
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_UpperCAmelCase :			str              =	200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_UpperCAmelCase :			Optional[Any]              =	50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_UpperCAmelCase :			Optional[Any]              =	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))


def 				A	( lowercase   ,	lowercase			)       ->  tuple[str, float]:
		'''simple docstring'''

		UpperCamelCase          =					len([g for position, g in enumerate(__lowerCAmelCase			) if g == main_target[position]]			)
		return (item, float(__lowerCAmelCase			))


def 				A	( lowercase   ,	lowercase			)       ->  tuple[str, str]:
		'''simple docstring'''

		UpperCamelCase          =					random.randint(0   ,	len(__lowerCAmelCase			) - 1			)
		UpperCamelCase          =					parent_a[:random_slice] + parent_a[random_slice:]
		UpperCamelCase          =					parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)


def 				A	( lowercase   ,	lowercase			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					list(__lowerCAmelCase			)
		if random.uniform(0   ,	1			) < MUTATION_PROBABILITY:
				UpperCamelCase          =					random.choice(__lowerCAmelCase			)
		return "".join(__lowerCAmelCase			)


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	)       ->  list[str]:
		'''simple docstring'''

		UpperCamelCase          =					[]
		# Generate more children proportionally to the fitness score.
		UpperCamelCase          =					int(parent_a[1] * 100			) + 1
		UpperCamelCase          =					10 if child_n >= 10 else child_n
		for _ in range(__lowerCAmelCase			):
				UpperCamelCase          =					population_score[random.randint(0   ,	__lowerCAmelCase			)][0]

				UpperCamelCase          =					crossover(parent_a[0]   ,	__lowerCAmelCase			)
				# Append new string to the population list.
				pop.append(mutate(__lowerCAmelCase   ,	__lowerCAmelCase			)			)
				pop.append(mutate(__lowerCAmelCase   ,	__lowerCAmelCase			)			)
		return pop


def 				A	( lowercase   ,	lowercase   ,	lowercase = True			)       ->  tuple[int, int, str]:
		'''simple docstring'''

		if N_POPULATION < N_SELECTED:
				UpperCamelCase          =					f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
				raise ValueError(__lowerCAmelCase			)
		# Verify that the target contains no genes besides the ones inside genes variable.
		UpperCamelCase          =					sorted({c for c in target if c not in genes}			)
		if not_in_genes_list:
				UpperCamelCase          =					f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
				raise ValueError(__lowerCAmelCase			)

		# Generate random starting population.
		UpperCamelCase          =					[]
		for _ in range(__lowerCAmelCase			):
				population.append(''.join([random.choice(__lowerCAmelCase			) for i in range(len(__lowerCAmelCase			)			)]			)			)

		# Just some logs to know what the algorithms is doing.
		UpperCamelCase          =					0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(__lowerCAmelCase			)

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				UpperCamelCase          =					[evaluate(__lowerCAmelCase   ,	__lowerCAmelCase			) for item in population]

				# Check if there is a matching evolution.
				UpperCamelCase          =					sorted(__lowerCAmelCase   ,	key=lambda lowercase			: x[1]   ,	reverse=__lowerCAmelCase			)
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    f'''\nGeneration: {generation}'''
						    f'''\nTotal Population:{total_population}'''
						    f'''\nBest score: {population_score[0][1]}'''
						    f'''\nBest string: {population_score[0][0]}'''			)

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				UpperCamelCase          =					population[: int(N_POPULATION / 3			)]
				population.clear()
				population.extend(__lowerCAmelCase			)
				# Normalize population score to be between 0 and 1.
				UpperCamelCase          =					[
				    (item, score / len(__lowerCAmelCase			)) for item, score in population_score
				]

				# This is selection
				for i in range(__lowerCAmelCase			):
						population.extend(select(population_score[int(__lowerCAmelCase			)]   ,	__lowerCAmelCase   ,	__lowerCAmelCase			)			)
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(__lowerCAmelCase			) > N_POPULATION:
								break


if __name__ == "__main__":
					_UpperCAmelCase :			Any              =	(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					_UpperCAmelCase :			List[str]              =	list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\"
					)
					_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase :			List[str]              =	basic(target_str, genes_list)
					print(
					    F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor


class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=30    ,				A_=2    ,				A_=3    ,				A_=True    ,				A_=True    ,				A_=32    ,				A_=2    ,				A_=4    ,				A_=37    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=10    ,				A_=0.02    ,				A_=3    ,				A_=0.6    ,				A_=None    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					image_size
				UpperCamelCase          =					patch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					type_sequence_label_size
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					mask_ratio
				UpperCamelCase          =					scope

				# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
				# (we add 1 for the [CLS] token)
				UpperCamelCase          =					(image_size // patch_size) ** 2
				UpperCamelCase          =					int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

				UpperCamelCase          =					self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				return ViTMAEConfig(
				    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    ,				decoder_hidden_size=self.hidden_size    ,				decoder_num_hidden_layers=self.num_hidden_layers    ,				decoder_num_attention_heads=self.num_attention_heads    ,				decoder_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    ,				mask_ratio=self.mask_ratio    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					TFViTMAEModel(config=A_	)
				UpperCamelCase          =					model(A_    ,				training=A_	)
				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					TFViTMAEForPreTraining(A_	)
				UpperCamelCase          =					model(A_    ,				training=A_	)
				# expected sequence length = num_patches
				UpperCamelCase          =					(self.image_size // self.patch_size) ** 2
				UpperCamelCase          =					self.patch_size**2 * self.num_channels
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, num_patches, expected_num_channels)	)

				# test greyscale images
				UpperCamelCase          =					1
				UpperCamelCase          =					TFViTMAEForPreTraining(A_	)

				UpperCamelCase          =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
				UpperCamelCase          =					model(A_    ,				training=A_	)
				UpperCamelCase          =					self.patch_size**2
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, num_patches, expected_num_channels)	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				((UpperCamelCase)     ,  (UpperCamelCase)     ,  (UpperCamelCase))          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values}
				return config, inputs_dict






@require_tf
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Union[str, Any]     					=    (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		__lowercase	:	Optional[int]     					=    {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Dict     					=    False
		__lowercase	:	List[str]     					=    False



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					TFViTMAEModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				has_text_modality=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='ViTMAE does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

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



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase          =					['pixel_values']
						self.assertListEqual(arg_names[:1]    ,				A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				# make the mask reproducible
				np.random.seed(2	)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				UpperCamelCase          =					int((config.image_size // config.patch_size) ** 2	)
				UpperCamelCase          =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						UpperCamelCase          =					self._prepare_for_class(A_    ,				A_	)

						UpperCamelCase          =					model(A_    ,				noise=A_	)

						UpperCamelCase          =					copy.deepcopy(self._prepare_for_class(A_    ,				A_	)	)
						UpperCamelCase          =					model(**A_    ,				noise=A_	)
						UpperCamelCase          =					outputs_dict[0].numpy()
						UpperCamelCase          =					outputs_keywords[0].numpy()

						self.assertLess(np.sum(np.abs(output_dict - output_keywords	)	)    ,				1e-6	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				# make the mask reproducible
				np.random.seed(2	)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				UpperCamelCase          =					int((config.image_size // config.patch_size) ** 2	)
				UpperCamelCase          =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

				def prepare_numpy_arrays(A_	):
						UpperCamelCase          =					{}
						for k, v in inputs_dict.items():
								if tf.is_tensor(A_	):
										UpperCamelCase          =					v.numpy()
								else:
										UpperCamelCase          =					np.array(A_	)

						return inputs_np_dict

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)

						UpperCamelCase          =					self._prepare_for_class(A_    ,				A_	)
						UpperCamelCase          =					prepare_numpy_arrays(A_	)

						UpperCamelCase          =					model(A_    ,				noise=A_	)
						UpperCamelCase          =					model(**A_    ,				noise=A_	)
						self.assert_outputs_same(A_    ,				A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				# make masks reproducible
				np.random.seed(2	)

				UpperCamelCase          =					int((tf_model.config.image_size // tf_model.config.patch_size) ** 2	)
				UpperCamelCase          =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
				UpperCamelCase          =					tf.constant(A_	)

				# Add `noise` argument.
				# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
				UpperCamelCase          =					tf_noise

				super().check_pt_tf_models(A_    ,				A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# make mask reproducible
				np.random.seed(2	)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				UpperCamelCase          =					{
				    module_member
				    for model_class in self.all_model_classes
				    for module in (import_module(model_class.__module__	),)
				    for module_member_name in dir(A_	)
				    if module_member_name.endswith('MainLayer'	)
				    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
				    and module_member_name[: -len('MainLayer'	)] == model_class.__name__[: -len('Model'	)]
				    for module_member in (getattr(A_    ,				A_	),)
				    if isinstance(A_    ,				A_	)
				    and tf.keras.layers.Layer in module_member.__bases__
				    and getattr(A_    ,				'_keras_serializable'    ,				A_	)
				}

