



'''simple docstring'''
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[str]         =  logging.get_logger(__name__)






@add_end_docstrings(lowercase   )
class 							lowercase__	(							lowercase   ):





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







							'''simple docstring'''
							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 	UpperCamelCase_  ( self					:				int ,**lowerCamelCase__					:				int ):







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





	def __call__( self					:				Dict ,lowerCamelCase__					:				Optional[Any] ,*lowerCamelCase__					:				Optional[int] ,lowerCamelCase__					:				List[Any]=None ,lowerCamelCase__					:				Tuple=None ,**lowerCamelCase__					:				List[Any] ):







							'''simple docstring'''
							return super().__call__(lowerCamelCase__ ,*lowerCamelCase__ ,num_workers=lowerCamelCase__ ,batch_size=lowerCamelCase__ ,**lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Optional[Any]=64 ,lowerCamelCase__					:				int = 0 ,lowerCamelCase__					:				float = 512 / 1500 ,lowerCamelCase__					:				Optional[int] = 32 ,lowerCamelCase__					:				Optional[int] = 1 ,):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   load_image(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   self.image_processor.size['longest_edge']
							_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Any 				=   self.image_processor.generate_crop_boxes(
							    lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
							_UpperCamelCase					:		List[str] 				=   self.image_processor(images=lowerCamelCase__ ,return_tensors='pt' )

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

							_UpperCamelCase					:		List[Any] 				=   grid_points.shape[1]
							_UpperCamelCase					:		Optional[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 ,lowerCamelCase__ ,lowerCamelCase__ ):
													_UpperCamelCase					:		Optional[Any] 				=   grid_points[:, i : i + points_per_batch, :, :]
													_UpperCamelCase					:		Optional[int] 				=   input_labels[:, i : i + points_per_batch]
													_UpperCamelCase					:		int 				=   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 ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				Tuple=0.8_8 ,lowerCamelCase__					:				Optional[Any]=0.9_5 ,lowerCamelCase__					:				Tuple=0 ,lowerCamelCase__					:				Optional[int]=1 ,):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   model_inputs.pop('input_boxes' )
							_UpperCamelCase					:		str 				=   model_inputs.pop('is_last' )
							_UpperCamelCase					:		int 				=   model_inputs.pop('original_sizes' ).tolist()
							_UpperCamelCase					:		Optional[int] 				=   model_inputs.pop('reshaped_input_sizes' ).tolist()

							_UpperCamelCase					:		Optional[int] 				=   self.model(**lowerCamelCase__ )

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							_UpperCamelCase					:		Dict 				=   model_outputs['pred_masks']
							_UpperCamelCase					:		List[str] 				=   self.image_processor.post_process_masks(
							    lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,binarize=lowerCamelCase__ )
							_UpperCamelCase					:		Dict 				=   model_outputs['iou_scores']
							_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Any 				=   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 	UpperCamelCase_  ( self					:				Optional[Any] ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				str=False ,lowerCamelCase__					:				Tuple=False ,lowerCamelCase__					:				Optional[Any]=0.7 ,):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   []
							_UpperCamelCase					:		List[str] 				=   []
							_UpperCamelCase					:		Dict 				=   []
							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					:		Dict 				=   torch.cat(lowerCamelCase__ )
							_UpperCamelCase					:		Dict 				=   torch.cat(lowerCamelCase__ )
							_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Dict 				=   self.image_processor.post_process_for_mask_generation(
							    lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		List[Any] 				=   defaultdict(lowerCamelCase__ )
							for output in model_outputs:
													for k, v in output.items():
																			extra[k].append(lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   {}
							if output_rle_mask:
													_UpperCamelCase					:		Any 				=   rle_mask

							if output_bboxes_mask:
													_UpperCamelCase					:		Dict 				=   bounding_boxes

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






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor







class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase=7      ,  __lowercase=3      ,  __lowercase=1_8      ,  __lowercase=3_0      ,  __lowercase=4_0_0      ,  __lowercase=True      ,  __lowercase=None      ,  __lowercase=True      ,  __lowercase=None      ,  )    ->			List[Any]:
										lowerCAmelCase_       :					Optional[Any]			 = size if size is not None else {'''shortest_edge''': 2_0}
										lowerCAmelCase_       :					Any			 = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										lowerCAmelCase_       :					Any			 = parent
										lowerCAmelCase_       :					Any			 = batch_size
										lowerCAmelCase_       :					Optional[int]			 = num_channels
										lowerCAmelCase_       :					Tuple			 = image_size
										lowerCAmelCase_       :					List[str]			 = min_resolution
										lowerCAmelCase_       :					Dict			 = max_resolution
										lowerCAmelCase_       :					Tuple			 = do_resize
										lowerCAmelCase_       :					Optional[Any]			 = size
										lowerCAmelCase_       :					Union[str, Any]			 = do_center_crop
										lowerCAmelCase_       :					Optional[Any]			 = crop_size




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}







@require_torch
@require_vision
class  snake_case__(							UpperCAmelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							MobileNetVaImageProcessor if is_vision_available() else None




						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = MobileNetVaImageProcessingTester(self							)




						@property
						def   lowercase_       ( self							)    ->			Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_resize'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''size'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_center_crop'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''crop_size'''							)							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					str			 = self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 2_0}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 1_8, '''width''': 1_8}							)

										lowerCAmelCase_       :					Tuple			 = self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2      ,  crop_size=8_4							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 8_4, '''width''': 8_4}							)




						def   lowercase_       ( self							)    ->			Tuple:
										pass




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										# Initialize image_processing
										lowerCAmelCase_       :					Optional[int]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_       :					Union[str, Any]			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  Image.Image							)

										# Test not batched input
										lowerCAmelCase_       :					Tuple			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )




						def   lowercase_       ( self							)    ->			Optional[int]:
										# Initialize image_processing
										lowerCAmelCase_       :					Tuple			 = self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_       :					Any			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  numpify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  np.ndarray							)

										# Test not batched input
										lowerCAmelCase_       :					Any			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )




						def   lowercase_       ( self							)    ->			Any:
										# Initialize image_processing
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_       :					int			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  torchify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  torch.Tensor							)

										# Test not batched input
										lowerCAmelCase_       :					List[Any]			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Dict			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )