





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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]	)		->							Dict:
 '''simple docstring'''


 if "resnet-50" in model_name:
  SCREAMING_SNAKE_CASE								=			ResNetConfig.from_pretrained('microsoft/resnet-50'	)
 elif "resnet-101" in model_name:
  SCREAMING_SNAKE_CASE								=			ResNetConfig.from_pretrained('microsoft/resnet-101'	)
 else:
  raise ValueError('Model name should include either resnet50 or resnet101'	)

 SCREAMING_SNAKE_CASE								=			DetrConfig(use_timm_backbone=__lowerCAmelCase		,							backbone_config=__lowerCAmelCase	)

 # set label attributes
 SCREAMING_SNAKE_CASE								=			'panoptic' in model_name
 if is_panoptic:
  SCREAMING_SNAKE_CASE								=			2_50
 else:
  SCREAMING_SNAKE_CASE								=			91
  SCREAMING_SNAKE_CASE								=			'huggingface/label-files'
  SCREAMING_SNAKE_CASE								=			'coco-detection-id2label.json'
  SCREAMING_SNAKE_CASE								=			json.load(open(hf_hub_download(__lowerCAmelCase		,							__lowerCAmelCase		,							repo_type='dataset'	)		,							'r'	)	)
  SCREAMING_SNAKE_CASE								=			{int(__lowerCAmelCase	): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE								=			idalabel
  SCREAMING_SNAKE_CASE								=			{v: k for k, v in idalabel.items()}

 return config, is_panoptic


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple	)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]

 # 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    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Any	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			state_dict.pop(__lowerCAmelCase	)
 SCREAMING_SNAKE_CASE								=			val


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  str=False	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			''
 if is_panoptic:
  SCREAMING_SNAKE_CASE								=			'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)
  SCREAMING_SNAKE_CASE								=			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
  SCREAMING_SNAKE_CASE								=			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
  SCREAMING_SNAKE_CASE								=			in_proj_weight[:2_56, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[:2_56]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[2_56:5_12, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[2_56:5_12]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[-2_56:, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[-2_56:]
 # 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
  SCREAMING_SNAKE_CASE								=			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
  SCREAMING_SNAKE_CASE								=			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
  SCREAMING_SNAKE_CASE								=			in_proj_weight[:2_56, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[:2_56]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[2_56:5_12, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[2_56:5_12]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[-2_56:, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[-2_56:]
  # read in weights + bias of input projection layer of cross-attention
  SCREAMING_SNAKE_CASE								=			state_dict.pop(
      f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
  SCREAMING_SNAKE_CASE								=			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
  SCREAMING_SNAKE_CASE								=			in_proj_weight_cross_attn[:2_56, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias_cross_attn[:2_56]
  SCREAMING_SNAKE_CASE								=			in_proj_weight_cross_attn[2_56:5_12, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias_cross_attn[2_56:5_12]
  SCREAMING_SNAKE_CASE								=			in_proj_weight_cross_attn[-2_56:, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias_cross_attn[-2_56:]


def        __lowerCAmelCase    (       )		->							str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE								=			Image.open(requests.get(__lowerCAmelCase		,							stream=__lowerCAmelCase	).raw	)

 return im


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  Dict=None		,							_UpperCamelCase		:  Any=False	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			get_detr_config(__lowerCAmelCase	)

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

  # finally, create HuggingFace model and load state dict
 SCREAMING_SNAKE_CASE								=			DetrForSegmentation(__lowerCAmelCase	) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase	)
 model.load_state_dict(__lowerCAmelCase	)
 model.eval()

 # verify our conversion on an image
 SCREAMING_SNAKE_CASE								=			'coco_panoptic' if is_panoptic else 'coco_detection'
 SCREAMING_SNAKE_CASE								=			DetrImageProcessor(format=__lowerCAmelCase	)

 SCREAMING_SNAKE_CASE								=			processor(images=prepare_img()		,							return_tensors='pt'	)
 SCREAMING_SNAKE_CASE								=			encoding['pixel_values']

 SCREAMING_SNAKE_CASE								=			detr(__lowerCAmelCase	)
 SCREAMING_SNAKE_CASE								=			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__":
      a_    :						Any    		=      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.")
      a_    :						Tuple    		=      parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




