

'''simple docstring'''
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCamelCase      					=     {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCamelCase      					=     {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCamelCase      					=     {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCamelCase      					=     {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCamelCase      					=     {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCamelCase      					=     {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}


def  a__       (    _SCREAMING_SNAKE_CASE   :       Union[str, Any]				)     ->					List[Any]:


	"""simple docstring"""





	if isinstance(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				):
		return v
	if v.lower() in ("yes", "true", "t", "y", "1"):
		return True
	elif v.lower() in ("no", "false", "f", "n", "0"):
		return False
	else:
		raise argparse.ArgumentTypeError("boolean value expected"				)


def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]=False				)     ->					List[Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      Tuple						=  checkpoint[F'''{old_prefix}.in_layers.0.weight''']
	UpperCAmelCase_     :      List[Any]						=  checkpoint[F'''{old_prefix}.in_layers.0.bias''']
	UpperCAmelCase_     :      Optional[int]						=  checkpoint[F'''{old_prefix}.in_layers.2.weight''']
	UpperCAmelCase_     :      Any						=  checkpoint[F'''{old_prefix}.in_layers.2.bias''']
	UpperCAmelCase_     :      str						=  checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
	UpperCAmelCase_     :      Dict						=  checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
	UpperCAmelCase_     :      List[Any]						=  checkpoint[F'''{old_prefix}.out_layers.0.weight''']
	UpperCAmelCase_     :      Dict						=  checkpoint[F'''{old_prefix}.out_layers.0.bias''']
	UpperCAmelCase_     :      List[Any]						=  checkpoint[F'''{old_prefix}.out_layers.3.weight''']
	UpperCAmelCase_     :      Tuple						=  checkpoint[F'''{old_prefix}.out_layers.3.bias''']

	if has_skip:
		UpperCAmelCase_     :      Any						=  checkpoint[F'''{old_prefix}.skip_connection.weight''']
		UpperCAmelCase_     :      Tuple						=  checkpoint[F'''{old_prefix}.skip_connection.bias''']

	return new_checkpoint


def  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Any	,				_SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       Tuple=None				)     ->					str:


	"""simple docstring"""





	UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3	,				dim=0				)
	UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      int						=  checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3	,				dim=0				)

	UpperCAmelCase_     :      Tuple						=  checkpoint[F'''{old_prefix}.norm.weight''']
	UpperCAmelCase_     :      Optional[Any]						=  checkpoint[F'''{old_prefix}.norm.bias''']

	UpperCAmelCase_     :      Union[str, Any]						=  weight_q.squeeze(-1				).squeeze(-1				)
	UpperCAmelCase_     :      int						=  bias_q.squeeze(-1				).squeeze(-1				)
	UpperCAmelCase_     :      str						=  weight_k.squeeze(-1				).squeeze(-1				)
	UpperCAmelCase_     :      Any						=  bias_k.squeeze(-1				).squeeze(-1				)
	UpperCAmelCase_     :      Tuple						=  weight_v.squeeze(-1				).squeeze(-1				)
	UpperCAmelCase_     :      int						=  bias_v.squeeze(-1				).squeeze(-1				)

	UpperCAmelCase_     :      Tuple						=  (
	    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
	)
	UpperCAmelCase_     :      Optional[int]						=  checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

	return new_checkpoint


def  a__       (    _SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]				)     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location="cpu"				)
	UpperCAmelCase_     :      List[str]						=  {}

	UpperCAmelCase_     :      str						=  checkpoint["time_embed.0.weight"]
	UpperCAmelCase_     :      Any						=  checkpoint["time_embed.0.bias"]
	UpperCAmelCase_     :      int						=  checkpoint["time_embed.2.weight"]
	UpperCAmelCase_     :      Union[str, Any]						=  checkpoint["time_embed.2.bias"]

	if unet_config["num_class_embeds"] is not None:
		UpperCAmelCase_     :      Optional[Any]						=  checkpoint["label_emb.weight"]

	UpperCAmelCase_     :      Tuple						=  checkpoint["input_blocks.0.0.weight"]
	UpperCAmelCase_     :      List[str]						=  checkpoint["input_blocks.0.0.bias"]

