torch.IntTensor([[image.height, image.width]]       )
							UpperCAmelCase_   :				str						=						self.image_processor(images=[image]    ,		return_tensors='pt'       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						self.tokenizer(text=inputs['words']    ,		boxes=inputs['boxes']    ,		return_tensors='pt'       )
							UpperCAmelCase_   :				Any						=						target_size
							return inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						model_inputs.pop('target_size'       )
							UpperCAmelCase_   :				Optional[Any]						=						self.model(**_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						outputs.__class__({'target_size': target_size, **outputs}       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						model_inputs['bbox']
							return model_outputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0.9       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						model_outputs['target_size']
							if self.tokenizer is not None:
												# This is a LayoutLMForTokenClassification variant.
												# The OCR got the boxes and the model classified the words.
												UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						target_size[0].tolist()

												def unnormalize(_UpperCamelCase       ):
																	return self._get_bounding_box(
																	    torch.Tensor(
																	        [
																	            (width * bbox[0] / 1_0_0_0),
																	            (height * bbox[1] / 1_0_0_0),
																	            (width * bbox[2] / 1_0_0_0),
																	            (height * bbox[3] / 1_0_0_0),
																	        ]       )       )

												UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						model_outputs['logits'].squeeze(0       ).softmax(dim=-1       ).max(dim=-1       )
												UpperCAmelCase_   :				Union[str, Any]						=						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
												UpperCAmelCase_   :				Tuple						=						[unnormalize(_UpperCamelCase       ) for bbox in model_outputs['bbox'].squeeze(0       )]
												UpperCAmelCase_   :				List[str]						=						['score', 'label', 'box']
												UpperCAmelCase_   :				Any						=						[dict(zip(_UpperCamelCase    ,		_UpperCamelCase       )       ) for vals in zip(scores.tolist()    ,		_UpperCamelCase    ,		_UpperCamelCase       ) if vals[0] > threshold]
							else:
												# This is a regular ForObjectDetectionModel
												UpperCAmelCase_   :				Union[str, Any]						=						self.image_processor.post_process_object_detection(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
												UpperCAmelCase_   :				str						=						raw_annotations[0]
												UpperCAmelCase_   :				str						=						raw_annotation['scores']
												UpperCAmelCase_   :				Tuple						=						raw_annotation['labels']
												UpperCAmelCase_   :				List[Any]						=						raw_annotation['boxes']

												UpperCAmelCase_   :				Union[str, Any]						=						scores.tolist()
												UpperCAmelCase_   :				int						=						[self.model.config.idalabel[label.item()] for label in labels]
												UpperCAmelCase_   :				Any						=						[self._get_bounding_box(_UpperCamelCase       ) for box in boxes]

												# {"scores": [...], ...} --> [{"score":x, ...}, ...]
												UpperCAmelCase_   :				int						=						['score', 'label', 'box']
												UpperCAmelCase_   :				Dict						=						[
												    dict(zip(_UpperCamelCase    ,		_UpperCamelCase       )       )
												    for vals in zip(raw_annotation['scores']    ,		raw_annotation['labels']    ,		raw_annotation['boxes']       )
												]

							return annotation



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict[str, int]:
							if self.framework != "pt":
												raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'       )
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						box.int().tolist()
							UpperCAmelCase_   :				str						=						{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox