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, ChunkPipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      import torch

      from transformers.modeling_outputs import BaseModelOutput

      from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

a_    :						Dict    		=      logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE   )
class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Any							,    **snake_case__ :      Optional[int]    ):

     """simple docstring"""






     super().__init__(**snake_case__    )

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

     requires_backends(self							,    'vision'    )
     self.check_model_type(snake_case__    )

    def __call__(			self :      List[Any]							,    snake_case__ :      Union[str, "Image.Image", List[Dict[str, Any]]]							,    snake_case__ :      Union[str, List[str]] = None							,    **snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






     if "text_queries" in kwargs:
      SCREAMING_SNAKE_CASE								=			kwargs.pop('text_queries'    )

     if isinstance(snake_case__							,    (str, Image.Image)    ):
      SCREAMING_SNAKE_CASE								=			{'image': image, 'candidate_labels': candidate_labels}
     else:
      SCREAMING_SNAKE_CASE								=			image
     SCREAMING_SNAKE_CASE								=			super().__call__(snake_case__							,    **snake_case__    )
     return results

    def UpperCamelCase			(			self :      Union[str, Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			{}
     if "threshold" in kwargs:
      SCREAMING_SNAKE_CASE								=			kwargs['threshold']
     if "top_k" in kwargs:
      SCREAMING_SNAKE_CASE								=			kwargs['top_k']
     return {}, {}, postprocess_params

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			load_image(inputs['image']    )
     SCREAMING_SNAKE_CASE								=			inputs['candidate_labels']
     if isinstance(snake_case__							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			candidate_labels.split(','    )

     SCREAMING_SNAKE_CASE								=			torch.tensor([[image.height, image.width]]							,    dtype=torch.intaa    )
     for i, candidate_label in enumerate(snake_case__    ):
      SCREAMING_SNAKE_CASE								=			self.tokenizer(snake_case__							,    return_tensors=self.framework    )
      SCREAMING_SNAKE_CASE								=			self.image_processor(snake_case__							,    return_tensors=self.framework    )
      yield {
          "is_last": i == len(snake_case__    ) - 1,
          "target_size": target_size,
          "candidate_label": candidate_label,
          **text_inputs,
          **image_features,
      }

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			model_inputs.pop('target_size'    )
     SCREAMING_SNAKE_CASE								=			model_inputs.pop('candidate_label'    )
     SCREAMING_SNAKE_CASE								=			model_inputs.pop('is_last'    )

     SCREAMING_SNAKE_CASE								=			self.model(**snake_case__    )

     SCREAMING_SNAKE_CASE								=			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
     return model_outputs

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      str=0.1							,    snake_case__ :      Union[str, Any]=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]
     for model_output in model_outputs:
      SCREAMING_SNAKE_CASE								=			model_output['candidate_label']
      SCREAMING_SNAKE_CASE								=			BaseModelOutput(snake_case__    )
      SCREAMING_SNAKE_CASE								=			self.image_processor.post_process_object_detection(
          outputs=snake_case__							,    threshold=snake_case__							,    target_sizes=model_output['target_size']    )[0]

      for index in outputs["scores"].nonzero():
       SCREAMING_SNAKE_CASE								=			outputs['scores'][index].item()
       SCREAMING_SNAKE_CASE								=			self._get_bounding_box(outputs['boxes'][index][0]    )

       SCREAMING_SNAKE_CASE								=			{'score': score, 'label': label, 'box': box}
       results.append(snake_case__    )

     SCREAMING_SNAKE_CASE								=			sorted(snake_case__							,    key=lambda snake_case__    : x["score"]							,    reverse=snake_case__    )
     if top_k:
      SCREAMING_SNAKE_CASE								=			results[:top_k]

     return results




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      "torch.Tensor"    ):

     """simple docstring"""






     if self.framework != "pt":
      raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'    )
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			box.int().tolist()
     SCREAMING_SNAKE_CASE								=			{
         'xmin': xmin,
         'ymin': ymin,
         'xmax': xmax,
         'ymax': ymax,
     }
     return bbox

