'''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

__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

@add_end_docstrings(snake_case__    )
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Dict						,					**A						:				int       ):
             super().__init__(**A       )
             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(A       )




      def      _A	(       self						:				Optional[int]						,					**A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    {}
             _UpperCAmelCase     :  Optional[Any]							      =    {}
             _UpperCAmelCase     :  Union[str, Any]							      =    {}
             # preprocess args
             if "points_per_batch" in kwargs:
                    _UpperCAmelCase     :  Tuple							      =    kwargs["points_per_batch"]
             if "points_per_crop" in kwargs:
                    _UpperCAmelCase     :  List[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     :  List[str]							      =    kwargs["crop_n_points_downscale_factor"]
             # postprocess args
             if "pred_iou_thresh" in kwargs:
                    _UpperCAmelCase     :  Optional[Any]							      =    kwargs["pred_iou_thresh"]
             if "stability_score_offset" in kwargs:
                    _UpperCAmelCase     :  str							      =    kwargs["stability_score_offset"]
             if "mask_threshold" in kwargs:
                    _UpperCAmelCase     :  str							      =    kwargs["mask_threshold"]
             if "stability_score_thresh" in kwargs:
                    _UpperCAmelCase     :  str							      =    kwargs["stability_score_thresh"]
             if "crops_nms_thresh" in kwargs:
                    _UpperCAmelCase     :  List[Any]							      =    kwargs["crops_nms_thresh"]
             if "output_rle_mask" in kwargs:
                    _UpperCAmelCase     :  Any							      =    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						:				Optional[Any]						,					A						:				List[str]						,					*A						:				List[Any]						,					A						:				str=None						,					A						:				str=None						,					**A						:				str       ):
             return super().__call__(A						,					*A						,					num_workers=A						,					batch_size=A						,					**A       )




      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[Any]						,					A						:				List[Any]=64						,					A						:				int = 0						,					A						:				float = 512 / 1500						,					A						:				Optional[int] = 32						,					A						:				Optional[int] = 1						,					):
             _UpperCAmelCase     :  List[str]							      =    load_image(A       )
             _UpperCAmelCase     :  int							      =    self.image_processor.size["longest_edge"]
             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    self.image_processor.generate_crop_boxes(
                 A						,					A						,					A						,					A						,					A						,					A       )
             _UpperCAmelCase     :  List[Any]							      =    self.image_processor(images=A						,					return_tensors="pt"       )

             with self.device_placement():
                    if self.framework == "pt":
                           _UpperCAmelCase     :  List[str]							      =    self.get_inference_context()
                           with inference_context():
                                  _UpperCAmelCase     :  Any							      =    self._ensure_tensor_on_device(A						,					device=self.device       )
                                  _UpperCAmelCase     :  Dict							      =    self.model.get_image_embeddings(model_inputs.pop("pixel_values"       )       )
                                  _UpperCAmelCase     :  List[str]							      =    image_embeddings

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

             _UpperCAmelCase     :  List[Any]							      =    self.model(**A       )

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





      def      _A	(       self						:				List[str]						,					A						:				List[str]						,					A						:				Optional[int]=False						,					A						:				List[str]=False						,					A						:				Tuple=0.7						,					):
             _UpperCAmelCase     :  Tuple							      =    []
             _UpperCAmelCase     :  int							      =    []
             _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     :  int							      =    torch.cat(A       )
             _UpperCAmelCase     :  Tuple							      =    torch.cat(A       )
             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    self.image_processor.post_process_for_mask_generation(
                 A						,					A						,					A						,					A       )

             _UpperCAmelCase     :  Any							      =    defaultdict(A       )
             for output in model_outputs:
                    for k, v in output.items():
                           extra[k].append(A       )

             _UpperCAmelCase     :  List[str]							      =    {}
             if output_rle_mask:
                    _UpperCAmelCase     :  Optional[Any]							      =    rle_mask

             if output_bboxes_mask:
                    _UpperCAmelCase     :  str							      =    bounding_boxes

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


'''simple docstring'''




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 MobileViTImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 20}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Dict							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # 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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(A						,					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      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					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      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  List[str]							      =    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
             _UpperCAmelCase     :  Any							      =    image_processing(A						,					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"],
                 )						,					)

