


import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMAEForPreTraining, ViTMAEModel
			from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class     a		:




							"""simple docstring"""


							def __init__(							self	:  List[str]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  int=13   ,     lowerCamelCase	:  int=30   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Tuple=3   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[Any]=5   ,     lowerCamelCase	:  Tuple=4   ,     lowerCamelCase	:  Union[str, Any]=37   ,     lowerCamelCase	:  int="gelu"   ,     lowerCamelCase	:  Dict=0.1   ,     lowerCamelCase	:  str=0.1   ,     lowerCamelCase	:  List[Any]=10   ,     lowerCamelCase	:  Optional[int]=0.02   ,     lowerCamelCase	:  List[str]=3   ,     lowerCamelCase	:  Dict=0.6   ,     lowerCamelCase	:  Dict=None   ,     )     ->   Any:
														__snake_case		:      str										=       parent
														__snake_case		:      Tuple										=       batch_size
														__snake_case		:      Optional[int]										=       image_size
														__snake_case		:      Dict										=       patch_size
														__snake_case		:      List[Any]										=       num_channels
														__snake_case		:      Tuple										=       is_training
														__snake_case		:      Dict										=       use_labels
														__snake_case		:      List[Any]										=       hidden_size
														__snake_case		:      List[str]										=       num_hidden_layers
														__snake_case		:      List[Any]										=       num_attention_heads
														__snake_case		:      Union[str, Any]										=       intermediate_size
														__snake_case		:      Any										=       hidden_act
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Optional[int]										=       attention_probs_dropout_prob
														__snake_case		:      Union[str, Any]										=       type_sequence_label_size
														__snake_case		:      List[str]										=       initializer_range
														__snake_case		:      Optional[int]										=       mask_ratio
														__snake_case		:      Optional[int]										=       scope

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

							def    __snake_case (							self	:  Optional[int]    )     ->   Union[str, Any]:
														__snake_case		:      int										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

														__snake_case		:      List[Any]										=       None
														if self.use_labels:
																					__snake_case		:      Optional[Any]										=       ids_tensor([self.batch_size]   ,     self.type_sequence_label_size    )

														__snake_case		:      Optional[Any]										=       self.get_config()

														return config, pixel_values, labels

							def    __snake_case (							self	:  int    )     ->   Union[str, Any]:
														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   ,     hidden_act=self.hidden_act   ,     hidden_dropout_prob=self.hidden_dropout_prob   ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,     is_decoder=lowerCamelCase   ,     initializer_range=self.initializer_range   ,     mask_ratio=self.mask_ratio   ,     )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Union[str, Any]    )     ->   List[str]:
														__snake_case		:      List[Any]										=       ViTMAEModel(config=lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Optional[Any]										=       model(lowerCamelCase    )
														self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size)    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  int    )     ->   Optional[Any]:
														__snake_case		:      Dict										=       ViTMAEForPreTraining(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      List[Any]										=       model(lowerCamelCase    )
														__snake_case		:      List[Any]										=       (self.image_size // self.patch_size) ** 2
														__snake_case		:      str										=       self.patch_size**2 * self.num_channels
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, num_patches, expected_num_channels)    )

														# test greyscale images
														__snake_case		:      List[Any]										=       1
														__snake_case		:      int										=       ViTMAEForPreTraining(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Tuple										=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
														__snake_case		:      Optional[int]										=       model(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       self.patch_size**2
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, num_patches, expected_num_channels)    )






							def    __snake_case (							self	:  Dict    )     ->   Tuple:
														__snake_case		:      Optional[int]										=       self.prepare_config_and_inputs()
														__snake_case			,      __snake_case			,      __snake_case		:      Optional[int]										=       config_and_inputs
														__snake_case		:      Union[str, Any]										=       {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class     a		(_lowerCAmelCase					,    _lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							__UpperCAmelCase   :							List[Any]					  =  {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

							__UpperCAmelCase   :							Any					  =  False
							__UpperCAmelCase   :							List[str]					  =  False
							__UpperCAmelCase   :							int					  =  False
							__UpperCAmelCase   :							Any					  =  False

							def    __snake_case (							self	:  List[str]    )     ->   Dict:
														__snake_case		:      Union[str, Any]										=       ViTMAEModelTester(self    )
														__snake_case		:      List[Any]										=       ConfigTester(self   ,     config_class=lowerCamelCase   ,     has_text_modality=lowerCamelCase   ,     hidden_size=37    )

							def    __snake_case (							self	:  List[Any]    )     ->   int:
														self.config_tester.run_common_tests()

							@unittest.skip(reason="ViTMAE does not use inputs_embeds"    )
							def    __snake_case (							self	:  str    )     ->   Tuple:
														pass

							def    __snake_case (							self	:  Any    )     ->   List[str]:
														__snake_case			,      __snake_case		:      Optional[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case		:      Optional[int]										=       model_class(lowerCamelCase    )
																					self.assertIsInstance(model.get_input_embeddings()   ,     (nn.Module)    )
																					__snake_case		:      int										=       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(lowerCamelCase   ,     nn.Linear    )    )

							def    __snake_case (							self	:  List[str]    )     ->   Optional[int]:
														__snake_case			,      __snake_case		:      Dict										=       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case		:      Dict										=       model_class(lowerCamelCase    )
																					__snake_case		:      Union[str, Any]										=       inspect.signature(model.forward    )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					__snake_case		:      List[Any]										=       [*signature.parameters.keys()]