	UpperCAmelCase_     :      Union[str, Any]						=  unet_config["down_block_types"]
	UpperCAmelCase_     :      Tuple						=  unet_config["layers_per_block"]
	UpperCAmelCase_     :      Any						=  unet_config["attention_head_dim"]
	UpperCAmelCase_     :      Optional[Any]						=  unet_config["block_out_channels"]
	UpperCAmelCase_     :      Optional[Any]						=  1
	UpperCAmelCase_     :      Any						=  channels_list[0]

	for i, layer_type in enumerate(_SCREAMING_SNAKE_CASE				):
		UpperCAmelCase_     :      Dict						=  channels_list[i]
		UpperCAmelCase_     :      List[Any]						=  current_channels != prev_channels
		if layer_type == "ResnetDownsampleBlock2D":
			for j in range(_SCREAMING_SNAKE_CASE				):
				UpperCAmelCase_     :      Tuple						=  F'''down_blocks.{i}.resnets.{j}'''
				UpperCAmelCase_     :      Union[str, Any]						=  F'''input_blocks.{current_layer}.0'''
				UpperCAmelCase_     :      Optional[int]						=  True if j == 0 and downsample_block_has_skip else False
				UpperCAmelCase_     :      Optional[int]						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				has_skip=_SCREAMING_SNAKE_CASE				)
				current_layer += 1

		elif layer_type == "AttnDownBlock2D":
			for j in range(_SCREAMING_SNAKE_CASE				):
				UpperCAmelCase_     :      List[Any]						=  F'''down_blocks.{i}.resnets.{j}'''
				UpperCAmelCase_     :      List[str]						=  F'''input_blocks.{current_layer}.0'''
				UpperCAmelCase_     :      Optional[Any]						=  True if j == 0 and downsample_block_has_skip else False
				UpperCAmelCase_     :      Dict						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				has_skip=_SCREAMING_SNAKE_CASE				)
				UpperCAmelCase_     :      Tuple						=  F'''down_blocks.{i}.attentions.{j}'''
				UpperCAmelCase_     :      Tuple						=  F'''input_blocks.{current_layer}.1'''
				UpperCAmelCase_     :      Union[str, Any]						=  convert_attention(
				    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
				current_layer += 1

		if i != len(_SCREAMING_SNAKE_CASE				) - 1:
			UpperCAmelCase_     :      str						=  F'''down_blocks.{i}.downsamplers.0'''
			UpperCAmelCase_     :      int						=  F'''input_blocks.{current_layer}.0'''
			UpperCAmelCase_     :      Any						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
			current_layer += 1

		UpperCAmelCase_     :      List[str]						=  current_channels

	# hardcoded the mid-block for now
	UpperCAmelCase_     :      Tuple						=  "mid_block.resnets.0"
	UpperCAmelCase_     :      Dict						=  "middle_block.0"
	UpperCAmelCase_     :      Union[str, Any]						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Optional[int]						=  "mid_block.attentions.0"
	UpperCAmelCase_     :      int						=  "middle_block.1"
	UpperCAmelCase_     :      Union[str, Any]						=  convert_attention(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      str						=  "mid_block.resnets.1"
	UpperCAmelCase_     :      Dict						=  "middle_block.2"
	UpperCAmelCase_     :      List[str]						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      int						=  0
	UpperCAmelCase_     :      Tuple						=  unet_config["up_block_types"]

	for i, layer_type in enumerate(_SCREAMING_SNAKE_CASE				):
		if layer_type == "ResnetUpsampleBlock2D":
			for j in range(layers_per_block + 1				):
				UpperCAmelCase_     :      Union[str, Any]						=  F'''up_blocks.{i}.resnets.{j}'''
				UpperCAmelCase_     :      Dict						=  F'''output_blocks.{current_layer}.0'''
				UpperCAmelCase_     :      Optional[Any]						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				has_skip=_SCREAMING_SNAKE_CASE				)
				current_layer += 1

