
from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import (
          MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
          MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
          MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
          MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
      )


_snake_case			:       List[str]      =						logging.get_logger(__name__)


_snake_case			:       Optional[Any]      =						Dict[str, Any]
_snake_case			:       Optional[int]      =						List[Prediction]





@add_end_docstrings(__a		)
class      UpperCamelCase_			(			__a		):







      '''simple docstring'''



      def __init__( self       :Union[str, Any]		,						*lowerCAmelCase__       :List[Any]		,						**lowerCAmelCase__       :Dict    )     ->List[str]:
          super().__init__(*lowerCAmelCase__		,						**lowerCAmelCase__    )

          if self.framework == "tf":
              raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''    )

          requires_backends(self		,						"vision"    )
          self.check_model_type(
              dict(
                  MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                  + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                  + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                  + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()    )    )





      def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						**lowerCAmelCase__       :int    )     ->Any:
          lowercase						    =			{}
          lowercase						    =			{}
          if "subtask" in kwargs:
              lowercase						    =			kwargs["subtask"]
              lowercase						    =			kwargs["subtask"]
          if "threshold" in kwargs:
              lowercase						    =			kwargs["threshold"]
          if "mask_threshold" in kwargs:
              lowercase						    =			kwargs["mask_threshold"]
          if "overlap_mask_area_threshold" in kwargs:
              lowercase						    =			kwargs["overlap_mask_area_threshold"]

          return preprocess_kwargs, {}, postprocess_kwargs





      def __call__( self       :Optional[Any]		,						lowerCAmelCase__       :Optional[Any]		,						**lowerCAmelCase__       :Any    )     ->Union[Predictions, List[Prediction]]:
          return super().__call__(lowerCAmelCase__		,						**lowerCAmelCase__    )





      def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Dict=None    )     ->Optional[Any]:
          lowercase						    =			load_image(lowerCAmelCase__    )
          lowercase						    =			[(image.height, image.width)]
          if self.model.config.__class__.__name__ == "OneFormerConfig":
              if subtask is None:
                  lowercase						    =			{}
              else:
                  lowercase						    =			{"task_inputs": [subtask]}
              lowercase						    =			self.image_processor(images=[image]		,						return_tensors="pt"		,						**lowerCAmelCase__    )
              lowercase						    =			self.tokenizer(
                  inputs["task_inputs"]		,						padding="max_length"		,						max_length=self.model.config.task_seq_len		,						return_tensors=self.framework		,						)["input_ids"]
          else:
              lowercase						    =			self.image_processor(images=[image]		,						return_tensors="pt"    )
          lowercase						    =			target_size
          return inputs





      def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :Any    )     ->List[str]:
          lowercase						    =			model_inputs.pop("target_size"    )
          lowercase						    =			self.model(**lowerCAmelCase__    )
          lowercase						    =			target_size
          return model_outputs





      def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :Any=None		,						lowerCAmelCase__       :Optional[int]=0.9		,						lowerCAmelCase__       :str=0.5		,						lowerCAmelCase__       :int=0.5    )     ->Tuple:
          lowercase						    =			None
          if subtask in {"panoptic", None} and hasattr(self.image_processor		,						"post_process_panoptic_segmentation"    ):
              lowercase						    =			self.image_processor.post_process_panoptic_segmentation
          elif subtask in {"instance", None} and hasattr(self.image_processor		,						"post_process_instance_segmentation"    ):
              lowercase						    =			self.image_processor.post_process_instance_segmentation

          if fn is not None:
              lowercase						    =			fn(
                  lowerCAmelCase__		,						threshold=lowerCAmelCase__		,						mask_threshold=lowerCAmelCase__		,						overlap_mask_area_threshold=lowerCAmelCase__		,						target_sizes=model_outputs["target_size"]		,						)[0]

              lowercase						    =			[]
              lowercase						    =			outputs["segmentation"]

              for segment in outputs["segments_info"]:
                  lowercase						    =			(segmentation == segment["id"]) * 255
                  lowercase						    =			Image.fromarray(mask.numpy().astype(np.uinta    )		,						mode="L"    )
                  lowercase						    =			self.model.config.idalabel[segment["label_id"]]
                  lowercase						    =			segment["score"]
                  annotation.append({"score": score, "label": label, "mask": mask}    )

          elif subtask in {"semantic", None} and hasattr(self.image_processor		,						"post_process_semantic_segmentation"    ):
              lowercase						    =			self.image_processor.post_process_semantic_segmentation(
                  lowerCAmelCase__		,						target_sizes=model_outputs["target_size"]    )[0]

