
"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class A_	(unittest.TestCase      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=3				,   lowercase_=32				,   lowercase_=3				,   lowercase_=10				,   lowercase_=[10, 20, 30, 40]				,   lowercase_=[1, 1, 2, 1]				,   lowercase_=True				,   lowercase_=True				,   lowercase_="relu"				,   lowercase_=3				,   lowercase_=None				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    parent
     UpperCAmelCase_					:					Tuple			     =    batch_size
     UpperCAmelCase_					:					List[Any]			     =    image_size
     UpperCAmelCase_					:					Optional[int]			     =    num_channels
     UpperCAmelCase_					:					Union[str, Any]			     =    embeddings_size
     UpperCAmelCase_					:					str			     =    hidden_sizes
     UpperCAmelCase_					:					List[Any]			     =    depths
     UpperCAmelCase_					:					List[str]			     =    is_training
     UpperCAmelCase_					:					Optional[int]			     =    use_labels
     UpperCAmelCase_					:					List[str]			     =    hidden_act
     UpperCAmelCase_					:					Tuple			     =    num_labels
     UpperCAmelCase_					:					Dict			     =    scope
     UpperCAmelCase_					:					Tuple			     =    len(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     UpperCAmelCase_					:					List[str]			     =    self.get_config()

     return config, pixel_values







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return RegNetConfig(
         num_channels=self.num_channels				,   embeddings_size=self.embeddings_size				,   hidden_sizes=self.hidden_sizes				,   depths=self.depths				,   hidden_act=self.hidden_act				,   num_labels=self.num_labels				,   image_size=self.image_size				,   )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    FlaxRegNetModel(config=lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    model(lowercase_  )

     # Output shape (b, c, h, w)
     self.parent.assertEqual(
         result.last_hidden_state.shape				,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,   )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.num_labels
     UpperCAmelCase_					:					str			     =    FlaxRegNetForImageClassification(config=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.prepare_config_and_inputs()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    config_and_inputs
     UpperCAmelCase_					:					List[Any]			     =    {"pixel_values": pixel_values}
     return config, inputs_dict




@require_flax
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

  SCREAMING_SNAKE_CASE__			:						Any       =		False
  SCREAMING_SNAKE_CASE__			:						int       =		False
  SCREAMING_SNAKE_CASE__			:						Tuple       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    FlaxRegNetModelTester(self  )
     UpperCAmelCase_					:					str			     =    ConfigTester(self				,   config_class=lowercase_				,   has_text_modality=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_  )







  @unittest.skip(reason="RegNet does not use inputs_embeds"  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  @unittest.skip(reason="RegNet does not support input and output embeddings"  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					Union[str, Any]			     =    model_class(lowercase_  )
        UpperCAmelCase_					:					str			     =    inspect.signature(model.__call__  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCAmelCase_					:					Tuple			     =    [*signature.parameters.keys()]

        UpperCAmelCase_					:					List[Any]			     =    ["pixel_values"]
        self.assertListEqual(arg_names[:1]				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     def check_hidden_states_output(lowercase_				,   lowercase_				,   lowercase_  ):
        UpperCAmelCase_					:					Optional[int]			     =    model_class(lowercase_  )

        UpperCAmelCase_					:					List[str]			     =    model(**self._prepare_for_class(lowercase_				,   lowercase_  )  )

        UpperCAmelCase_					:					Tuple			     =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        UpperCAmelCase_					:					int			     =    self.model_tester.num_stages
        self.assertEqual(len(lowercase_  )				,   expected_num_stages + 1  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					int			     =    True
        check_hidden_states_output(lowercase_				,   lowercase_				,   lowercase_  )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCAmelCase_					:					Optional[Any]			     =    True

        check_hidden_states_output(lowercase_				,   lowercase_				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					Optional[int]			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
           UpperCAmelCase_					:					Dict			     =    model_class(lowercase_  )

           @jax.jit
           def model_jitted(lowercase_				,   **lowercase_  ):
              return model(pixel_values=lowercase_				,   **lowercase_  )

           with self.subTest("JIT Enabled"  ):
              UpperCAmelCase_					:					Dict			     =    model_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					int			     =    model_jitted(**lowercase_  ).to_tuple()

           self.assertEqual(len(lowercase_  )				,   len(lowercase_  )  )
           for jitted_output, output in zip(lowercase_				,   lowercase_  ):
              self.assertEqual(jitted_output.shape				,   output.shape  )







def 	__a     (		):
   UpperCAmelCase_					:					Optional[int]			     =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   return image


@require_flax
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  @cached_property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"  ) if is_vision_available() else None







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"  )

     UpperCAmelCase_					:					str			     =    self.default_image_processor
     UpperCAmelCase_					:					Optional[Any]			     =    prepare_img()
     UpperCAmelCase_					:					Union[str, Any]			     =    image_processor(images=lowercase_				,   return_tensors="np"  )

     UpperCAmelCase_					:					str			     =    model(**lowercase_  )

     # verify the logits
     UpperCAmelCase_					:					Tuple			     =    (1, 1000)
     self.assertEqual(outputs.logits.shape				,   lowercase_  )

     UpperCAmelCase_					:					List[str]			     =    jnp.array([-0.41_80, -1.50_51, -3.48_36]  )

     self.assertTrue(jnp.allclose(outputs.logits[0, :3]				,   lowercase_				,   atol=1E-4  )  )








import numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							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							):







	'''simple docstring'''





	return 1 / (1 + numpy.exp(-value							))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return (value) * (1 - (value))


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	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()