			if i != len(_SCREAMING_SNAKE_CASE				) - 1:
				UpperCAmelCase_     :      int						=  F'''up_blocks.{i}.upsamplers.0'''
				UpperCAmelCase_     :      int						=  F'''output_blocks.{current_layer-1}.1'''
				UpperCAmelCase_     :      Dict						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
		elif layer_type == "AttnUpBlock2D":
			for j in range(layers_per_block + 1				):
				UpperCAmelCase_     :      Optional[Any]						=  F'''up_blocks.{i}.resnets.{j}'''
				UpperCAmelCase_     :      List[Any]						=  F'''output_blocks.{current_layer}.0'''
				UpperCAmelCase_     :      int						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				has_skip=_SCREAMING_SNAKE_CASE				)
				UpperCAmelCase_     :      Tuple						=  F'''up_blocks.{i}.attentions.{j}'''
				UpperCAmelCase_     :      Dict						=  F'''output_blocks.{current_layer}.1'''
				UpperCAmelCase_     :      int						=  convert_attention(
				    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
				current_layer += 1

			if i != len(_SCREAMING_SNAKE_CASE				) - 1:
				UpperCAmelCase_     :      List[str]						=  F'''up_blocks.{i}.upsamplers.0'''
				UpperCAmelCase_     :      Optional[Any]						=  F'''output_blocks.{current_layer-1}.2'''
				UpperCAmelCase_     :      str						=  convert_resnet(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Optional[Any]						=  checkpoint["out.0.weight"]
	UpperCAmelCase_     :      Any						=  checkpoint["out.0.bias"]
	UpperCAmelCase_     :      Optional[int]						=  checkpoint["out.2.weight"]
	UpperCAmelCase_     :      Optional[Any]						=  checkpoint["out.2.bias"]

	return new_checkpoint


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

					parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
					parser.add_argument(
					    """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
					)
					parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

					_lowerCamelCase      					=     parser.parse_args()
					_lowerCamelCase      					=     strabool(args.class_cond)

					_lowerCamelCase      					=     os.path.basename(args.unet_path)
					print(f"""Checkpoint: {ckpt_name}""")

					# Get U-Net config
					if "imagenet64" in ckpt_name:
										_lowerCamelCase      					=     IMAGENET_64_UNET_CONFIG
					elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
										_lowerCamelCase      					=     LSUN_256_UNET_CONFIG
					elif "test" in ckpt_name:
										_lowerCamelCase      					=     TEST_UNET_CONFIG
					else:
										raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

					if not args.class_cond:
										_lowerCamelCase      					=     None

					_lowerCamelCase      					=     con_pt_to_diffuser(args.unet_path, unet_config)

					_lowerCamelCase      					=     UNetaDModel(**unet_config)
					image_unet.load_state_dict(converted_unet_ckpt)

					# Get scheduler config
					if "cd" in ckpt_name or "test" in ckpt_name:
										_lowerCamelCase      					=     CD_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
										_lowerCamelCase      					=     CT_IMAGENET_64_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
										_lowerCamelCase      					=     CT_LSUN_256_SCHEDULER_CONFIG
					else:
										raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

					_lowerCamelCase      					=     CMStochasticIterativeScheduler(**scheduler_config)

					_lowerCamelCase      					=     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
					consistency_model.save_pretrained(args.dump_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

UpperCAmelCase__      			=						logging.get_logger(__name__)