				UpperCamelCase          =					int((config.image_size // config.patch_size) ** 2	)
				UpperCamelCase          =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
				UpperCamelCase          =					tf.convert_to_tensor(A_	)
				inputs_dict.update({'noise': noise}	)

				for main_layer_class in tf_main_layer_classes:
						UpperCamelCase          =					main_layer_class(A_	)

						UpperCamelCase          =					{
						    name: tf.keras.Input(tensor.shape[1:]    ,				dtype=tensor.dtype	) for name, tensor in inputs_dict.items()
						}

						UpperCamelCase          =					tf.keras.Model(A_    ,				outputs=main_layer(A_	)	)
						UpperCamelCase          =					model(A_	)

						with tempfile.TemporaryDirectory() as tmpdirname:
								UpperCamelCase          =					os.path.join(A_    ,				'keras_model.h5'	)
								model.save(A_	)
								UpperCamelCase          =					tf.keras.models.load_model(
								    A_    ,				custom_objects={main_layer_class.__name__: main_layer_class}	)
								assert isinstance(A_    ,				tf.keras.Model	)
								UpperCamelCase          =					model(A_	)
								self.assert_outputs_same(A_    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				# make mask reproducible
				np.random.seed(2	)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				UpperCamelCase          =					int((config.image_size // config.patch_size) ** 2	)
				UpperCamelCase          =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						UpperCamelCase          =					self._prepare_for_class(A_    ,				A_	)
						UpperCamelCase          =					model(A_    ,				noise=A_	)

						if model_class.__name__ == "TFViTMAEModel":
								UpperCamelCase          =					outputs.last_hidden_state.numpy()
								UpperCamelCase          =					0
						else:
								UpperCamelCase          =					outputs.logits.numpy()
								UpperCamelCase          =					0

						with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(A_    ,				saved_model=A_	)
								UpperCamelCase          =					model_class.from_pretrained(A_	)
								UpperCamelCase          =					model(A_    ,				noise=A_	)

								if model_class.__name__ == "TFViTMAEModel":
										UpperCamelCase          =					after_outputs['last_hidden_state'].numpy()
										UpperCamelCase          =					0
								else:
										UpperCamelCase          =					after_outputs['logits'].numpy()
										UpperCamelCase          =					0

								UpperCamelCase          =					np.amax(np.abs(out_a - out_a	)	)
								self.assertLessEqual(A_    ,				1e-5	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				# make mask reproducible
				np.random.seed(2	)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				UpperCamelCase          =					int((config.image_size // config.patch_size) ** 2	)
				UpperCamelCase          =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						UpperCamelCase          =					self._prepare_for_class(A_    ,				A_	)

						UpperCamelCase          =					model(A_    ,				noise=A_	)
						UpperCamelCase          =					model.get_config()
						# make sure that returned config is jsonifiable, which is required by keras
						json.dumps(A_	)
						UpperCamelCase          =					model_class.from_config(model.get_config()	)
						# make sure it also accepts a normal config
						UpperCamelCase          =					model_class.from_config(model.config	)
						UpperCamelCase          =					new_model(A_	)  # Build model
						new_model.set_weights(model.get_weights()	)
						UpperCamelCase          =					new_model(A_    ,				noise=A_	)

						self.assert_outputs_same(A_    ,				A_	)



		@unittest.skip(
		    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'	)
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'	)
				self.assertIsNotNone(A_	)






def 				A	( )       ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image


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



		@cached_property
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'	) if is_vision_available() else None



		@slow
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				# make random mask reproducible across the PT and TF model
				np.random.seed(2	)

				UpperCamelCase          =					TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='tf'	)

				# prepare a noise vector that will be also used for testing the TF model
				# (this way we can ensure that the PT and TF models operate on the same inputs)
				UpperCamelCase          =					ViTMAEConfig()
				UpperCamelCase          =					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
				UpperCamelCase          =					np.random.uniform(size=(1, num_patches)	)

				# forward pass
				UpperCamelCase          =					model(**A_    ,				noise=A_	)

				# verify the logits
				UpperCamelCase          =					tf.convert_to_tensor([1, 196, 768]	)
				self.assertEqual(outputs.logits.shape    ,				A_	)

				UpperCamelCase          =					tf.convert_to_tensor(
				    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]	)

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