






'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_pytesseract_available():
      from PIL import Image

      from transformers import LayoutLMvaImageProcessor




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=3      ,  __lowerCAmelCase=18      ,  __lowerCAmelCase=30      ,  __lowerCAmelCase=4_00      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=True      ,  ):
       UpperCamelCase_					: Dict            =				size if size is not None else {"""height""": 18, """width""": 18}
       UpperCamelCase_					: str            =				parent
       UpperCamelCase_					: Any            =				batch_size
       UpperCamelCase_					: str            =				num_channels
       UpperCamelCase_					: Tuple            =				image_size
       UpperCamelCase_					: Dict            =				min_resolution
       UpperCamelCase_					: Optional[int]            =				max_resolution
       UpperCamelCase_					: Dict            =				do_resize
       UpperCamelCase_					: int            =				size
       UpperCamelCase_					: Tuple            =				apply_ocr





 def 	_UpperCAmelCase							(				self			):
       return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class      A     (   SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    int			  =    LayoutLMvaImageProcessor if is_pytesseract_available() else None
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				LayoutLMvaImageProcessingTester(self			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return self.image_processor_tester.prepare_image_processor_dict()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """do_resize"""			)			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """size"""			)			)
       self.assertTrue(hasattr(__lowerCAmelCase      ,  """apply_ocr"""			)			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size      ,  {"""height""": 18, """width""": 18}			)

       UpperCamelCase_					: Union[str, Any]            =				self.image_processing_class.from_dict(self.image_processor_dict      ,  size=42			)
       self.assertEqual(image_processor.size      ,  {"""height""": 42, """width""": 42}			)
 def 	_UpperCAmelCase							(				self			):
       pass
 def 	_UpperCAmelCase							(				self			):
       # Initialize image_processing
       UpperCamelCase_					: List[str]            =				self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       UpperCamelCase_					: List[Any]            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowerCAmelCase			)
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase      ,  Image.Image			)

       # Test not batched input
       UpperCamelCase_					: Union[str, Any]            =				image_processing(image_inputs[0]      ,  return_tensors="""pt"""			)
       self.assertEqual(
           encoding.pixel_values.shape      ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )      ,  )

       self.assertIsInstance(encoding.words      ,  __lowerCAmelCase			)
       self.assertIsInstance(encoding.boxes      ,  __lowerCAmelCase			)

       # Test batched
       UpperCamelCase_					: Union[str, Any]            =				image_processing(__lowerCAmelCase      ,  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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )      ,  )
 def 	_UpperCAmelCase							(				self			):
       # Initialize image_processing
       UpperCamelCase_					: Union[str, Any]            =				self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       UpperCamelCase_					: str            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowerCAmelCase      ,  numpify=__lowerCAmelCase			)
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase      ,  np.ndarray			)

       # Test not batched input
       UpperCamelCase_					: 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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )      ,  )

       # Test batched
       UpperCamelCase_					: Tuple            =				image_processing(__lowerCAmelCase      ,  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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )      ,  )
 def 	_UpperCAmelCase							(				self			):
       # Initialize image_processing
       UpperCamelCase_					: Union[str, Any]            =				self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       UpperCamelCase_					: Dict            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowerCAmelCase      ,  torchify=__lowerCAmelCase			)
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase      ,  torch.Tensor			)

       # Test not batched input
       UpperCamelCase_					: int            =				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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )      ,  )

       # Test batched
       UpperCamelCase_					: Tuple            =				image_processing(__lowerCAmelCase      ,  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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )      ,  )





 def 	_UpperCAmelCase							(				self			):
       # with apply_OCR = True
       UpperCamelCase_					: List[str]            =				LayoutLMvaImageProcessor()

       from datasets import load_dataset

       UpperCamelCase_					: Any            =				load_dataset("""hf-internal-testing/fixtures_docvqa"""      ,  split="""test"""			)

       UpperCamelCase_					: Optional[Any]            =				Image.open(ds[0]["""file"""]			).convert("""RGB"""			)

       UpperCamelCase_					: List[Any]            =				image_processing(__lowerCAmelCase      ,  return_tensors="""pt"""			)

       self.assertEqual(encoding.pixel_values.shape      ,  (1, 3, 2_24, 2_24)			)
       self.assertEqual(len(encoding.words			)      ,  len(encoding.boxes			)			)

