from __future__ import annotations

from PIL import Image

# Define glider example
snake_case__					:      str				      =  [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
snake_case__					:      List[Any]				      =  [[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def  _lowerCamelCase   (				lowerCamelCase_ :							list[list[int]]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Tuple       =				[]
     for i in range(len(lowerCamelCase_       )       ):
          UpperCAmelCase_							:       List[Any]       =				[]
          for j in range(len(cells[i]       )       ):
               # Get the number of live neighbours
               UpperCAmelCase_							:       Optional[Any]       =				0
               if i > 0 and j > 0:
                    neighbour_count += cells[i - 1][j - 1]
               if i > 0:
                    neighbour_count += cells[i - 1][j]
               if i > 0 and j < len(cells[i]       ) - 1:
                    neighbour_count += cells[i - 1][j + 1]
               if j > 0:
                    neighbour_count += cells[i][j - 1]
               if j < len(cells[i]       ) - 1:
                    neighbour_count += cells[i][j + 1]
               if i < len(lowerCamelCase_       ) - 1 and j > 0:
                    neighbour_count += cells[i + 1][j - 1]
               if i < len(lowerCamelCase_       ) - 1:
                    neighbour_count += cells[i + 1][j]
               if i < len(lowerCamelCase_       ) - 1 and j < len(cells[i]       ) - 1:
                    neighbour_count += cells[i + 1][j + 1]

               # Rules of the game of life (excerpt from Wikipedia):
               # 1. Any live cell with two or three live neighbours survives.
               # 2. Any dead cell with three live neighbours becomes a live cell.
               # 3. All other live cells die in the next generation.
               #    Similarly, all other dead cells stay dead.
               UpperCAmelCase_							:       str       =				cells[i][j] == 1
               if (
                   (alive and 2 <= neighbour_count <= 3)
                   or not alive
                   and neighbour_count == 3
               ):
                    next_generation_row.append(1       )
               else:
                    next_generation_row.append(0       )

          next_generation.append(lowerCamelCase_       )
     return next_generation


def  _lowerCamelCase   (				lowerCamelCase_ :							list[list[int]]     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     UpperCAmelCase_							:       int       =				[]
     for _ in range(lowerCamelCase_       ):
          # Create output image
          UpperCAmelCase_							:       List[Any]       =				Image.new('RGB'     ,					(len(cells[0]       ), len(lowerCamelCase_       ))       )
          UpperCAmelCase_							:       List[Any]       =				img.load()

          # Save cells to image
          for x in range(len(lowerCamelCase_       )       ):
               for y in range(len(cells[0]       )       ):
                    UpperCAmelCase_							:       Optional[int]       =				255 - cells[y][x] * 255
                    UpperCAmelCase_							:       Optional[int]       =				(colour, colour, colour)

        # Save image
          images.append(lowerCamelCase_       )
          UpperCAmelCase_							:       int       =				new_generation(lowerCamelCase_       )
     return images


if __name__ == "__main__":
 snake_case__					:      str				      =  generate_images(GLIDER, 16)
 images[0].save('''out.gif''', save_all=True, append_images=images[1:])







'''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 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				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						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






