





'''simple docstring'''







def    __UpperCamelCase	(	a    : list[list]     ) ->list[list]:
 snake_case				=			current_set.copy()
 for row_index, row in enumerate(a     ):
  snake_case				=			row[0]
  for column_index, column in enumerate(a     ):
   if magnitude == 0:
    snake_case				=			column
    continue
   snake_case				=			column / magnitude
    # Subtract to cancel term
 snake_case				=			current_set[0]
 snake_case				=			[first_row]
 snake_case				=			current_set[1::]
 for row in current_set:
  snake_case				=			[]
  # If first term is 0, it is already in form we want, so we preserve it
  if row[0] == 0:
   final_set.append(a     )
   continue
  for column_index in range(len(a     )     ):
   temp_row.append(first_row[column_index] - row[column_index]     )
  final_set.append(a     )
 # Create next recursion iteration set
 if len(final_set[0]     ) != 3:
  snake_case				=			final_set[0]
  snake_case				=			[]
  snake_case				=			[]
  for row in final_set[1::]:
   current_first_column.append(row[0]     )
   next_iteration.append(row[1::]     )
  snake_case				=			simplify(a     )
  for i in range(len(a     )     ):
   resultant[i].insert(0					,   current_first_column[i]     )
  resultant.insert(0					,   a     )
  snake_case				=			resultant
 return final_set

def    __UpperCamelCase	(	a    : list[list]     ) ->list:
 if len(a     ) == 0:
  raise IndexError('''solve_simultaneous() requires n lists of length n+1'''     )
 snake_case				=			len(a     ) + 1
 if any(len(a     ) != _length for item in equations     ):
  raise IndexError('''solve_simultaneous() requires n lists of length n+1'''     )
 for row in equations:
  if any(not isinstance(a					,   (int, float)     ) for column in row     ):
   raise ValueError('''solve_simultaneous() requires lists of integers'''     )
 if len(a     ) == 1:
  return [equations[0][-1] / equations[0][0]]
 snake_case				=			equations.copy()
 if any(0 in row for row in data_set     ):
  snake_case				=			data_set.copy()
  snake_case				=			[]
  for row_index, row in enumerate(a     ):
   if 0 not in row:
    snake_case				=			data_set.pop(a     )
    break
  if not full_row:
   raise ValueError('''solve_simultaneous() requires at least 1 full equation'''     )
  data_set.insert(0					,   a     )
 snake_case				=			data_set.copy()
 snake_case				=			simplify(a     )
 snake_case				=			simplified[::-1]
 snake_case				=			[]
 for row in simplified:
  snake_case				=			row[-1]
  if not solutions:
   if row[-2] == 0:
    solutions.append(0     )
    continue
   solutions.append(current_solution / row[-2]     )
   continue
  snake_case				=			row.copy()[: len(a     ) - 1 :]
  while temp_row[0] == 0:
   temp_row.pop(0     )
  if len(a     ) == 0:
   solutions.append(0     )
   continue
  snake_case				=			temp_row[1::]
  snake_case				=			temp_row[::-1]
  for column_index, column in enumerate(a     ):
   current_solution -= column * solutions[column_index]
  solutions.append(a     )
 snake_case				=			[]
 for item in solutions:
  final.append(float(round(a					,   5     )     )     )
 return final[::-1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _lowercase       						=			[
       [2, 1, 1, 1, 1, 4],
       [1, 2, 1, 1, 1, 5],
       [1, 1, 2, 1, 1, 6],
       [1, 1, 1, 2, 1, 7],
       [1, 1, 1, 1, 2, 8],
   ]
   print(solve_simultaneous(eq))
   print(solve_simultaneous([[4, 2]]))








'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