       # fmt: off
       # the words and boxes were obtained with Tesseract 4.1.1
       UpperCamelCase_					: str            =				[["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
       UpperCamelCase_					: Dict            =				[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
       # fmt: on

       self.assertListEqual(encoding.words      ,  __lowerCAmelCase			)
       self.assertListEqual(encoding.boxes      ,  __lowerCAmelCase			)

       # with apply_OCR = False
       UpperCamelCase_					: Optional[Any]            =				LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				image_processing(__lowerCAmelCase      ,  return_tensors="""pt"""			)

       self.assertEqual(encoding.pixel_values.shape      ,  (1, 3, 2_24, 2_24)			)














'''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

UpperCamelCase      					=logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE__    )
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  **__lowerCAmelCase			):
       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      ,  **__lowerCAmelCase			):
       UpperCamelCase_					: int            =				{}
       UpperCamelCase_					: int            =				{}
       UpperCamelCase_					: Any            =				{}
       # preprocess args
       if "points_per_batch" in kwargs:
             UpperCamelCase_					: Dict            =				kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
             UpperCamelCase_					: str            =				kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
             UpperCamelCase_					: Any            =				kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
             UpperCamelCase_					: 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_					: Any            =				kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
             UpperCamelCase_					: List[str]            =				kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
             UpperCamelCase_					: Tuple            =				kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
             UpperCamelCase_					: Optional[Any]            =				kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
             UpperCamelCase_					: Dict            =				kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
             UpperCamelCase_					: Optional[int]            =				kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
             UpperCamelCase_					: List[Any]            =				kwargs["""output_bboxes_mask"""]
       return preprocess_kwargs, forward_params, postprocess_kwargs
 def __call__(				self      ,  __lowerCAmelCase      ,  *__lowerCAmelCase      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  **__lowerCAmelCase			):
       return super().__call__(__lowerCAmelCase      ,  *__lowerCAmelCase      ,  num_workers=__lowerCAmelCase      ,  batch_size=__lowerCAmelCase      ,  **__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=64      ,  __lowerCAmelCase = 0      ,  __lowerCAmelCase = 5_12 / 15_00      ,  __lowerCAmelCase = 32      ,  __lowerCAmelCase = 1      ,  ):
       UpperCamelCase_					: Tuple            =				load_image(__lowerCAmelCase			)
       UpperCamelCase_					: Tuple            =				self.image_processor.size["""longest_edge"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Dict            =				self.image_processor.generate_crop_boxes(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       UpperCamelCase_					: Any            =				self.image_processor(images=__lowerCAmelCase      ,  return_tensors="""pt"""			)

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

       UpperCamelCase_					: Any            =				grid_points.shape[1]
       UpperCamelCase_					: List[str]            =				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_					: Dict            =				grid_points[:, i : i + points_per_batch, :, :]
             UpperCamelCase_					: List[Any]            =				input_labels[:, i : i + points_per_batch]
             UpperCamelCase_					: Dict            =				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      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0.88      ,  __lowerCAmelCase=0.95      ,  __lowerCAmelCase=0      ,  __lowerCAmelCase=1      ,  ):
       UpperCamelCase_					: Union[str, Any]            =				model_inputs.pop("""input_boxes"""			)
       UpperCamelCase_					: Tuple            =				model_inputs.pop("""is_last"""			)
       UpperCamelCase_					: Dict            =				model_inputs.pop("""original_sizes"""			).tolist()
       UpperCamelCase_					: int            =				model_inputs.pop("""reshaped_input_sizes"""			).tolist()

       UpperCamelCase_					: Optional[Any]            =				self.model(**__lowerCAmelCase			)

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       UpperCamelCase_					: List[str]            =				model_outputs["""pred_masks"""]
       UpperCamelCase_					: Optional[int]            =				self.image_processor.post_process_masks(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  binarize=__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				model_outputs["""iou_scores"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Optional[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      ,  __lowerCAmelCase      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=0.7      ,  ):
       UpperCamelCase_					: Tuple            =				[]
       UpperCamelCase_					: Optional[Any]            =				[]
       UpperCamelCase_					: Tuple            =				[]
       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_					: Any            =				torch.cat(__lowerCAmelCase			)
       UpperCamelCase_					: Union[str, Any]            =				torch.cat(__lowerCAmelCase			)
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Any            =				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_					: Optional[Any]            =				{}
       if output_rle_mask:
             UpperCamelCase_					: int            =				rle_mask

       if output_bboxes_mask:
             UpperCamelCase_					: int            =				bounding_boxes

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






