
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()
lowerCamelCase_						     =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_						     =		[]
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 						__magic_name__			(   __a      :	Dict	,		__a      :	Any	,		__a      :	Optional[Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    state_dict.pop(__a						)
     UpperCamelCase__				=    val






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCamelCase__				=    key.replace("""backbone.0.body"""	,		"""backbone.conv_encoder.model"""						)
               UpperCamelCase__				=    value
          else:
               UpperCamelCase__				=    value

     return new_state_dict






def 						__magic_name__			(   __a      :	Any	,		__a      :	Union[str, Any]=False						):



     '''simple docstring'''




     UpperCamelCase__				=    """"""
     if is_panoptic:
          UpperCamelCase__				=    """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__				=    state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"						)
          UpperCamelCase__				=    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__				=    in_proj_weight[:256, :]
          UpperCamelCase__				=    in_proj_bias[:256]
          UpperCamelCase__				=    in_proj_weight[256:512, :]
          UpperCamelCase__				=    in_proj_bias[256:512]
          UpperCamelCase__				=    in_proj_weight[-256:, :]
          UpperCamelCase__				=    in_proj_bias[-256:]






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCamelCase__				=    Image.open(requests.get(__a	,		stream=__a						).raw						)

     return im






@torch.no_grad()
def 						__magic_name__			(   __a      :	int	,		__a      :	Dict						):



     '''simple docstring'''




     UpperCamelCase__				=    ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          UpperCamelCase__				=    """resnet101"""
     if "dc5" in model_name:
          UpperCamelCase__				=    True
     UpperCamelCase__				=    """panoptic""" in model_name
     if is_panoptic:
          UpperCamelCase__				=    250
     else:
          UpperCamelCase__				=    91
          UpperCamelCase__				=    """huggingface/label-files"""
          UpperCamelCase__				=    """coco-detection-id2label.json"""
          UpperCamelCase__				=    json.load(open(hf_hub_download(__a	,		__a	,		repo_type="""dataset"""						)	,		"""r"""						)						)
          UpperCamelCase__				=    {int(__a						): v for k, v in idalabel.items()}
          UpperCamelCase__				=    idalabel
          UpperCamelCase__				=    {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCamelCase__				=    """coco_panoptic""" if is_panoptic else """coco_detection"""
     UpperCamelCase__				=    ConditionalDetrImageProcessor(format=__a						)

     # prepare image
     UpperCamelCase__				=    prepare_img()
     UpperCamelCase__				=    image_processor(images=__a	,		return_tensors="""pt"""						)
     UpperCamelCase__				=    encoding["""pixel_values"""]

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

     # load original model from torch hub
     UpperCamelCase__				=    torch.hub.load("""DeppMeng/ConditionalDETR"""	,		__a	,		pretrained=__a						).eval()
     UpperCamelCase__				=    conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               UpperCamelCase__				=    """conditional_detr.""" + src
          rename_key(__a	,		__a	,		__a						)
     UpperCamelCase__				=    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__				=    """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__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCamelCase__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
               elif key.startswith("""bbox_attention"""						) or key.startswith("""mask_head"""						):
                    continue
               else:
                    UpperCamelCase__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
          else:
               if not key.startswith("""class_labels_classifier"""						) and not key.startswith("""bbox_predictor"""						):
                    UpperCamelCase__				=    state_dict.pop(__a						)
                    UpperCamelCase__				=    val
    # finally, create HuggingFace model and load state dict
     UpperCamelCase__				=    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__				=    conditional_detr(__a						)
     UpperCamelCase__				=    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__":
    lowerCamelCase_						     =		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.'''
    )
    lowerCamelCase_						     =		parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowerCamelCase_						     =					logging.get_logger(__name__)




@add_end_docstrings(__lowerCamelCase			)
class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      **SCREAMING_SNAKE_CASE_						):
       super().__init__(**SCREAMING_SNAKE_CASE_						)
       requires_backends(self	,      """vision"""						)
       requires_backends(self	,      """torch"""						)

