






'''simple docstring'''







from typing import Any

import numpy as np


def 						_UpperCamelCase							(     snake_case__							):
      assert np.shape(snake_case__							)[0] == np.shape(snake_case__							)[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(snake_case__							,			matrix.T							) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      __A   ,				__A		   =      np.linalg.eigh(snake_case__							)

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0							)							)


def 						_UpperCamelCase							(     snake_case__							):
      __A		   =      np.random.randn(snake_case__							,			snake_case__							)
      __A		   =      np.dot(snake_case__							,			random_matrix.T							)
      assert _is_matrix_spd(snake_case__							)
      return spd_matrix


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							,			snake_case__ = 1_000							,			snake_case__ = 1E-8							,			):
      assert np.shape(snake_case__							)[0] == np.shape(snake_case__							)[1]
      assert np.shape(snake_case__							)[0] == np.shape(snake_case__							)[0]
      assert _is_matrix_spd(snake_case__							)

      # Initialize solution guess, residual, search direction.
      __A		   =      np.zeros((np.shape(snake_case__							)[0], 1)							)
      __A		   =      np.copy(snake_case__							)
      __A		   =      np.copy(snake_case__							)

      # Set initial errors in solution guess and residual.
      __A		   =      1E9
      __A		   =      1E9
      __A		   =      1E9

      # Set iteration counter to threshold number of iterations.
      __A		   =      0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            __A		   =      np.dot(snake_case__							,			snake_case__							)

            # The main algorithm.

            # Update search direction magnitude.
            __A		   =      np.dot(ra.T							,			snake_case__							) / np.dot(pa.T							,			snake_case__							)
            # Update solution guess.
            __A		   =      xa + alpha * pa
            # Calculate new residual.
            __A		   =      ra - alpha * w
            # Calculate new Krylov subspace scale.
            __A		   =      np.dot(r.T							,			snake_case__							) / np.dot(ra.T							,			snake_case__							)
            # Calculate new A conjuage search direction.
            __A		   =      r + beta * pa

            # Calculate errors.
            __A		   =      np.linalg.norm(r - ra							)
            __A		   =      np.linalg.norm(x - xa							)
            __A		   =      np.maximum(snake_case__							,			snake_case__							)

            # Update variables.
            __A		   =      np.copy(snake_case__							)
            __A		   =      np.copy(snake_case__							)
            __A		   =      np.copy(snake_case__							)

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x


def 						_UpperCamelCase							(     ):
      __A		   =      3
      __A		   =      _create_spd_matrix(snake_case__							)
      __A		   =      np.random.randn(snake_case__							,			1							)
      __A		   =      np.dot(snake_case__							,			snake_case__							)

      # Numpy solution.
      __A		   =      np.linalg.solve(snake_case__							,			snake_case__							)

      # Our implementation.
      __A		   =      conjugate_gradient(snake_case__							,			snake_case__							)

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true							) <= 1E-6
      assert np.linalg.norm(x_conjugate_gradient - x_true							) <= 1E-6


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       test_conjugate_gradient()










'''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


