


import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
	from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor






class   __lowercase				(  __snake_case  ):





						def        _lowercase				(		self						:					Optional[Any]       )    ->   List[str]:






									"""simple docstring"""







									UpperCAmelCase							=     self.config_class(**self.inputs_dict       )
									self.parent.assertTrue(hasattr(__lowerCamelCase					,       """tf_padding"""       )       )
									self.parent.assertTrue(hasattr(__lowerCamelCase					,       """depth_multiplier"""       )       )






class   __lowercase				:





						def __init__(		self						:					str					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					List[str]=1_3					,       __lowerCamelCase						:					str=3					,       __lowerCamelCase						:					Union[str, Any]=3_2					,       __lowerCamelCase						:					List[Any]=0.25					,       __lowerCamelCase						:					Union[str, Any]=8					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					str=1_0_2_4					,       __lowerCamelCase						:					Any=3_2					,       __lowerCamelCase						:					Optional[int]="relu6"					,       __lowerCamelCase						:					int=0.1					,       __lowerCamelCase						:					str=0.02					,       __lowerCamelCase						:					Any=True					,       __lowerCamelCase						:					int=True					,       __lowerCamelCase						:					Union[str, Any]=1_0					,       __lowerCamelCase						:					Union[str, Any]=None					,       )    ->   Tuple:






									"""simple docstring"""







									UpperCAmelCase							=     parent
									UpperCAmelCase							=     batch_size
									UpperCAmelCase							=     num_channels
									UpperCAmelCase							=     image_size
									UpperCAmelCase							=     depth_multiplier
									UpperCAmelCase							=     min_depth
									UpperCAmelCase							=     tf_padding
									UpperCAmelCase							=     int(last_hidden_size * depth_multiplier       )
									UpperCAmelCase							=     output_stride
									UpperCAmelCase							=     hidden_act
									UpperCAmelCase							=     classifier_dropout_prob
									UpperCAmelCase							=     use_labels
									UpperCAmelCase							=     is_training
									UpperCAmelCase							=     num_labels
									UpperCAmelCase							=     initializer_range
									UpperCAmelCase							=     scope





						def        _lowercase				(		self						:					Tuple       )    ->   Any:






									"""simple docstring"""







									UpperCAmelCase							=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

									UpperCAmelCase							=     None
									UpperCAmelCase							=     None
									if self.use_labels:
												UpperCAmelCase							=     ids_tensor([self.batch_size]					,       self.num_labels       )
												UpperCAmelCase							=     ids_tensor([self.batch_size, self.image_size, self.image_size]					,       self.num_labels       )

									UpperCAmelCase							=     self.get_config()

									return config, pixel_values, labels, pixel_labels





						def        _lowercase				(		self						:					Tuple       )    ->   Optional[int]:






									"""simple docstring"""







									return MobileNetVaConfig(
									    num_channels=self.num_channels					,       image_size=self.image_size					,       depth_multiplier=self.depth_multiplier					,       min_depth=self.min_depth					,       tf_padding=self.tf_padding					,       hidden_act=self.hidden_act					,       classifier_dropout_prob=self.classifier_dropout_prob					,       initializer_range=self.initializer_range					,       )





						def        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					Tuple					,       __lowerCamelCase						:					List[Any]       )    ->   List[Any]:






									"""simple docstring"""







									UpperCAmelCase							=     MobileNetVaModel(config=__lowerCamelCase       )
									model.to(__lowerCamelCase       )
									model.eval()
									UpperCAmelCase							=     model(__lowerCamelCase       )
									self.parent.assertEqual(
									    result.last_hidden_state.shape					,       (
									        self.batch_size,
									        self.last_hidden_size,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )					,       )





						def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Union[str, Any]					,       __lowerCamelCase						:					int       )    ->   Union[str, Any]:






									"""simple docstring"""







									UpperCAmelCase							=     self.num_labels
									UpperCAmelCase							=     MobileNetVaForImageClassification(__lowerCamelCase       )
									model.to(__lowerCamelCase       )
									model.eval()
									UpperCAmelCase							=     model(__lowerCamelCase					,       labels=__lowerCamelCase       )
									self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)       )





						def        _lowercase				(		self						:					str       )    ->   Tuple:






									"""simple docstring"""







									UpperCAmelCase							=     self.prepare_config_and_inputs()
									UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     config_and_inputs
									UpperCAmelCase							=     {"""pixel_values""": pixel_values}
									return config, inputs_dict






@require_torch
class   __lowercase				(  __snake_case ,					__snake_case ,					unittest.TestCase  ):
						UpperCamelCase								=       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
						UpperCamelCase								=       (
						    {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase								=       False
						UpperCamelCase								=       False
						UpperCamelCase								=       False
						UpperCamelCase								=       False





						def        _lowercase				(		self						:					List[Any]       )    ->   Optional[int]:






									"""simple docstring"""







