

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

__lowercase				      =						logging.get_logger(__name__)

@add_end_docstrings(__lowerCamelCase			)
class    _lowercase				(					__lowerCamelCase			):

   def __init__(				self	:    Any			,						**lowerCamelCase__	:    str       ) ->			Optional[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	:    Optional[int]			,						**lowerCamelCase__	:    Dict       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		{}
         A_							       =		{}
         A_							       =		{}
         # preprocess args
         if "points_per_batch" in kwargs:
               A_							       =		kwargs['''points_per_batch''']
         if "points_per_crop" in kwargs:
               A_							       =		kwargs['''points_per_crop''']
         if "crops_n_layers" in kwargs:
               A_							       =		kwargs['''crops_n_layers''']
         if "crop_overlap_ratio" in kwargs:
               A_							       =		kwargs['''crop_overlap_ratio''']
         if "crop_n_points_downscale_factor" in kwargs:
               A_							       =		kwargs['''crop_n_points_downscale_factor''']
         # postprocess args
         if "pred_iou_thresh" in kwargs:
               A_							       =		kwargs['''pred_iou_thresh''']
         if "stability_score_offset" in kwargs:
               A_							       =		kwargs['''stability_score_offset''']
         if "mask_threshold" in kwargs:
               A_							       =		kwargs['''mask_threshold''']
         if "stability_score_thresh" in kwargs:
               A_							       =		kwargs['''stability_score_thresh''']
         if "crops_nms_thresh" in kwargs:
               A_							       =		kwargs['''crops_nms_thresh''']
         if "output_rle_mask" in kwargs:
               A_							       =		kwargs['''output_rle_mask''']
         if "output_bboxes_mask" in kwargs:
               A_							       =		kwargs['''output_bboxes_mask''']
         return preprocess_kwargs, forward_params, postprocess_kwargs

   def __call__(				self	:    Optional[Any]			,						lowerCamelCase__	:    Dict			,						*lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]=None			,						lowerCamelCase__	:    Dict=None			,						**lowerCamelCase__	:    List[str]       ) ->			Any:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    Any=6_4			,						lowerCamelCase__	:    int = 0			,						lowerCamelCase__	:    float = 5_1_2 / 1_5_0_0			,						lowerCamelCase__	:    Optional[int] = 3_2			,						lowerCamelCase__	:    Optional[int] = 1			,						) ->			int:
         """simple docstring"""





         A_							       =		load_image(lowerCamelCase__       )
         A_							       =		self.image_processor.size['''longest_edge''']
         A_       ,A_       ,A_       ,A_							       =		self.image_processor.generate_crop_boxes(
             lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__       )
         A_							       =		self.image_processor(images=lowerCamelCase__			,						return_tensors='''pt'''       )

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

         A_							       =		grid_points.shape[1]
         A_							       =		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__       ):
               A_							       =		grid_points[:, i : i + points_per_batch, :, :]
               A_							       =		input_labels[:, i : i + points_per_batch]
               A_							       =		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	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    str=0.88			,						lowerCamelCase__	:    Dict=0.95			,						lowerCamelCase__	:    int=0			,						lowerCamelCase__	:    int=1			,						) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		model_inputs.pop('''input_boxes'''       )
         A_							       =		model_inputs.pop('''is_last'''       )
         A_							       =		model_inputs.pop('''original_sizes'''       ).tolist()
         A_							       =		model_inputs.pop('''reshaped_input_sizes'''       ).tolist()

         A_							       =		self.model(**lowerCamelCase__       )

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         A_							       =		model_outputs['''pred_masks''']
         A_							       =		self.image_processor.post_process_masks(
             lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						binarize=lowerCamelCase__       )
         A_							       =		model_outputs['''iou_scores''']
         A_       ,A_       ,A_							       =		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	:    Any			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Dict=False			,						lowerCamelCase__	:    str=False			,						lowerCamelCase__	:    List[str]=0.7			,						) ->			List[Any]:
         """simple docstring"""





         A_							       =		[]
         A_							       =		[]
         A_							       =		[]
         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_							       =		torch.cat(lowerCamelCase__       )
         A_							       =		torch.cat(lowerCamelCase__       )
         A_       ,A_       ,A_       ,A_							       =		self.image_processor.post_process_for_mask_generation(
             lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__       )

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

         A_							       =		{}
         if output_rle_mask:
               A_							       =		rle_mask

         if output_bboxes_mask:
               A_							       =		bounding_boxes

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








import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Dict[str, int] = None			,						lowerCamelCase__	:    int = 3_2			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Union[int, float] = 1 / 2_5_5			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    List[str]=7			,						lowerCamelCase__	:    int=3_0			,						lowerCamelCase__	:    List[Any]=4_0_0			,						lowerCamelCase__	:    int=3			,						) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		do_resize
         A_							       =		size if size is not None else {'''shortest_edge''': 2_8_8}
         A_							       =		size_divisor
         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_normalize
         A_							       =		do_center_crop
         A_							       =		image_mean
         A_							       =		image_std
         A_							       =		do_pad
         A_							       =		batch_size
         A_							       =		num_channels
         A_							       =		min_resolution
         A_							       =		max_resolution

   def     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    int=False       ) ->			Dict:
         """simple docstring"""





         if not batched:
               A_							       =		self.size['''shortest_edge''']
               A_							       =		image_inputs[0]
               if isinstance(lowerCamelCase__			,						Image.Image       ):
                     A_       ,A_							       =		image.size
               else:
                     A_       ,A_							       =		image.shape[1], image.shape[2]
               A_							       =		size / min(lowerCamelCase__			,						lowerCamelCase__       )
               if h < w:
                     A_       ,A_							       =		size, scale * w
               else:
                     A_       ,A_							       =		scale * h, size

               A_							       =		int((1_3_3_3 / 8_0_0) * size       )
               if max(lowerCamelCase__			,						lowerCamelCase__       ) > max_size:
                     A_							       =		max_size / max(lowerCamelCase__			,						lowerCamelCase__       )
                     A_							       =		newh * scale
                     A_							       =		neww * scale

               A_       ,A_							       =		int(newh + 0.5       ), int(neww + 0.5       )
               A_       ,A_							       =		(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

         else:
               A_							       =		[]
               for image in image_inputs:
                     A_       ,A_							       =		self.get_expected_values([image]       )
                     expected_values.append((expected_height, expected_width)       )
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[0]       )[0]
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[1]       )[1]

         return expected_height, expected_width



@require_torch
@require_vision
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    BridgeTowerImageProcessor if is_vision_available() else None

   def     UpperCamelCase					(				self	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		BridgeTowerImageProcessingTester(self       )

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_mean'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_std'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_normalize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_resize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size_divisor'''       )       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						Image.Image       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)

   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_							       =		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
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_							       =		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
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





