

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor





class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				List[str]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				str=1_3							,   _UpperCamelCase :				str=3_0							,   _UpperCamelCase :				List[str]=2							,   _UpperCamelCase :				int=3							,   _UpperCamelCase :				List[str]=True							,   _UpperCamelCase :				Optional[Any]=True							,   _UpperCamelCase :				int=3_2							,   _UpperCamelCase :				Dict=2							,   _UpperCamelCase :				Any=4							,   _UpperCamelCase :				Optional[Any]=3_7							,   _UpperCamelCase :				int="gelu"							,   _UpperCamelCase :				List[str]=0.1							,   _UpperCamelCase :				Any=0.1							,   _UpperCamelCase :				Tuple=1_0							,   _UpperCamelCase :				Union[str, Any]=0.02							,   _UpperCamelCase :				List[Any]=3							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				Dict=2							,   )      ->Tuple:
									snake_case_   =	parent
									snake_case_   =	batch_size
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	is_training
									snake_case_   =	use_labels
									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	type_sequence_label_size
									snake_case_   =	initializer_range
									snake_case_   =	scope
									snake_case_   =	encoder_stride

									# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
									snake_case_   =	(image_size // patch_size) ** 2
									snake_case_   =	num_patches + 2







			def 				snake_case__(     self :				Tuple							)      ->Dict:
									snake_case_   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

									snake_case_   =	self.get_config()

									return config, pixel_values, labels







			def 				snake_case__(     self :				List[Any]							)      ->Any:
									return DeiTConfig(
									    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=_UpperCamelCase							,   initializer_range=self.initializer_range							,   encoder_stride=self.encoder_stride							,   )







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				int							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[Any]							)      ->Dict:
									snake_case_   =	TFDeiTModel(config=_UpperCamelCase							)
									snake_case_   =	model(_UpperCamelCase							)
									self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)							)







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Tuple							)      ->List[Any]:
									snake_case_   =	TFDeiTForMaskedImageModeling(config=_UpperCamelCase							)
									snake_case_   =	model(_UpperCamelCase							)
									self.parent.assertEqual(
									    result.reconstruction.shape							,   (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

									# test greyscale images
									snake_case_   =	1
									snake_case_   =	TFDeiTForMaskedImageModeling(_UpperCamelCase							)

									snake_case_   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
									snake_case_   =	model(_UpperCamelCase							)
									self.parent.assertEqual(result.reconstruction.shape							,   (self.batch_size, 1, self.image_size, self.image_size)							)







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Any							)      ->Tuple:
									snake_case_   =	self.type_sequence_label_size
									snake_case_   =	TFDeiTForImageClassification(_UpperCamelCase							)
									snake_case_   =	model(_UpperCamelCase							,   labels=_UpperCamelCase							)
									self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.type_sequence_label_size)							)

									# test greyscale images
									snake_case_   =	1
									snake_case_   =	TFDeiTForImageClassification(_UpperCamelCase							)

									snake_case_   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
									snake_case_   =	model(_UpperCamelCase							,   labels=_UpperCamelCase							)
									self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.type_sequence_label_size)							)







			def 				snake_case__(     self :				int							)      ->Any:
									snake_case_   =	self.prepare_config_and_inputs()
									snake_case_,    snake_case_,    snake_case_   =	config_and_inputs
									snake_case_   =	{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_tf
class 	snake_case_   (		__A	,       __A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				(
			    (
			        TFDeiTModel,
			        TFDeiTForImageClassification,
			        TFDeiTForImageClassificationWithTeacher,
			        TFDeiTForMaskedImageModeling,
			    )
			    if is_tf_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				(
			    {
			        "feature-extraction": TFDeiTModel,
			        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
			    }
			    if is_tf_available()
			    else {}
			)

			SCREAMING_SNAKE_CASE     :   int									=				False
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				False
			SCREAMING_SNAKE_CASE     :   List[Any]									=				False
			SCREAMING_SNAKE_CASE     :   List[Any]									=				False







			def 				snake_case__(     self :				List[Any]							)      ->Tuple:
									snake_case_   =	TFDeiTModelTester(self							)
									snake_case_   =	ConfigTester(self							,   config_class=_UpperCamelCase							,   has_text_modality=_UpperCamelCase							,   hidden_size=3_7							)







			def 				snake_case__(     self :				List[str]							)      ->Tuple:
									self.config_tester.run_common_tests()







			@unittest.skip(reason='''DeiT does not use inputs_embeds'''							)
			def 				snake_case__(     self :				Tuple							)      ->int:
									pass







			def 				snake_case__(     self :				Optional[int]							)      ->str:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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







			def 				snake_case__(     self :				Dict							)      ->str:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case_   =	['''pixel_values''']
															self.assertListEqual(arg_names[:1]							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							)      ->str:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase							)







			def 				snake_case__(     self :				Optional[int]							)      ->Dict:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCamelCase							)







			def 				snake_case__(     self :				Any							)      ->List[Any]:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				List[str]=False							)      ->Tuple:
									snake_case_   =	super()._prepare_for_class(_UpperCamelCase							,   _UpperCamelCase							,   return_labels=_UpperCamelCase							)

									if return_labels:
															if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call							).parameters:
																					del inputs_dict["labels"]

									return inputs_dict







			@slow
			def 				snake_case__(     self :				str							)      ->Tuple:
									for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_   =	TFDeiTModel.from_pretrained(_UpperCamelCase							)
															self.assertIsNotNone(_UpperCamelCase							)






def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
						return image





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







			'''simple docstring'''

			@cached_property
			def 				snake_case__(     self :				List[str]							)      ->Any:
									return (
									    DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''							)
									    if is_vision_available()
									    else None
									)







			@slow
			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''							)

									snake_case_   =	self.default_image_processor
									snake_case_   =	prepare_img()
									snake_case_   =	image_processor(images=_UpperCamelCase							,   return_tensors='''tf'''							)

									# forward pass
									snake_case_   =	model(**_UpperCamelCase							)

									# verify the logits
									snake_case_   =	tf.TensorShape((1, 1_0_0_0)							)
									self.assertEqual(outputs.logits.shape							,   _UpperCamelCase							)

									snake_case_   =	tf.constant([-1.0266, 0.1912, -1.2861]							)

									self.assertTrue(np.allclose(outputs.logits[0, :3]							,   _UpperCamelCase							,   atol=1e-4							)							)


from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# We need to create solution object to save path.
						snake_case_   =	[[0 for _ in range(SCREAMING_SNAKE_CASE__       )] for _ in range(SCREAMING_SNAKE_CASE__       )]
						snake_case_   =	run_maze(SCREAMING_SNAKE_CASE__  ,		0  ,		0  ,		SCREAMING_SNAKE_CASE__       )
						if solved:
												print('''\n'''.join(str(SCREAMING_SNAKE_CASE__       ) for row in solutions       )       )
						else:
												print('''No solution exists!'''       )
						return solved


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# Final check point.
						if i == j == (size - 1):
												snake_case_   =	1
												return True

						snake_case_   =	(not i < 0) and (not j < 0)  # Check lower bounds
						snake_case_   =	(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												snake_case_   =	(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		snake_case_   =	1

																		# check for directions
																		if (
																		    run_maze(SCREAMING_SNAKE_CASE__  ,		i + 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j + 1  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		i - 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j - 1  ,		SCREAMING_SNAKE_CASE__       )
																		):
																								return True

																		snake_case_   =	0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()