




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(UpperCAmelCase_   )
class   __a(     UpperCAmelCase_   ):






    """simple docstring"""





    def __init__(      self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE  ) ->		Optional[int]:
         super().__init__(*__UpperCamelCase   ,**__UpperCamelCase  )

         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   ,**_SCREAMING_SNAKE_CASE  ) ->		str:
         UpperCAmelCase_			:							int   =			{}
         UpperCAmelCase_			:							List[Any]   =			{}
         if "subtask" in kwargs:
              UpperCAmelCase_			:							List[str]   =			kwargs['''subtask''']
              UpperCAmelCase_			:							List[Any]   =			kwargs['''subtask''']
         if "threshold" in kwargs:
              UpperCAmelCase_			:							str   =			kwargs['''threshold''']
         if "mask_threshold" in kwargs:
              UpperCAmelCase_			:							Dict   =			kwargs['''mask_threshold''']
         if "overlap_mask_area_threshold" in kwargs:
              UpperCAmelCase_			:							Any   =			kwargs['''overlap_mask_area_threshold''']

         return preprocess_kwargs, {}, postprocess_kwargs





    def __call__(      self   ,_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE  ) ->		Tuple:
         return super().__call__(__UpperCamelCase   ,**__UpperCamelCase  )





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





    def 							a__      (      self   ,_SCREAMING_SNAKE_CASE  ) ->		List[str]:
         UpperCAmelCase_			:							Optional[Any]   =			model_inputs.pop('''target_size'''  )
         UpperCAmelCase_			:							List[str]   =			self.model(**__UpperCamelCase  )
         UpperCAmelCase_			:							Tuple   =			target_size
         return model_outputs





    def 							a__      (      self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=0.9   ,_SCREAMING_SNAKE_CASE=0.5   ,_SCREAMING_SNAKE_CASE=0.5  ) ->		int:
         UpperCAmelCase_			:							Union[str, Any]   =			None
         if subtask in {"panoptic", None} and hasattr(self.image_processor   ,'''post_process_panoptic_segmentation'''  ):
              UpperCAmelCase_			:							Any   =			self.image_processor.post_process_panoptic_segmentation
         elif subtask in {"instance", None} and hasattr(self.image_processor   ,'''post_process_instance_segmentation'''  ):
              UpperCAmelCase_			:							str   =			self.image_processor.post_process_instance_segmentation

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

              UpperCAmelCase_			:							int   =			[]
              UpperCAmelCase_			:							Optional[Any]   =			outputs['''segmentation''']

              for segment in outputs["segments_info"]:
                   UpperCAmelCase_			:							Union[str, Any]   =			(segmentation == segment['''id''']) * 255
                   UpperCAmelCase_			:							List[Any]   =			Image.fromarray(mask.numpy().astype(np.uinta  )   ,mode='''L'''  )
                   UpperCAmelCase_			:							List[str]   =			self.model.config.idalabel[segment['''label_id''']]
                   UpperCAmelCase_			:							Union[str, Any]   =			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_			:							str   =			self.image_processor.post_process_semantic_segmentation(
                  __UpperCamelCase   ,target_sizes=model_outputs['''target_size''']  )[0]

              UpperCAmelCase_			:							List[str]   =			[]
              UpperCAmelCase_			:							Union[str, Any]   =			outputs.numpy()
              UpperCAmelCase_			:							Dict   =			np.unique(__UpperCamelCase  )

              for label in labels:
                   UpperCAmelCase_			:							Optional[Any]   =			(segmentation == label) * 255
                   UpperCAmelCase_			:							Dict   =			Image.fromarray(mask.astype(np.uinta  )   ,mode='''L'''  )
                   UpperCAmelCase_			:							Any   =			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





'''simple docstring'''







import unittest
from timeit import timeit






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       first_num if first_num >= second_num else second_num
		_snake_case	       =       max_num
		while (common_mult % first_num > 0) or (common_mult % second_num > 0):
				common_mult += max_num
		return common_mult






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return b if a == 0 else greatest_common_divisor(b % a						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return first_num // greatest_common_divisor(lowercase_						,							lowercase_  ) * second_num






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       (
		    "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
		)
		print(
		    "least_common_multiple_slow():"						,							timeit("least_common_multiple_slow(1000, 999)"						,							setup=lowercase_  )						,							)
		print(
		    "least_common_multiple_fast():"						,							timeit("least_common_multiple_fast(1000, 999)"						,							setup=lowercase_  )						,							)





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (
					    (1_0, 2_0),
					    (1_3, 1_5),
					    (4, 3_1),
					    (1_0, 4_2),
					    (4_3, 3_4),
					    (5, 1_2),
					    (1_2, 2_5),
					    (1_0, 2_5),
					    (6, 9),
					)
					lowercase__    = (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for i, (first_num, second_num) in enumerate(self.test_inputs ):
									_snake_case	       =       least_common_multiple_slow(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       least_common_multiple_fast(__UpperCamelCase,  __UpperCamelCase )
									with self.subTest(i=__UpperCamelCase ):
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )


if __name__ == "__main__":
						benchmark()
						unittest.main()