																					__snake_case		:      Optional[Any]										=       ["pixel_values"]
																					self.assertListEqual(arg_names[:1]   ,     lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   Optional[Any]:
														__snake_case		:      Any										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   Any:
														__snake_case		:      Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Any    )     ->   str:
														# make masks reproducible
														np.random.seed(2    )

														__snake_case		:      List[Any]										=       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2    )
														__snake_case		:      List[Any]										=       np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
														__snake_case		:      Any										=       torch.from_numpy(lowerCamelCase    )

														# Add `noise` argument.
														# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
														__snake_case		:      Optional[int]										=       pt_noise

														super().check_pt_tf_models(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   Optional[Any]:
														__snake_case			,      __snake_case		:      Any										=       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case		:      Union[str, Any]										=       model_class(lowerCamelCase    )
																					model.to(lowerCamelCase    )
																					model.eval()
																					# make random mask reproducible
																					torch.manual_seed(2    )
																					with torch.no_grad():
																												__snake_case		:      str										=       model(**self._prepare_for_class(lowerCamelCase   ,     lowerCamelCase    )    )

																					__snake_case		:      List[Any]										=       outputs[0].cpu().numpy()
																					__snake_case		:      Union[str, Any]										=       0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(lowerCamelCase    )
																												__snake_case		:      List[Any]										=       model_class.from_pretrained(lowerCamelCase    )
																												model.to(lowerCamelCase    )
																												# make random mask reproducible
																												torch.manual_seed(2    )
																												with torch.no_grad():
																																			__snake_case		:      str										=       model(**self._prepare_for_class(lowerCamelCase   ,     lowerCamelCase    )    )

																												# Make sure we don't have nans
																												__snake_case		:      Any										=       after_outputs[0].cpu().numpy()
																												__snake_case		:      Tuple										=       0
																												__snake_case		:      List[str]										=       np.amax(np.abs(out_a - out_a    )    )
																												self.assertLessEqual(lowerCamelCase   ,     1E-5    )

							@unittest.skip(
							    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."    )
							def    __snake_case (							self	:  Optional[Any]    )     ->   Optional[int]:
														pass

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

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

							@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"    )
							def    __snake_case (							self	:  Dict    )     ->   Dict:
														pass

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
							def    __snake_case (							self	:  List[str]    )     ->   Any:
														pass






							@slow
							def    __snake_case (							self	:  int    )     ->   str:
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case		:      List[Any]										=       ViTMAEModel.from_pretrained(lowerCamelCase    )
																					self.assertIsNotNone(lowerCamelCase    )







def     lowerCAmelCase_					( ):
							__snake_case		:      Tuple										=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							return image

@require_torch
@require_vision
class     a		(unittest.TestCase				):




							"""simple docstring"""


							@cached_property
							def    __snake_case (							self	:  List[Any]    )     ->   List[Any]:
														return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"    ) if is_vision_available() else None






							@slow
							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														# make random mask reproducible across the PT and TF model
														np.random.seed(2    )

														__snake_case		:      Optional[int]										=       ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"    ).to(lowerCamelCase    )

														__snake_case		:      List[Any]										=       self.default_image_processor
														__snake_case		:      str										=       prepare_img()
														__snake_case		:      List[str]										=       image_processor(images=lowerCamelCase   ,     return_tensors="pt"    ).to(lowerCamelCase    )

														# 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)
														__snake_case		:      Tuple										=       ViTMAEConfig()
														__snake_case		:      Any										=       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
														__snake_case		:      Optional[Any]										=       np.random.uniform(size=(1, num_patches)    )

														# forward pass
														with torch.no_grad():
																					__snake_case		:      int										=       model(**lowerCamelCase   ,     noise=torch.from_numpy(lowerCamelCase    ).to(device=lowerCamelCase    )    )

														# verify the logits
														__snake_case		:      Tuple										=       torch.Size((1, 196, 768)    )
														self.assertEqual(outputs.logits.shape   ,     lowerCamelCase    )

														__snake_case		:      Any										=       torch.tensor(
														    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]    )

														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,     expected_slice.to(lowerCamelCase    )   ,     atol=1E-4    )    )





import numpy

class     a		:




							"""simple docstring"""


							def __init__(							self	:  str   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  numpy.ndarray    )     ->   None:
														__snake_case		:      Any										=       input_array

														# Random initial weights are assigned where first argument is the
														# number of nodes in previous layer and second argument is the
														# number of nodes in the next layer.