									UpperCAmelCase							=     MobileNetVaModelTester(self       )
									UpperCAmelCase							=     MobileNetVaConfigTester(self					,       config_class=__lowerCamelCase					,       has_text_modality=__lowerCamelCase       )





						def        _lowercase				(		self						:					Optional[int]       )    ->   Tuple:






									"""simple docstring"""







									self.config_tester.run_common_tests()





						@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""       )
						def        _lowercase				(		self						:					List[str]       )    ->   Optional[int]:






									"""simple docstring"""







									pass





						@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""       )
						def        _lowercase				(		self						:					Union[str, Any]       )    ->   Tuple:






									"""simple docstring"""







									pass





						@unittest.skip(reason="""MobileNetV1 does not output attentions"""       )
						def        _lowercase				(		self						:					List[Any]       )    ->   Optional[Any]:






									"""simple docstring"""







									pass





						def        _lowercase				(		self						:					Dict       )    ->   Optional[int]:






									"""simple docstring"""







									UpperCAmelCase				,  UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCAmelCase							=     ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]					,       __lowerCamelCase       )





						def        _lowercase				(		self						:					Any       )    ->   Union[str, Any]:






									"""simple docstring"""







									UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase       )





						def        _lowercase				(		self						:					Dict       )    ->   str:






									"""simple docstring"""







									def check_hidden_states_output(__lowerCamelCase						:					Union[str, Any]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Tuple       ):
												UpperCAmelCase							=     model_class(__lowerCamelCase       )
												model.to(__lowerCamelCase       )
												model.eval()

												with torch.no_grad():
															UpperCAmelCase							=     model(**self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase       )       )

												UpperCAmelCase							=     outputs.hidden_states

												UpperCAmelCase							=     2_6
												self.assertEqual(len(__lowerCamelCase       )					,       __lowerCamelCase       )

									UpperCAmelCase				,  UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												UpperCAmelCase							=     True
												check_hidden_states_output(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCAmelCase							=     True

												check_hidden_states_output(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )





						def        _lowercase				(		self						:					int       )    ->   Union[str, Any]:






									"""simple docstring"""







									UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase       )





						@slow
						def        _lowercase				(		self						:					Union[str, Any]       )    ->   Tuple:






									"""simple docstring"""







									for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase							=     MobileNetVaModel.from_pretrained(__lowerCamelCase       )
												self.assertIsNotNone(__lowerCamelCase       )

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







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





						@cached_property
						def        _lowercase				(		self						:					int       )    ->   Optional[int]:






									"""simple docstring"""







									return (
									    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""       ) if is_vision_available() else None
									)





						@slow
						def        _lowercase				(		self						:					Union[str, Any]       )    ->   Optional[int]:






									"""simple docstring"""







									UpperCAmelCase							=     MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""       ).to(__lowerCamelCase       )

									UpperCAmelCase							=     self.default_image_processor
									UpperCAmelCase							=     prepare_img()
									UpperCAmelCase							=     image_processor(images=__lowerCamelCase					,       return_tensors="""pt"""       ).to(__lowerCamelCase       )

									# forward pass
									with torch.no_grad():
												UpperCAmelCase							=     model(**__lowerCamelCase       )

									# verify the logits
									UpperCAmelCase							=     torch.Size((1, 1_0_0_1)       )
									self.assertEqual(outputs.logits.shape					,       __lowerCamelCase       )

									UpperCAmelCase							=     torch.tensor([-4.1_739, -1.1_233, 3.1_205]       ).to(__lowerCamelCase       )

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







import numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     numpy.random.rand(
             4					,       3       )

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

         # Real output values provided.
         UpperCAmelCase							=     output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         UpperCAmelCase							=     numpy.zeros(output_array.shape       )





      def        _lowercase				(		self						:					List[str]       )    ->   numpy.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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        _lowercase				(		self						:					Optional[Any]       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   None:






         """simple docstring"""







         for iteration in range(1					,       iterations + 1       ):
            UpperCAmelCase							=     self.feedforward()
            self.back_propagation()
            if give_loss:
               UpperCAmelCase							=     numpy.mean(numpy.square(output - self.feedforward()       )       )
               print(F"""Iteration {iteration} Loss: {loss}"""       )





      def        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     input_arr

         UpperCAmelCase							=     sigmoid(
             numpy.dot(self.array					,       self.input_layer_and_first_hidden_layer_weights       )       )

         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer					,       self.first_hidden_layer_and_second_hidden_layer_weights					,       )       )

         UpperCAmelCase							=     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   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return 1 / (1 + numpy.exp(-value   ))
def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return (value) * (1 - (value))
def   _UpperCamelCase				(						)						->int:
   UpperCAmelCase							=     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.
   UpperCAmelCase							=     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,						dtype=numpy.floataa   )

   # Calling neural network class.
   UpperCAmelCase							=     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()



