




import unittest

import numpy as np
import requests

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

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       UpperCAmelCase_									=	False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor





class     lowerCamelCase__(					unittest.TestCase):
      def __init__(				self: Optional[Any]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Tuple=7    ,       UpperCamelCase_: List[Any]=3    ,       UpperCamelCase_: Optional[Any]=18    ,       UpperCamelCase_: List[Any]=30    ,       UpperCamelCase_: int=4_00    ,       UpperCamelCase_: int=None    ,       UpperCamelCase_: Optional[int]=True    ,       UpperCamelCase_: Dict=True    ,       UpperCamelCase_: int=None    ,       ):
             __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   =						size
             __lowerCamelCase   =						do_normalize
             __lowerCamelCase   =						do_convert_rgb
             __lowerCamelCase   =						[5_12, 10_24, 20_48, 40_96]
             __lowerCamelCase   =						patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
             __lowerCamelCase   =						Image.open(requests.get(UpperCamelCase_    ,       stream=UpperCamelCase_   ).raw   ).convert("""RGB"""   )
             return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,     reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' ,     )
@require_torch
@require_vision
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Optional[int]    		=   PixaStructImageProcessor if is_vision_available() else None
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						PixaStructImageProcessingTester(self   )
      @property
      def 			lowerCAmelCase__						(				self: List[str]   ):
             return self.image_processor_tester.prepare_image_processor_dict()
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(UpperCamelCase_    ,       """do_normalize"""   )   )
             self.assertTrue(hasattr(UpperCamelCase_    ,       """do_convert_rgb"""   )   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.image_processor_tester.prepare_dummy_image()

             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             __lowerCamelCase   =						20_48

             __lowerCamelCase   =						image_processor(UpperCamelCase_    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   )
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()    ,       torch.tensor(0.0606   )    ,       atol=1E-3    ,       rtol=1E-3   )   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             # Initialize image_processor
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCamelCase_   )
             for image in image_inputs:
                    self.assertIsInstance(UpperCamelCase_    ,       Image.Image   )

             # Test not batched input
             __lowerCamelCase   =						(
                 (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    __lowerCamelCase   =						image_processor(
                        image_inputs[0]    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (1, max_patch, expected_hidden_dim)    ,       )

                    # Test batched
                    __lowerCamelCase   =						image_processor(
                        UpperCamelCase_    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,       )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             # Initialize image_processor
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCamelCase_   )
             for image in image_inputs:
                    self.assertIsInstance(UpperCamelCase_    ,       Image.Image   )

             # Test not batched input
             __lowerCamelCase   =						(
                 (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                 * self.image_processor_tester.num_channels
             ) + 2

             __lowerCamelCase   =						True

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    with self.assertRaises(UpperCamelCase_   ):
                           __lowerCamelCase   =						image_processor(
                               image_inputs[0]    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches

                    __lowerCamelCase   =						"""Hello"""

                    __lowerCamelCase   =						image_processor(
                        image_inputs[0]    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_    ,       header_text=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (1, max_patch, expected_hidden_dim)    ,       )

                    # Test batched
                    __lowerCamelCase   =						image_processor(
                        UpperCamelCase_    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_    ,       header_text=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,       )
      def 			lowerCAmelCase__						(				self: Dict   ):
             # Initialize image_processor
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCamelCase_    ,       numpify=UpperCamelCase_   )
             for image in image_inputs:
                    self.assertIsInstance(UpperCamelCase_    ,       np.ndarray   )

             __lowerCamelCase   =						(
                 (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    __lowerCamelCase   =						image_processor(
                        image_inputs[0]    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (1, max_patch, expected_hidden_dim)    ,       )

                    # Test batched
                    __lowerCamelCase   =						image_processor(
                        UpperCamelCase_    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,       )




      def 			lowerCAmelCase__						(				self: List[Any]   ):
             # Initialize image_processor
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             __lowerCamelCase   =						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
             __lowerCamelCase   =						(
                 (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    __lowerCamelCase   =						image_processor(
                        image_inputs[0]    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (1, max_patch, expected_hidden_dim)    ,       )

                    # Test batched
                    __lowerCamelCase   =						image_processor(
                        UpperCamelCase_    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,       )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,     reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' ,     )
@require_torch
@require_vision
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	int    		=   PixaStructImageProcessor if is_vision_available() else None
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						PixaStructImageProcessingTester(self    ,       num_channels=4   )
             __lowerCamelCase   =						3
      @property
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             return self.image_processor_tester.prepare_image_processor_dict()
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(UpperCamelCase_    ,       """do_normalize"""   )   )
             self.assertTrue(hasattr(UpperCamelCase_    ,       """do_convert_rgb"""   )   )




      def 			lowerCAmelCase__						(				self: List[Any]   ):
             # Initialize image_processor
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCamelCase_   )
             for image in image_inputs:
                    self.assertIsInstance(UpperCamelCase_    ,       Image.Image   )

             # Test not batched input
             __lowerCamelCase   =						(
                 (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                 * (self.image_processor_tester.num_channels - 1)
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    __lowerCamelCase   =						image_processor(
                        image_inputs[0]    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (1, max_patch, expected_hidden_dim)    ,       )

                    # Test batched
                    __lowerCamelCase   =						image_processor(
                        UpperCamelCase_    ,       return_tensors="""pt"""    ,       max_patches=UpperCamelCase_   ).flattened_patches
                    self.assertEqual(
                        encoded_images.shape    ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,       )







from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [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
UpperCAmelCase_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						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(A__	) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(A__	) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(A__	) - 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.
                     __lowerCamelCase   =						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(A__	)
       return next_generation




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for _ in range(A__	):
              # Create output image
              __lowerCamelCase   =						Image.new("""RGB"""  ,			(len(cells[0]	), len(A__	))	)
              __lowerCamelCase   =						img.load()

              # Save cells to image
              for x in range(len(A__	)	):
                     for y in range(len(cells[0]	)	):
                            __lowerCamelCase   =						255 - cells[y][x] * 255
                            __lowerCamelCase   =						(colour, colour, colour)

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


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

