
'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class 			UpperCAmelCase__    :



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

          # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          _lowerCamelCase					: str		    = (image_size // patch_size) ** 2
          _lowerCamelCase					: Optional[Any]		    = num_patches + 1



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: List[Any]		    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

          _lowerCamelCase					: int		    = None
          if self.use_labels:
                _lowerCamelCase					: Dict		    = ids_tensor([self.batch_size],self.type_sequence_label_size  )

          _lowerCamelCase					: Tuple		    = self.get_config()

          return config, pixel_values, labels



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          return ViTConfig(
              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=__A,initializer_range=self.initializer_range,)



    def 	lowerCamelCase_	(						self   :							Any,__A   :							Any,__A   :							Dict,__A   :							Any  ):
          _lowerCamelCase					: int		    = TFViTModel(config=__A  )
          _lowerCamelCase					: Dict		    = model(__A,training=__A  )
          self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)  )

          # Test with an image with different size than the one specified in config.
          _lowerCamelCase					: Dict		    = self.image_size // 2
          _lowerCamelCase					: Any		    = pixel_values[:, :, :image_size, :image_size]
          _lowerCamelCase					: int		    = model(__A,interpolate_pos_encoding=__A,training=__A  )
          _lowerCamelCase					: int		    = (image_size // self.patch_size) ** 2 + 1
          self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, seq_length, self.hidden_size)  )



    def 	lowerCamelCase_	(						self   :							Union[str, Any],__A   :							Any,__A   :							List[str],__A   :							Optional[Any]  ):
          _lowerCamelCase					: Optional[int]		    = self.type_sequence_label_size
          _lowerCamelCase					: Dict		    = TFViTForImageClassification(__A  )
          _lowerCamelCase					: Any		    = model(__A,labels=__A,training=__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)  )

          # Test with an image with different size than the one specified in config.
          _lowerCamelCase					: Union[str, Any]		    = self.image_size // 2
          _lowerCamelCase					: int		    = pixel_values[:, :, :image_size, :image_size]
          _lowerCamelCase					: Any		    = model(__A,interpolate_pos_encoding=__A,training=__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)  )

          # test greyscale images
          _lowerCamelCase					: List[Any]		    = 1
          _lowerCamelCase					: Dict		    = TFViTForImageClassification(__A  )
          _lowerCamelCase					: int		    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
          _lowerCamelCase					: Any		    = model(__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)  )



    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: str		    = self.prepare_config_and_inputs()
          _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: Any		    = config_and_inputs
          _lowerCamelCase					: Any		    = {"pixel_values": pixel_values}
          return config, inputs_dict


@require_tf
class 			UpperCAmelCase__    (							A    ,						A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    lowerCAmelCase_					     =		(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: List[str]		    = TFViTModelTester(self  )
          _lowerCamelCase					: Tuple		    = ConfigTester(self,config_class=__A,has_text_modality=__A,hidden_size=3_7  )



    def 	lowerCamelCase_	(						self   :							int  ):
          self.config_tester.run_common_tests()



    @unittest.skip(reason="ViT does not use inputs_embeds"  )
    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          pass



    @unittest.skip(reason="ViT does not use inputs_embeds"  )
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          pass



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase    ,		_lowerCamelCase					: Optional[int]		    = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                _lowerCamelCase					: Optional[int]		    = model_class(__A  )
                self.assertIsInstance(model.get_input_embeddings(),(tf.keras.layers.Layer)  )
                _lowerCamelCase					: Optional[int]		    = model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(__A,tf.keras.layers.Layer  )  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase    ,		_lowerCamelCase					: Optional[Any]		    = self.model_tester.prepare_config_and_inputs_for_common()

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

                _lowerCamelCase					: Optional[int]		    = ["pixel_values"]
                self.assertListEqual(arg_names[:1],__A  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__A  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: List[str]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__A  )



    @slow
    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          _lowerCamelCase					: int		    = TFViTModel.from_pretrained("google/vit-base-patch16-224"  )
          self.assertIsNotNone(__A  )







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
      return image



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



    @cached_property
    def 	lowerCamelCase_	(						self   :							Tuple  ):
          return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"  ) if is_vision_available() else None



    @slow
    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Any		    = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"  )

          _lowerCamelCase					: Any		    = self.default_image_processor
          _lowerCamelCase					: Optional[Any]		    = prepare_img()
          _lowerCamelCase					: Dict		    = image_processor(images=__A,return_tensors="tf"  )

          # forward pass
          _lowerCamelCase					: Any		    = model(**__A  )

          # verify the logits
          _lowerCamelCase					: Union[str, Any]		    = tf.TensorShape((1, 1_0_0_0)  )
          self.assertEqual(outputs.logits.shape,__A  )

          _lowerCamelCase					: Optional[int]		    = tf.constant([-0.2744, 0.8215, -0.0836]  )

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





'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   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						 =   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						 =   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						 =   numpy.random.rand(3       ,		1      )

							# Real output values provided.
							__snake_case						 =   output_array

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




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   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						 =   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						 =   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     :  List[str]      ):
							__snake_case						 =   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						 =   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						 =   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     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  bool      ):
							for iteration in range(1       ,		iterations + 1      ):
													__snake_case						 =   self.feedforward()
													self.back_propagation()
													if give_loss:
																			__snake_case						 =   numpy.mean(numpy.square(output - self.feedforward()      )      )
																			print(F'Iteration {iteration} Loss: {loss}'      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

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

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

							__snake_case						 =   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__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   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						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   TwoHiddenLayerNeuralNetwork(
						    input_array=a    ,	output_array=a							)

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=a    ,	iterations=10    ,	give_loss=a							)

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


if __name__ == "__main__":
	example()

