

'''simple docstring'''





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     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=3_0					,						_A=2					,						_A=3					,						_A=True					,						_A=True					,						_A=3_2					,						_A=2					,						_A=4					,						_A=3_7					,						_A="gelu"					,						_A=0.1					,						_A=0.1					,						_A=1_0					,						_A=0.02					,						_A=3					,						_A=0.6					,						_A=None					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							patch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							is_training
												__lowerCAmelCase					=							use_labels
												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							num_hidden_layers
												__lowerCAmelCase					=							num_attention_heads
												__lowerCAmelCase					=							intermediate_size
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							hidden_dropout_prob
												__lowerCAmelCase					=							attention_probs_dropout_prob
												__lowerCAmelCase					=							type_sequence_label_size
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							mask_ratio
												__lowerCAmelCase					=							scope

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




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values, labels




							def        A__  ( self							):
												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=__UpperCamelCase					,						initializer_range=self.initializer_range					,						mask_ratio=self.mask_ratio					,						)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							TFViTMAEModel(config=__UpperCamelCase							)
												__lowerCAmelCase					=							model(__UpperCamelCase					,						training=__UpperCamelCase							)
												self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)							)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							TFViTMAEForPreTraining(__UpperCamelCase							)
												__lowerCAmelCase					=							model(__UpperCamelCase					,						training=__UpperCamelCase							)
												# expected sequence length = num_patches
												__lowerCAmelCase					=							(self.image_size // self.patch_size) ** 2
												__lowerCAmelCase					=							self.patch_size**2 * self.num_channels
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, num_patches, expected_num_channels)							)

												# test greyscale images
												__lowerCAmelCase					=							1
												__lowerCAmelCase					=							TFViTMAEForPreTraining(__UpperCamelCase							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												((__lowerCAmelCase),	(__lowerCAmelCase),	(__lowerCAmelCase))					=							config_and_inputs
												__lowerCAmelCase					=							{'pixel_values': pixel_values}
												return config, inputs_dict





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


							"""simple docstring"""





							__UpperCAmelCase       =(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TFViTMAEModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=__UpperCamelCase					,						has_text_modality=__UpperCamelCase					,						hidden_size=3_7							)




							def        A__  ( self							):
												self.config_tester.run_common_tests()




							@unittest.skip(reason='ViTMAE does not use inputs_embeds'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=							['pixel_values']
																	self.assertListEqual(arg_names[:1]					,						__UpperCamelCase							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase							)




							def        A__  ( self							):
												np.random.seed(2							)

												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(__UpperCamelCase							)
																	__lowerCAmelCase					=							self._prepare_for_class(__UpperCamelCase					,						__UpperCamelCase							)

																	__lowerCAmelCase					=							model(__UpperCamelCase					,						noise=__UpperCamelCase							)

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

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




							def        A__  ( self							):
												np.random.seed(2							)

												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

																	return inputs_np_dict

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(__UpperCamelCase							)

																	__lowerCAmelCase					=							self._prepare_for_class(__UpperCamelCase					,						__UpperCamelCase							)
																	__lowerCAmelCase					=							prepare_numpy_arrays(__UpperCamelCase							)

																	__lowerCAmelCase					=							model(__UpperCamelCase					,						noise=__UpperCamelCase							)
																	__lowerCAmelCase					=							model(**__UpperCamelCase					,						noise=__UpperCamelCase							)
																	self.assert_outputs_same(__UpperCamelCase					,						__UpperCamelCase							)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												np.random.seed(2							)

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

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

												super().check_pt_tf_models(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase							)




							def        A__  ( self							):
												np.random.seed(2							)

												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												__lowerCAmelCase					=							{
												    module_member
												    for model_class in self.all_model_classes
												    for module in (import_module(model_class.__module__							),)
												    for module_member_name in dir(__UpperCamelCase							)
												    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(__UpperCamelCase					,						__UpperCamelCase							),)
												    if isinstance(__UpperCamelCase					,						__UpperCamelCase							)
												    and tf.keras.layers.Layer in module_member.__bases__
												    and getattr(__UpperCamelCase					,						'_keras_serializable'					,						__UpperCamelCase							)
												}

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

												for main_layer_class in tf_main_layer_classes:
																	__lowerCAmelCase					=							main_layer_class(__UpperCamelCase							)

