

"""simple docstring"""




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


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BlipImageProcessor


class 		_A							(      unittest.TestCase       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=7     ,    SCREAMING_SNAKE_CASE__=3     ,    SCREAMING_SNAKE_CASE__=18     ,    SCREAMING_SNAKE_CASE__=30     ,    SCREAMING_SNAKE_CASE__=400     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=False     ,    SCREAMING_SNAKE_CASE__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]     ,    SCREAMING_SNAKE_CASE__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]     ,    SCREAMING_SNAKE_CASE__=True     ,    ) ->       Dict:
         lowerCamelCase__						       =      size if size is not None else {"height": 20, "width": 20}
         lowerCamelCase__						       =      parent
         lowerCamelCase__						       =      batch_size
         lowerCamelCase__						       =      num_channels
         lowerCamelCase__						       =      image_size
         lowerCamelCase__						       =      min_resolution
         lowerCamelCase__						       =      max_resolution
         lowerCamelCase__						       =      do_resize
         lowerCamelCase__						       =      size
         lowerCamelCase__						       =      do_normalize
         lowerCamelCase__						       =      image_mean
         lowerCamelCase__						       =      image_std
         lowerCamelCase__						       =      do_pad
         lowerCamelCase__						       =      do_convert_rgb





      def        _lowerCamelCase	(	self		) ->       Optional[int]:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_convert_rgb": self.do_convert_rgb,
             "do_pad": self.do_pad,
         }





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__=False     ,    SCREAMING_SNAKE_CASE__=False     ,    SCREAMING_SNAKE_CASE__=False		) ->       Union[str, Any]:
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
            lowerCamelCase__						       =      []
            for i in range(self.batch_size		):
               image_inputs.append(
                   np.random.randint(
                       255     ,    size=(self.num_channels, self.max_resolution, self.max_resolution)     ,    dtype=np.uinta		)		)
         else:
            lowerCamelCase__						       =      []
            for i in range(self.batch_size		):
               lowerCamelCase__       ,							lowerCamelCase__						       =      np.random.choice(np.arange(self.min_resolution     ,    self.max_resolution		)     ,    2		)
               image_inputs.append(np.random.randint(255     ,    size=(self.num_channels, width, height)     ,    dtype=np.uinta		)		)

         if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            lowerCamelCase__						       =      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__     ,    0     ,    -1		)		) for x in image_inputs]

         if torchify:
            lowerCamelCase__						       =      [torch.from_numpy(SCREAMING_SNAKE_CASE__		) for x in image_inputs]

         return image_inputs






@require_torch
@require_vision
class 		_A							(      __a					,							unittest.TestCase       ):
      __a             =						BlipImageProcessor if is_vision_available() else None





      def        _lowerCamelCase	(	self		) ->       str:
         lowerCamelCase__						       =      BlipImageProcessingTester(self		)





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





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         lowerCamelCase__						       =      self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "do_resize"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "size"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "do_normalize"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "image_mean"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "image_std"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "do_convert_rgb"		)		)





      def        _lowerCamelCase	(	self		) ->       Tuple:
         pass





      def        _lowerCamelCase	(	self		) ->       str:
         # Initialize image_processor
         lowerCamelCase__						       =      self.image_processing_class(**self.image_processor_dict		)
         # create random PIL images
         lowerCamelCase__						       =      self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__		)
         for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,    Image.Image		)

         # Test not batched input
         lowerCamelCase__						       =      image_processor(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
         lowerCamelCase__						       =      image_processor(SCREAMING_SNAKE_CASE__     ,    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        _lowerCamelCase	(	self		) ->       Tuple:
         # Initialize image_processor
         lowerCamelCase__						       =      self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         lowerCamelCase__						       =      self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__     ,    numpify=SCREAMING_SNAKE_CASE__		)
         for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,    np.ndarray		)

         # Test not batched input
         lowerCamelCase__						       =      image_processor(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
         lowerCamelCase__						       =      image_processor(SCREAMING_SNAKE_CASE__     ,    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        _lowerCamelCase	(	self		) ->       Optional[int]:
         # Initialize image_processor
         lowerCamelCase__						       =      self.image_processing_class(**self.image_processor_dict		)
         # create random PyTorch tensors
         lowerCamelCase__						       =      self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__     ,    torchify=SCREAMING_SNAKE_CASE__		)
         for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,    torch.Tensor		)

         # Test not batched input
         lowerCamelCase__						       =      image_processor(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
         lowerCamelCase__						       =      image_processor(SCREAMING_SNAKE_CASE__     ,    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"],
             )     ,    )






@require_torch
@require_vision
class 		_A							(      __a					,							unittest.TestCase       ):
      __a             =						BlipImageProcessor if is_vision_available() else None





      def        _lowerCamelCase	(	self		) ->       Tuple:
         lowerCamelCase__						       =      BlipImageProcessingTester(self     ,    num_channels=4		)
         lowerCamelCase__						       =      3





      @property
      def        _lowerCamelCase	(	self		) ->       Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         lowerCamelCase__						       =      self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "do_resize"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "size"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "do_normalize"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "image_mean"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "image_std"		)		)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,    "do_convert_rgb"		)		)





      def        _lowerCamelCase	(	self		) ->       List[str]:
         pass





      def        _lowerCamelCase	(	self		) ->       int:
         # Initialize image_processor
         lowerCamelCase__						       =      self.image_processing_class(**self.image_processor_dict		)
         # create random PIL images
         lowerCamelCase__						       =      self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__		)
         for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,    Image.Image		)

         # Test not batched input
         lowerCamelCase__						       =      image_processor(image_inputs[0]     ,    return_tensors="pt"		).pixel_values
         self.assertEqual(
             encoded_images.shape     ,    (
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )     ,    )

         # Test batched
         lowerCamelCase__						       =      image_processor(SCREAMING_SNAKE_CASE__     ,    return_tensors="pt"		).pixel_values
         self.assertEqual(
             encoded_images.shape     ,    (
                 self.image_processor_tester.batch_size,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )     ,    )







"""simple docstring"""




import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




