



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 ConvNextImageProcessor

class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def __init__(							self			:				Dict  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[str]=7  ,	UpperCamelCase			:				Union[str, Any]=3  ,	UpperCamelCase			:				Tuple=1_8  ,	UpperCamelCase			:				Tuple=3_0  ,	UpperCamelCase			:				List[Any]=4_0_0  ,	UpperCamelCase			:				str=True  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				List[str]=0.8_7_5  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Dict=[0.5, 0.5, 0.5]  ,	UpperCamelCase			:				int=[0.5, 0.5, 0.5]  ,	)->str:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						size if size is not None else {"shortest_edge": 2_0}
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						parent
      __SCREAMING_SNAKE_CASE							:     Tuple							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_size
      __SCREAMING_SNAKE_CASE							:     str							=						min_resolution
      __SCREAMING_SNAKE_CASE							:     Dict							=						max_resolution
      __SCREAMING_SNAKE_CASE							:     Any							=						do_resize
      __SCREAMING_SNAKE_CASE							:     Tuple							=						size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						crop_pct
      __SCREAMING_SNAKE_CASE							:     str							=						do_normalize
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_mean
      __SCREAMING_SNAKE_CASE							:     Any							=						image_std






     def 						__snake_case     (							self			:				List[str]					)->List[str]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "crop_pct": self.crop_pct,
      }






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      ConvNextImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				List[str]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Any							=						ConvNextImageProcessingTester(self					)





     @property
     def 						__snake_case     (							self			:				int					)->int:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_resize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"size"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"crop_pct"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_normalize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_mean"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_std"					)					)





     def 						__snake_case     (							self			:				Tuple					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 2_0}					)

      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_dict(self.image_processor_dict  ,	size=4_2					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 4_2}					)





     def 						__snake_case     (							self			:				int					)->List[Any]:
      pass





     def 						__snake_case     (							self			:				List[Any]					)->List[str]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase					)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	Image.Image					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     Any							=						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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Dict							=						image_processing(UpperCamelCase  ,	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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)





     def 						__snake_case     (							self			:				Dict					)->int:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	numpify=UpperCamelCase					)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	np.ndarray					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     str							=						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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_processing(UpperCamelCase  ,	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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)






     def 						__snake_case     (							self			:				str					)->List[Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	torch.Tensor					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     int							=						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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processing(UpperCamelCase  ,	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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)












"""simple docstring"""
import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)

 _UpperCamelCase      :	NDArray[floataa]					      =  np.zeros((rows, 1)  ,	dtype=__lowerCAmelCase						)
 for row in reversed(range(__lowerCAmelCase						)						):
  _UpperCamelCase      :	List[str]					      =  0
  for col in range(row + 1  ,	__lowerCAmelCase						):
   total += coefficients[row, col] * x[col]

  _UpperCamelCase      :	Dict					      =  (vector[row] - total) / coefficients[row, row]

 return x






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)
 if rows != columns:
  return np.array(()  ,	dtype=__lowerCAmelCase						)

 # augmented matrix
 _UpperCamelCase      :	NDArray[floataa]					      =  np.concatenate((coefficients, vector)  ,	axis=1						)
 _UpperCamelCase      :	List[str]					      =  augmented_mat.astype("float64"						)

 # scale the matrix leaving it triangular
 for row in range(rows - 1						):
  _UpperCamelCase      :	Optional[Any]					      =  augmented_mat[row, row]
  for col in range(row + 1  ,	__lowerCAmelCase						):
   _UpperCamelCase      :	Union[str, Any]					      =  augmented_mat[col, row] / pivot
   augmented_mat[col, :] -= factor * augmented_mat[row, :]

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

 return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()