@add_end_docstrings(__UpperCamelCase      )
class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					int ,					**__UpperCAmelCase:					Optional[Any]   )    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )
											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(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int] ,					**__UpperCAmelCase:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Any      =					{}
											__a					:						Union[str, Any]      =					{}
											__a					:						List[str]      =					{}
											# preprocess args
											if "points_per_batch" in kwargs:
																__a					:						Dict      =					kwargs["points_per_batch"]
											if "points_per_crop" in kwargs:
																__a					:						Tuple      =					kwargs["points_per_crop"]
											if "crops_n_layers" in kwargs:
																__a					:						Dict      =					kwargs["crops_n_layers"]
											if "crop_overlap_ratio" in kwargs:
																__a					:						int      =					kwargs["crop_overlap_ratio"]
											if "crop_n_points_downscale_factor" in kwargs:
																__a					:						Optional[int]      =					kwargs["crop_n_points_downscale_factor"]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
																__a					:						int      =					kwargs["pred_iou_thresh"]
											if "stability_score_offset" in kwargs:
																__a					:						Any      =					kwargs["stability_score_offset"]
											if "mask_threshold" in kwargs:
																__a					:						int      =					kwargs["mask_threshold"]
											if "stability_score_thresh" in kwargs:
																__a					:						Any      =					kwargs["stability_score_thresh"]
											if "crops_nms_thresh" in kwargs:
																__a					:						int      =					kwargs["crops_nms_thresh"]
											if "output_rle_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_rle_mask"]
											if "output_bboxes_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_bboxes_mask"]
											return preprocess_kwargs, forward_params, postprocess_kwargs



						def __call__(self:					Union[str, Any] ,					__UpperCAmelCase:					str ,					*__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					str=None ,					__UpperCAmelCase:					Union[str, Any]=None ,					**__UpperCAmelCase:					Optional[int]   )    ->     Tuple:




											'''simple docstring'''



											return super().__call__(__UpperCAmelCase ,					*__UpperCAmelCase ,					num_workers=__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int=64 ,					__UpperCAmelCase:					int = 0 ,					__UpperCAmelCase:					float = 512 / 1500 ,					__UpperCAmelCase:					Optional[int] = 32 ,					__UpperCAmelCase:					Optional[int] = 1 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					load_image(__UpperCAmelCase   )
											__a					:						List[str]      =					self.image_processor.size["longest_edge"]
											__a      ,			__a      ,			__a      ,			__a					:						int      =					self.image_processor.generate_crop_boxes(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					self.image_processor(images=__UpperCAmelCase ,					return_tensors="pt"   )

											with self.device_placement():
																if self.framework == "pt":
																					__a					:						str      =					self.get_inference_context()
																					with inference_context():
																										__a					:						int      =					self._ensure_tensor_on_device(__UpperCAmelCase ,					device=self.device   )
																										__a					:						Union[str, Any]      =					self.model.get_image_embeddings(model_inputs.pop("pixel_values"   )   )
																										__a					:						Optional[Any]      =					image_embeddings

											__a					:						Dict      =					grid_points.shape[1]
											__a					:						Any      =					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 ,					__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[str]      =					grid_points[:, i : i + points_per_batch, :, :]
																__a					:						str      =					input_labels[:, i : i + points_per_batch]
																__a					:						str      =					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:					Any ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Any=0.88 ,					__UpperCAmelCase:					Optional[int]=0.95 ,					__UpperCAmelCase:					Optional[int]=0 ,					__UpperCAmelCase:					int=1 ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						List[Any]      =					model_inputs.pop("input_boxes"   )
											__a					:						Optional[Any]      =					model_inputs.pop("is_last"   )
											__a					:						Dict      =					model_inputs.pop("original_sizes"   ).tolist()
											__a					:						int      =					model_inputs.pop("reshaped_input_sizes"   ).tolist()

											__a					:						Optional[Any]      =					self.model(**__UpperCAmelCase   )

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



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=False ,					__UpperCAmelCase:					int=False ,					__UpperCAmelCase:					List[str]=0.7 ,					)    ->     Dict:




											'''simple docstring'''



											__a					:						Dict      =					[]
											__a					:						Optional[Any]      =					[]
											__a					:						Optional[Any]      =					[]
											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"   )   )

											__a					:						Any      =					torch.cat(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					torch.cat(__UpperCAmelCase   )
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					self.image_processor.post_process_for_mask_generation(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Optional[int]      =					defaultdict(__UpperCAmelCase   )
											for output in model_outputs:
																for k, v in output.items():
																					extra[k].append(__UpperCAmelCase   )

											__a					:						int      =					{}
											if output_rle_mask:
																__a					:						Optional[int]      =					rle_mask

											if output_bboxes_mask:
																__a					:						List[str]      =					bounding_boxes

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