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

																	__lowerCAmelCase					=							tf.keras.Model(__UpperCamelCase					,						outputs=main_layer(__UpperCamelCase							)							)
																	__lowerCAmelCase					=							model(__UpperCamelCase							)

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




							@slow
							def        A__  ( self							):
												np.random.seed(2							)

												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(__UpperCamelCase							)
																	__lowerCAmelCase					=							self._prepare_for_class(__UpperCamelCase					,						__UpperCamelCase							)
																	__lowerCAmelCase					=							model(__UpperCamelCase					,						noise=__UpperCamelCase							)

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

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

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

																						__lowerCAmelCase					=							np.amax(np.abs(out_a - out_a							)							)
																						self.assertLessEqual(__UpperCamelCase					,						1e-5							)




							def        A__  ( self							):
												np.random.seed(2							)

												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(__UpperCamelCase							)
																	__lowerCAmelCase					=							self._prepare_for_class(__UpperCamelCase					,						__UpperCamelCase							)

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

																	self.assert_outputs_same(__UpperCamelCase					,						__UpperCamelCase							)




							@unittest.skip(
							    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'							)
							def        A__  ( self							):
												pass




							@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'							)
							def        A__  ( self							):
												pass




							@slow
							def        A__  ( self							):
												__lowerCAmelCase					=							TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'							)
												self.assertIsNotNone(__UpperCamelCase							)


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							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        A__  ( self							):
												return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'							) if is_vision_available() else None




							@slow
							def        A__  ( self							):
												np.random.seed(2							)

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

												__lowerCAmelCase					=							self.default_image_processor
												__lowerCAmelCase					=							prepare_img()
												__lowerCAmelCase					=							image_processor(images=__UpperCamelCase					,						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)
												__lowerCAmelCase					=							ViTMAEConfig()
												__lowerCAmelCase					=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
												__lowerCAmelCase					=							np.random.uniform(size=(1, num_patches)							)

												# forward pass
												__lowerCAmelCase					=							model(**__UpperCamelCase					,						noise=__UpperCamelCase							)

												# verify the logits
												__lowerCAmelCase					=							tf.convert_to_tensor([1, 1_9_6, 7_6_8]							)
												self.assertEqual(outputs.logits.shape					,						__UpperCamelCase							)

												__lowerCAmelCase					=							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]					,						__UpperCamelCase					,						atol=1e-4							)









def        __lowercase    (  UpperCAmelCase__ = 10		,					UpperCAmelCase__ = 1_000		,					UpperCAmelCase__ = True      ):

					"""simple docstring"""






					assert (
					    isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					    and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					    and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					), "Invalid type of value(s) specified to function!"

					if min_val > max_val:
										raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'      )
					return min_val if option else max_val


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return int((number_a + number_a) / 2      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert (
					    isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ) and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ) and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					), 'argument values must be type of "int"'

					if lower > higher:
										raise ValueError('argument value for lower and higher must be(lower > higher)'      )

					if not lower < to_guess < higher:
										raise ValueError(
										    'guess value must be within the range of lower and higher value'      )

					def answer(UpperCAmelCase__      ) -> str:
										if number > to_guess:
															return "high"
										elif number < to_guess:
															return "low"
										else:
															return "same"

					print('started...'      )

					__lowerCAmelCase					=							lower
					__lowerCAmelCase					=							higher

					__lowerCAmelCase					=							[]

					while True:
										__lowerCAmelCase					=							get_avg(UpperCAmelCase__		,					UpperCAmelCase__      )
										last_numbers.append(UpperCAmelCase__      )

										if answer(UpperCAmelCase__      ) == "low":
															__lowerCAmelCase					=							number
										elif answer(UpperCAmelCase__      ) == "high":
															__lowerCAmelCase					=							number
										else:
															break

					print(F"""guess the number : {last_numbers[-1]}"""      )
					print(F"""details : {last_numbers!s}"""      )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							int(input('Enter lower value : '      ).strip()      )
					__lowerCAmelCase					=							int(input('Enter high value : '      ).strip()      )
					__lowerCAmelCase					=							int(input('Enter value to guess : '      ).strip()      )
					guess_the_number(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )


if __name__ == "__main__":
		main()