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

       self.check_model_type(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}
       UpperCamelCase__				=    {}
       UpperCamelCase__				=    {}
       # preprocess args
       if "points_per_batch" in kwargs:
            UpperCamelCase__				=    kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
            UpperCamelCase__				=    kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
            UpperCamelCase__				=    kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
            UpperCamelCase__				=    kwargs["""crop_overlap_ratio"""]
       if "crop_n_points_downscale_factor" in kwargs:
            UpperCamelCase__				=    kwargs["""crop_n_points_downscale_factor"""]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
            UpperCamelCase__				=    kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
            UpperCamelCase__				=    kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
            UpperCamelCase__				=    kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
            UpperCamelCase__				=    kwargs["""output_bboxes_mask"""]
       return preprocess_kwargs, forward_params, postprocess_kwargs







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       return super().__call__(SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      num_workers=SCREAMING_SNAKE_CASE_	,      batch_size=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=64	,      SCREAMING_SNAKE_CASE_ = 0	,      SCREAMING_SNAKE_CASE_ = 5_12 / 15_00	,      SCREAMING_SNAKE_CASE_ = 32	,      SCREAMING_SNAKE_CASE_ = 1	,      ):
       UpperCamelCase__				=    load_image(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.image_processor.size["""longest_edge"""]
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.generate_crop_boxes(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)

       with self.device_placement():
            if self.framework == "pt":
                 UpperCamelCase__				=    self.get_inference_context()
                 with inference_context():
                      UpperCamelCase__				=    self._ensure_tensor_on_device(SCREAMING_SNAKE_CASE_	,      device=self.device						)
                      UpperCamelCase__				=    self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""						)						)
                      UpperCamelCase__				=    image_embeddings

       UpperCamelCase__				=    grid_points.shape[1]
       UpperCamelCase__				=    points_per_batch if points_per_batch is not None else n_points

       if points_per_batch <= 0:
            raise ValueError(
                """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
                """To return all points at once, set points_per_batch to None"""						)

       for i in range(0	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    grid_points[:, i : i + points_per_batch, :, :]
            UpperCamelCase__				=    input_labels[:, i : i + points_per_batch]
            UpperCamelCase__				=    i == n_points - points_per_batch
            yield {
                "input_points": batched_points,
                "input_labels": labels,
                "input_boxes": crop_boxes,
                "is_last": is_last,
                **model_inputs,
            }







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=0.88	,      SCREAMING_SNAKE_CASE_=0.95	,      SCREAMING_SNAKE_CASE_=0	,      SCREAMING_SNAKE_CASE_=1	,      ):
       UpperCamelCase__				=    model_inputs.pop("""input_boxes"""						)
       UpperCamelCase__				=    model_inputs.pop("""is_last"""						)
       UpperCamelCase__				=    model_inputs.pop("""original_sizes"""						).tolist()
       UpperCamelCase__				=    model_inputs.pop("""reshaped_input_sizes"""						).tolist()

       UpperCamelCase__				=    self.model(**SCREAMING_SNAKE_CASE_						)

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       UpperCamelCase__				=    model_outputs["""pred_masks"""]
       UpperCamelCase__				=    self.image_processor.post_process_masks(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      binarize=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model_outputs["""iou_scores"""]
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.filter_masks(
           masks[0]	,      iou_scores[0]	,      original_sizes[0]	,      input_boxes[0]	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      )
       return {
           "masks": masks,
           "is_last": is_last,
           "boxes": boxes,
           "iou_scores": iou_scores,
       }







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=0.7	,      ):
       UpperCamelCase__				=    []
       UpperCamelCase__				=    []
       UpperCamelCase__				=    []
       for model_output in model_outputs:
            all_scores.append(model_output.pop("""iou_scores"""						)						)
            all_masks.extend(model_output.pop("""masks"""						)						)
            all_boxes.append(model_output.pop("""boxes"""						)						)

       UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.post_process_for_mask_generation(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    defaultdict(SCREAMING_SNAKE_CASE_						)
       for output in model_outputs:
            for k, v in output.items():
                 extra[k].append(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    {}
       if output_rle_mask:
            UpperCamelCase__				=    rle_mask

       if output_bboxes_mask:
            UpperCamelCase__				=    bounding_boxes

       return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





