


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()
SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    # initialize config
    if "resnet-50" in model_name:
        _lowercase			:				Union[str, Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-50'					)
    elif "resnet-101" in model_name:
        _lowercase			:				Optional[Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-101'					)
    else:
        raise ValueError('Model name should include either resnet50 or resnet101'					)

    _lowercase			:				Tuple 						=						DetrConfig(use_timm_backbone=lowerCamelCase_				,					backbone_config=lowerCamelCase_					)

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

    return config, is_panoptic



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    _lowercase			:				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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				str 						=						state_dict.pop(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						val



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    _lowercase			:				Any 						=						''
    if is_panoptic:
        _lowercase			:				Optional[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)
        _lowercase			:				int 						=						state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
        _lowercase			:				Tuple 						=						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
        _lowercase			:				List[str] 						=						in_proj_weight[:256, :]
        _lowercase			:				Tuple 						=						in_proj_bias[:256]
        _lowercase			:				List[Any] 						=						in_proj_weight[256:512, :]
        _lowercase			:				Any 						=						in_proj_bias[256:512]
        _lowercase			:				int 						=						in_proj_weight[-256:, :]
        _lowercase			:				Optional[int] 						=						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
        _lowercase			:				str 						=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
        _lowercase			:				Optional[int] 						=						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
        _lowercase			:				Union[str, Any] 						=						in_proj_weight[:256, :]
        _lowercase			:				Dict 						=						in_proj_bias[:256]
        _lowercase			:				Tuple 						=						in_proj_weight[256:512, :]
        _lowercase			:				Dict 						=						in_proj_bias[256:512]
        _lowercase			:				str 						=						in_proj_weight[-256:, :]
        _lowercase			:				Optional[int] 						=						in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _lowercase			:				Tuple 						=						state_dict.pop(
            F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
        _lowercase			:				Dict 						=						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
        _lowercase			:				List[str] 						=						in_proj_weight_cross_attn[:256, :]
        _lowercase			:				Tuple 						=						in_proj_bias_cross_attn[:256]
        _lowercase			:				str 						=						in_proj_weight_cross_attn[256:512, :]
        _lowercase			:				Union[str, Any] 						=						in_proj_bias_cross_attn[256:512]
        _lowercase			:				List[Any] 						=						in_proj_weight_cross_attn[-256:, :]
        _lowercase			:				Dict 						=						in_proj_bias_cross_attn[-256:]



def 							UpperCamelCase_( )       ->		List[Any]:
    _lowercase			:				Dict 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				str 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)

    return im



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=False					)       ->		List[Any]:
    _lowercase						,							_lowercase			:				int 						=						get_detr_config(lowerCamelCase_					)

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

    # finally, create HuggingFace model and load state dict
    _lowercase			:				Optional[Any] 						=						DetrForSegmentation(lowerCamelCase_					) if is_panoptic else DetrForObjectDetection(lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    # verify our conversion on an image
    _lowercase			:				str 						=						'coco_panoptic' if is_panoptic else 'coco_detection'
    _lowercase			:				Optional[int] 						=						DetrImageProcessor(format=lowerCamelCase_					)

    _lowercase			:				str 						=						processor(images=prepare_img()				,					return_tensors='pt'					)
    _lowercase			:				Tuple 						=						encoding['pixel_values']

    _lowercase			:				int 						=						detr(lowerCamelCase_					)
    _lowercase			:				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__":
 SCREAMING_SNAKE_CASE			: List[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.")
 SCREAMING_SNAKE_CASE			: Tuple				   =					parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




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

_snake_case      :							List[Any]     	=    logging.get_logger(__name__)





@add_end_docstrings(__a		)
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Tuple		,    **lowerCAmelCase__							:Dict     )  ->int:
										super().__init__(**lowerCAmelCase__     )
										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(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    **lowerCAmelCase__							:Union[str, Any]     )  ->List[Any]:
										lowercase         =      {}
										lowercase         =      {}
										lowercase         =      {}
										# preprocess args
										if "points_per_batch" in kwargs:
														lowercase         =      kwargs["points_per_batch"]
										if "points_per_crop" in kwargs:
														lowercase         =      kwargs["points_per_crop"]
										if "crops_n_layers" in kwargs:
														lowercase         =      kwargs["crops_n_layers"]
										if "crop_overlap_ratio" in kwargs:
														lowercase         =      kwargs["crop_overlap_ratio"]
										if "crop_n_points_downscale_factor" in kwargs:
														lowercase         =      kwargs["crop_n_points_downscale_factor"]
										# postprocess args
										if "pred_iou_thresh" in kwargs:
														lowercase         =      kwargs["pred_iou_thresh"]
										if "stability_score_offset" in kwargs:
														lowercase         =      kwargs["stability_score_offset"]
										if "mask_threshold" in kwargs:
														lowercase         =      kwargs["mask_threshold"]
										if "stability_score_thresh" in kwargs:
														lowercase         =      kwargs["stability_score_thresh"]
										if "crops_nms_thresh" in kwargs:
														lowercase         =      kwargs["crops_nms_thresh"]
										if "output_rle_mask" in kwargs:
														lowercase         =      kwargs["output_rle_mask"]
										if "output_bboxes_mask" in kwargs:
														lowercase         =      kwargs["output_bboxes_mask"]
										return preprocess_kwargs, forward_params, postprocess_kwargs





						def __call__(			self							:List[str]		,    lowerCAmelCase__							:int		,    *lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Any=None		,    **lowerCAmelCase__							:List[str]     )  ->Optional[int]:
										return super().__call__(lowerCAmelCase__		,    *lowerCAmelCase__		,    num_workers=lowerCAmelCase__		,    batch_size=lowerCAmelCase__		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int = 0		,    lowerCAmelCase__							:float = 512 / 1500		,    lowerCAmelCase__							:Optional[int] = 32		,    lowerCAmelCase__							:Optional[int] = 1		,    )  ->Any:
										lowercase         =      load_image(lowerCAmelCase__     )
										lowercase         =      self.image_processor.size["longest_edge"]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.generate_crop_boxes(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      self.image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     )

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

										lowercase         =      grid_points.shape[1]
										lowercase         =      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		,    lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      grid_points[:, i : i + points_per_batch, :, :]
														lowercase         =      input_labels[:, i : i + points_per_batch]
														lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Dict=0.88		,    lowerCAmelCase__							:Dict=0.95		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:int=1		,    )  ->str:
										lowercase         =      model_inputs.pop("input_boxes"     )
										lowercase         =      model_inputs.pop("is_last"     )
										lowercase         =      model_inputs.pop("original_sizes"     ).tolist()
										lowercase         =      model_inputs.pop("reshaped_input_sizes"     ).tolist()

										lowercase         =      self.model(**lowerCAmelCase__     )

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





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:str=False		,    lowerCAmelCase__							:int=0.7		,    )  ->List[Any]:
										lowercase         =      []
										lowercase         =      []
										lowercase         =      []
										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"     )     )

										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.post_process_for_mask_generation(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

										lowercase         =      {}
										if output_rle_mask:
														lowercase         =      rle_mask

										if output_bboxes_mask:
														lowercase         =      bounding_boxes

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