              lowercase						    =			[]
              lowercase						    =			outputs.numpy()
              lowercase						    =			np.unique(lowerCAmelCase__    )

              for label in labels:
                  lowercase						    =			(segmentation == label) * 255
                  lowercase						    =			Image.fromarray(mask.astype(np.uinta    )		,						mode="L"    )
                  lowercase						    =			self.model.config.idalabel[label]
                  annotation.append({"score": None, "label": label, "mask": mask}    )
          else:
              raise ValueError(F'''Subtask {subtask} is not supported for model {type(self.model    )}'''    )
          return annotation


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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor





class      UpperCamelCase_			(			unittest.TestCase		):







						'''simple docstring'''



						def __init__( self       :Any		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Optional[int]=7		,						lowerCAmelCase__       :Tuple=3		,						lowerCAmelCase__       :List[str]=10		,						lowerCAmelCase__       :Dict=18		,						lowerCAmelCase__       :int=30		,						lowerCAmelCase__       :List[str]=400		,						lowerCAmelCase__       :Union[str, Any]=True		,						lowerCAmelCase__       :List[Any]=None		,						lowerCAmelCase__       :List[Any]=True		,						lowerCAmelCase__       :int=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Optional[Any]=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Dict=None		,						)     ->Union[str, Any]:
										lowercase						    =			size if size is not None else {"shortest_edge": 18}
										lowercase						    =			crop_size if crop_size is not None else {"height": 18, "width": 18}

										lowercase						    =			parent
										lowercase						    =			batch_size
										lowercase						    =			num_channels
										lowercase						    =			num_frames
										lowercase						    =			image_size
										lowercase						    =			min_resolution
										lowercase						    =			max_resolution
										lowercase						    =			do_resize
										lowercase						    =			size
										lowercase						    =			do_normalize
										lowercase						    =			image_mean
										lowercase						    =			image_std
										lowercase						    =			crop_size





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->int:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}





@require_torch
@require_vision
class      UpperCamelCase_			(			__a		,						unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase			:     Union[str, Any]							      =				VideoMAEImageProcessor if is_vision_available() else None





						def 					SCREAMING_SNAKE_CASE( self       :List[str]    )     ->List[Any]:
										lowercase						    =			VideoMAEImageProcessingTester(self    )





						@property
						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->Any:
										return self.image_processor_tester.prepare_image_processor_dict()





						def 					SCREAMING_SNAKE_CASE( self       :Dict    )     ->str:
										lowercase						    =			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__		,						"do_center_crop"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"size"    )    )





						def 					SCREAMING_SNAKE_CASE( self       :str    )     ->Tuple:
										lowercase						    =			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,						{"shortest_edge": 18}    )
										self.assertEqual(image_processor.crop_size		,						{"height": 18, "width": 18}    )

										lowercase						    =			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 					SCREAMING_SNAKE_CASE( self       :int    )     ->Any:
										pass





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->List[Any]:
										# Initialize image_processing
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										# create random PIL videos
										lowercase						    =			prepare_video_inputs(self.image_processor_tester		,						equal_resolution=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														self.assertIsInstance(video[0]		,						Image.Image    )

										# Test not batched input
										lowercase						    =			image_processing(video_inputs[0]		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)

										# Test batched
										lowercase						    =			image_processing(lowerCAmelCase__		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)





						def 					SCREAMING_SNAKE_CASE( self       :Dict    )     ->Optional[int]:
										# Initialize image_processing
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										lowercase						    =			prepare_video_inputs(self.image_processor_tester		,						equal_resolution=lowerCAmelCase__		,						numpify=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														self.assertIsInstance(video[0]		,						np.ndarray    )

										# Test not batched input
										lowercase						    =			image_processing(video_inputs[0]		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)

										# Test batched
										lowercase						    =			image_processing(lowerCAmelCase__		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)





						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Dict:
										# Initialize image_processing
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										lowercase						    =			prepare_video_inputs(self.image_processor_tester		,						equal_resolution=lowerCAmelCase__		,						torchify=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														self.assertIsInstance(video[0]		,						torch.Tensor    )

										# Test not batched input
										lowercase						    =			image_processing(video_inputs[0]		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)

										# Test batched
										lowercase						    =			image_processing(lowerCAmelCase__		,						return_tensors="pt"    ).pixel_values
										self.assertEqual(
										    encoded_videos.shape		,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )		,						)
