


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,
   )


_A									=		logging.get_logger(__name__)


_A									=		Dict[str, Any]
_A									=		List[Prediction]






@add_end_docstrings(A_						)
class        UpperCAmelCase__			(   A_						):



    """simple docstring"""






    def __init__(     self   ,  *A_   ,  **A_       )    ->			Tuple:
         super().__init__(*A_   ,  **A_       )

         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 _a   (     self   ,  **A_       )    ->			List[Any]:
         __UpperCamelCase       				={}
         __UpperCamelCase       				={}
         if "subtask" in kwargs:
              __UpperCamelCase       				=kwargs['subtask']
              __UpperCamelCase       				=kwargs['subtask']
         if "threshold" in kwargs:
              __UpperCamelCase       				=kwargs['threshold']
         if "mask_threshold" in kwargs:
              __UpperCamelCase       				=kwargs['mask_threshold']
         if "overlap_mask_area_threshold" in kwargs:
              __UpperCamelCase       				=kwargs['overlap_mask_area_threshold']

         return preprocess_kwargs, {}, postprocess_kwargs






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






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






    def _a   (     self   ,  A_       )    ->			Optional[int]:
         __UpperCamelCase       				=model_inputs.pop('target_size'       )
         __UpperCamelCase       				=self.model(**A_       )
         __UpperCamelCase       				=target_size
         return model_outputs






    def _a   (     self   ,  A_   ,  A_=None   ,  A_=0.9   ,  A_=0.5   ,  A_=0.5       )    ->			int:
         __UpperCamelCase       				=None
         if subtask in {"panoptic", None} and hasattr(self.image_processor   ,  'post_process_panoptic_segmentation'       ):
              __UpperCamelCase       				=self.image_processor.post_process_panoptic_segmentation
         elif subtask in {"instance", None} and hasattr(self.image_processor   ,  'post_process_instance_segmentation'       ):
              __UpperCamelCase       				=self.image_processor.post_process_instance_segmentation

         if fn is not None:
              __UpperCamelCase       				=fn(
                  A_   ,  threshold=A_   ,  mask_threshold=A_   ,  overlap_mask_area_threshold=A_   ,  target_sizes=model_outputs['target_size']   ,  )[0]

              __UpperCamelCase       				=[]
              __UpperCamelCase       				=outputs['segmentation']

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

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

              __UpperCamelCase       				=[]
              __UpperCamelCase       				=outputs.numpy()
              __UpperCamelCase       				=np.unique(A_       )

              for label in labels:
                   __UpperCamelCase       				=(segmentation == label) * 255
                   __UpperCamelCase       				=Image.fromarray(mask.astype(np.uinta       )   ,  mode='L'       )
                   __UpperCamelCase       				=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_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import EfficientNetImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






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



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






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






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict   ,  size=42       )
         self.assertEqual(image_processor.size   ,  {'height': 42, 'width': 42}       )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=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       				=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       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=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       				=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       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			Dict:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=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       				=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       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )





