






'''simple docstring'''







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

__A      :       str      =	[[1_2, 1_0], [3, 9]]
__A      :       Any      =	[[3, 4], [7, 4]]
__A      :       List[str]      =	[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
__A      :       Any      =	[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
__A      :       Union[str, Any]      =	[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
__A      :       str      =	[1]
__A      :       str      =	[2]

__A      :       List[Any]      =	logging.getLogger()
__A      :       List[Any]      =	logging.DEBUG
__A      :       Optional[Any]      =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")							,			[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
     if (np.array(snake_case_							)).shape < (2, 2) or (np.array(snake_case_							)).shape < (2, 2):
          with pytest.raises(snake_case_							):
               logger.info(f"\n\t{test_addition.__name__} returned integer"							)
               matop.add(snake_case_							,			snake_case_							)
     elif (np.array(snake_case_							)).shape == (np.array(snake_case_							)).shape:
          logger.info(f"\n\t{test_addition.__name__} with same matrix dims"							)
          __A		   =      (np.array(snake_case_							) + np.array(snake_case_							)).tolist()
          __A		   =      matop.add(snake_case_							,			snake_case_							)
          assert theo == act
     else:
          with pytest.raises(snake_case_							):
               logger.info(f"\n\t{test_addition.__name__} with different matrix dims"							)
               matop.add(snake_case_							,			snake_case_							)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")							,			[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
     if (np.array(snake_case_							)).shape < (2, 2) or (np.array(snake_case_							)).shape < (2, 2):
          with pytest.raises(snake_case_							):
               logger.info(f"\n\t{test_subtraction.__name__} returned integer"							)
               matop.subtract(snake_case_							,			snake_case_							)
     elif (np.array(snake_case_							)).shape == (np.array(snake_case_							)).shape:
          logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims"							)
          __A		   =      (np.array(snake_case_							) - np.array(snake_case_							)).tolist()
          __A		   =      matop.subtract(snake_case_							,			snake_case_							)
          assert theo == act
     else:
          with pytest.raises(snake_case_							):
               logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims"							)
               assert matop.subtract(snake_case_							,			snake_case_							)


@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")							,			[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
     if (np.array(snake_case_							)).shape < (2, 2) or (np.array(snake_case_							)).shape < (2, 2):
          logger.info(f"\n\t{test_multiplication.__name__} returned integer"							)
          with pytest.raises(snake_case_							):
               matop.add(snake_case_							,			snake_case_							)
     elif (np.array(snake_case_							)).shape == (np.array(snake_case_							)).shape:
          logger.info(f"\n\t{test_multiplication.__name__} meets dim requirements"							)
          __A		   =      (np.matmul(snake_case_							,			snake_case_							)).tolist()
          __A		   =      matop.multiply(snake_case_							,			snake_case_							)
          assert theo == act
     else:
          with pytest.raises(snake_case_							):
               logger.info(
                   f"\n\t{test_multiplication.__name__} does not meet dim requirements"							)
               assert matop.subtract(snake_case_							,			snake_case_							)


@pytest.mark.mat_ops
def 						_UpperCamelCase							(     ):
     __A		   =      (3.5 * np.array(snake_case_							)).tolist()
     __A		   =      matop.scalar_multiply(snake_case_							,			3.5							)
     assert theo == act


@pytest.mark.mat_ops
def 						_UpperCamelCase							(     ):
     __A		   =      (np.identity(5							)).tolist()
     __A		   =      matop.identity(5							)
     assert theo == act





@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"							,			[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]							)
def 						_UpperCamelCase							(     snake_case__							):
     if (np.array(snake_case_							)).shape < (2, 2):
          with pytest.raises(snake_case_							):
               logger.info(f"\n\t{test_transpose.__name__} returned integer"							)
               matop.transpose(snake_case_							)
     else:
          __A		   =      (np.transpose(snake_case_							)).tolist()
          __A		   =      matop.transpose(snake_case_							,			return_map=snake_case_							)
          assert theo == act










'''simple docstring'''







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      :       int      =	logging.get_logger(__name__)


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






@add_end_docstrings(lowerCamelCase						)
class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[Any]				,				*lowercase__    :	Dict				,				**lowercase__    :	Union[str, Any]					)  ->       Tuple:
           super().__init__(*lowercase__				,				**lowercase__					)

           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     _UpperCamelCase   (self    :	List[Any]				,				**lowercase__    :	Dict					)  ->       List[Any]:
           __A		   =      {}
           __A		   =      {}
           if "subtask" in kwargs:
                 __A		   =      kwargs["subtask"]
                 __A		   =      kwargs["subtask"]
           if "threshold" in kwargs:
                 __A		   =      kwargs["threshold"]
           if "mask_threshold" in kwargs:
                 __A		   =      kwargs["mask_threshold"]
           if "overlap_mask_area_threshold" in kwargs:
                 __A		   =      kwargs["overlap_mask_area_threshold"]

           return preprocess_kwargs, {}, postprocess_kwargs






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






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






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Optional[int]					)  ->       Dict:
           __A		   =      model_inputs.pop("target_size"					)
           __A		   =      self.model(**lowercase__					)
           __A		   =      target_size
           return model_outputs






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Tuple=0.9				,				lowercase__    :	str=0.5				,				lowercase__    :	Tuple=0.5					)  ->       Tuple:
           __A		   =      None
           if subtask in {"panoptic", None} and hasattr(self.image_processor				,				"post_process_panoptic_segmentation"					):
                 __A		   =      self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor				,				"post_process_instance_segmentation"					):
                 __A		   =      self.image_processor.post_process_instance_segmentation

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

                 __A		   =      []
                 __A		   =      outputs["segmentation"]

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

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

                 __A		   =      []
                 __A		   =      outputs.numpy()
                 __A		   =      np.unique(lowercase__					)

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


