





'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Dict					,				__a     :					int					,				__a     :					int=13					,				__a     :					Dict=30					,				__a     :					List[str]=2					,				__a     :					Union[str, Any]=3					,				__a     :					Optional[Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Tuple=32					,				__a     :					List[Any]=2					,				__a     :					int=4					,				__a     :					Optional[Any]=37					,				__a     :					Tuple="gelu"					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.1					,				__a     :					int=10					,				__a     :					int=0.02					,				__a     :					Tuple=3					,				__a     :					List[str]=None					,				):
								_a								= parent
								_a								= batch_size
								_a								= image_size
								_a								= patch_size
								_a								= num_channels
								_a								= is_training
								_a								= use_labels
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= intermediate_size
								_a								= hidden_act
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= type_sequence_label_size
								_a								= initializer_range
								_a								= scope

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								_a								= (image_size // patch_size) ** 2
								_a								= num_patches + 1




				def   UpperCamelCase__   (  self     :					int					):
								_a								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								_a								= None
								if self.use_labels:
												_a								= ids_tensor([self.batch_size]					,				self.type_sequence_label_size					)

								_a								= self.get_config()

								return config, pixel_values, labels




				def   UpperCamelCase__   (  self     :					Dict					):
								return ViTConfig(
								    image_size=self.image_size					,				patch_size=self.patch_size					,				num_channels=self.num_channels					,				hidden_size=self.hidden_size					,				num_hidden_layers=self.num_hidden_layers					,				num_attention_heads=self.num_attention_heads					,				intermediate_size=self.intermediate_size					,				hidden_act=self.hidden_act					,				hidden_dropout_prob=self.hidden_dropout_prob					,				attention_probs_dropout_prob=self.attention_probs_dropout_prob					,				is_decoder=__a					,				initializer_range=self.initializer_range					,				)




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					int					,				__a     :					Tuple					,				__a     :					Optional[Any]					):
								_a								= TFViTModel(config=__a					)
								_a								= model(__a					,				training=__a					)
								self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)

								# Test with an image with different size than the one specified in config.
								_a								= self.image_size // 2
								_a								= pixel_values[:, :, :image_size, :image_size]
								_a								= model(__a					,				interpolate_pos_encoding=__a					,				training=__a					)
								_a								= (image_size // self.patch_size) ** 2 + 1
								self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, seq_length, self.hidden_size)					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					List[Any]					,				__a     :					Optional[int]					,				__a     :					List[str]					):
								_a								= self.type_sequence_label_size
								_a								= TFViTForImageClassification(__a					)
								_a								= model(__a					,				labels=__a					,				training=__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)

								# Test with an image with different size than the one specified in config.
								_a								= self.image_size // 2
								_a								= pixel_values[:, :, :image_size, :image_size]
								_a								= model(__a					,				interpolate_pos_encoding=__a					,				training=__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)

								# test greyscale images
								_a								= 1
								_a								= TFViTForImageClassification(__a					)
								_a								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
								_a								= model(__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.prepare_config_and_inputs()
								_a	,					_a	,					_a								= config_and_inputs
								_a								= {"pixel_values": pixel_values}
								return config, inputs_dict


@require_tf
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
				__a            =(
				    {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__a            =False
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= TFViTModelTester(self					)
								_a								= ConfigTester(self					,				config_class=__a					,				has_text_modality=__a					,				hidden_size=37					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								self.config_tester.run_common_tests()




				@unittest.skip(reason="ViT does not use inputs_embeds"					)
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								pass




				@unittest.skip(reason="ViT does not use inputs_embeds"					)
				def   UpperCamelCase__   (  self     :					str					):
								pass




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_a								= model_class(__a					)
												self.assertIsInstance(model.get_input_embeddings()					,				(tf.keras.layers.Layer)					)
												_a								= model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a					,				tf.keras.layers.Layer					)					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_a								= model_class(__a					)
												_a								= inspect.signature(model.call					)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_a								= [*signature.parameters.keys()]

												_a								= ["pixel_values"]
												self.assertListEqual(arg_names[:1]					,				__a					)




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a					)




				@slow
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= TFViTModel.from_pretrained("google/vit-base-patch16-224"					)
								self.assertIsNotNone(__a					)






def  _lowerCamelCase				( )	->	int:
				_a								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image



@require_tf
@require_vision
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@cached_property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"					) if is_vision_available() else None




				@slow
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"					)

								_a								= self.default_image_processor
								_a								= prepare_img()
								_a								= image_processor(images=__a					,				return_tensors="tf"					)

								# forward pass
								_a								= model(**__a					)

								# verify the logits
								_a								= tf.TensorShape((1, 10_00)					)
								self.assertEqual(outputs.logits.shape					,				__a					)

								_a								= tf.constant([-0.2744, 0.8215, -0.0836]					)

								tf.debugging.assert_near(outputs.logits[0, :3]					,				__a					,				atol=1e-4					)





from __future__ import annotations

_lowercase: Tuple      			=		list[list[int]]

# assigning initial values to the grid
_lowercase: Matrix      			=		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
_lowercase: Matrix      			=		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]






def     a(      A       :    Matrix ,     A       :    int ,     A       :    int ,     A       :    int       )		->       bool:
					"""simple docstring"""





					for i in range(9       ):
										if grid[row][i] == n or grid[i][column] == n:
															return False

					for i in range(3       ):
										for j in range(3       ):
															if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																				return False

					return True






def     a(      A       :    Matrix       )		->       tuple[int, int] | None:
					"""simple docstring"""





					for i in range(9       ):
										for j in range(9       ):
															if grid[i][j] == 0:
																				return i, j
					return None






def     a(      A       :    Matrix       )		->       Matrix | None:
					"""simple docstring"""





					if location := find_empty_location(A       ):
										a     ,				a					     =   location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1 ,     10       ):
										if is_safe(A ,     A ,     A ,     A       ):
															a					     =   digit

															if sudoku(A       ) is not None:
																				return grid

															a					     =   0

					return None






def     a(      A       :    Matrix       )		->       None:
					"""simple docstring"""





					for row in grid:
										for cell in row:
															print(A ,     end=" "       )
										print()


if __name__ == "__main__":
				# make a copy of grid so that you can compare with the unmodified grid
				for example_grid in (initial_grid, no_solution):
								print("\nExample grid:\n" + "=" * 20)
								print_solution(example_grid)
								print("\nExample grid solution:")
								_lowercase: List[str]      			=		sudoku(example_grid)
								if solution is not None:
												print_solution(solution)
								else:
												print("Cannot find a solution.")