														# Random initial weights are assigned.
														# self.input_array.shape[1] is used to represent number of nodes in input layer.
														# First hidden layer consists of 4 nodes.
														__snake_case		:      int										=       numpy.random.rand(
														    self.input_array.shape[1]   ,     4    )

														# Random initial values for the first hidden layer.
														# First hidden layer has 4 nodes.
														# Second hidden layer has 3 nodes.
														__snake_case		:      Optional[int]										=       numpy.random.rand(
														    4   ,     3    )

														# Random initial values for the second hidden layer.
														# Second hidden layer has 3 nodes.
														# Output layer has 1 node.
														__snake_case		:      int										=       numpy.random.rand(3   ,     1    )

														# Real output values provided.
														__snake_case		:      Optional[Any]										=       output_array

														# Predicted output values by the neural network.
														# Predicted_output array initially consists of zeroes.
														__snake_case		:      Optional[int]										=       numpy.zeros(output_array.shape    )

							def    __snake_case (							self	:  List[Any]    )     ->   numpy.ndarray:
														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.input_array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
														# connecting the first hidden set of nodes with the second hidden set of nodes.
														__snake_case		:      str										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														# layer_between_second_hidden_layer_and_output is the layer connecting
														# second hidden layer with the output node.
														__snake_case		:      str										=       sigmoid(
														    numpy.dot(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer   ,     self.second_hidden_layer_and_output_layer_weights   ,     )    )

														return self.layer_between_second_hidden_layer_and_output

							def    __snake_case (							self	:  Union[str, Any]    )     ->   None:
														__snake_case		:      Optional[int]										=       numpy.dot(
														    self.layer_between_first_hidden_layer_and_second_hidden_layer.T   ,     2
														    * (self.output_array - self.predicted_output)
														    * sigmoid_derivative(self.predicted_output    )   ,     )
														__snake_case		:      Dict										=       numpy.dot(
														    self.layer_between_input_and_first_hidden_layer.T   ,     numpy.dot(
														        2
														        * (self.output_array - self.predicted_output)
														        * sigmoid_derivative(self.predicted_output    )   ,     self.second_hidden_layer_and_output_layer_weights.T   ,     )
														    * sigmoid_derivative(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer    )   ,     )
														__snake_case		:      Optional[Any]										=       numpy.dot(
														    self.input_array.T   ,     numpy.dot(
														        numpy.dot(
														            2
														            * (self.output_array - self.predicted_output)
														            * sigmoid_derivative(self.predicted_output    )   ,     self.second_hidden_layer_and_output_layer_weights.T   ,     )
														        * sigmoid_derivative(
														            self.layer_between_first_hidden_layer_and_second_hidden_layer    )   ,     self.first_hidden_layer_and_second_hidden_layer_weights.T   ,     )
														    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer    )   ,     )

														self.input_layer_and_first_hidden_layer_weights += (
														    updated_input_layer_and_first_hidden_layer_weights
														)
														self.first_hidden_layer_and_second_hidden_layer_weights += (
														    updated_first_hidden_layer_and_second_hidden_layer_weights
														)
														self.second_hidden_layer_and_output_layer_weights += (
														    updated_second_hidden_layer_and_output_layer_weights
														)

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  bool    )     ->   None:
														for iteration in range(1   ,     iterations + 1    ):
																					__snake_case		:      Any										=       self.feedforward()
																					self.back_propagation()
																					if give_loss:
																												__snake_case		:      str										=       numpy.mean(numpy.square(output - self.feedforward()    )    )
																												print(F'Iteration {iteration} Loss: {loss}'    )






							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  numpy.ndarray    )     ->   int:
														__snake_case		:      Any										=       input_arr

														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														__snake_case		:      List[Any]										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														__snake_case		:      Any										=       sigmoid(
														    numpy.dot(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer   ,     self.second_hidden_layer_and_output_layer_weights   ,     )    )

														return int(self.layer_between_second_hidden_layer_and_output > 0.6    )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return 1 / (1 + numpy.exp(-value     ))







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return (value) * (1 - (value))







def     lowerCAmelCase_					( ):
							__snake_case		:      Dict										=       numpy.array(
							    (
							        [0, 0, 0],
							        [0, 0, 1],
							        [0, 1, 0],
							        [0, 1, 1],
							        [1, 0, 0],
							        [1, 0, 1],
							        [1, 1, 0],
							        [1, 1, 1],
							    )							,     dtype=numpy.floataa							,     )

							# True output values for the given input values.
							__snake_case		:      Union[str, Any]										=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,     dtype=numpy.floataa     )

							# Calling neural network class.
							__snake_case		:      int										=       TwoHiddenLayerNeuralNetwork(
							    input_array=__lowerCamelCase							,     output_array=__lowerCamelCase     )

							# Calling training function.
							# Set give_loss to True if you want to see loss in every iteration.
							neural_network.train(output=__lowerCamelCase							,     iterations=1_0							,     give_loss=__lowerCamelCase     )

							return neural_network.predict(numpy.array(([1, 1, 1])							,     dtype=numpy.floataa     )     )


if __name__ == "__main__":
			example()

