
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 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			Optional[Any]							,A :			int							,A :			List[Any]=3							,A :			Any=32							,A :			List[str]=3							,A :			List[str]=10							,A :			List[str]=[10, 20, 30, 40]							,A :			List[Any]=[1, 1, 2, 1]							,A :			Dict=True							,A :			List[str]=True							,A :			Optional[int]="relu"							,A :			List[Any]=3							,A :			int=None							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			image_size
					__A             =			num_channels
					__A             =			embeddings_size
					__A             =			hidden_sizes
					__A             =			depths
					__A             =			is_training
					__A             =			use_labels
					__A             =			hidden_act
					__A             =			num_labels
					__A             =			scope
					__A             =			len(A			)






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

					__A             =			self.get_config()

					return config, pixel_values






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					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 :			List[Any]							,A :			Dict							,A :			int			):
					__A             =			FlaxRegNetModel(config=A			)
					__A             =			model(A			)

					# 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 :			Dict							,A :			Optional[Any]							,A :			int			):
					__A             =			self.num_labels
					__A             =			FlaxRegNetForImageClassification(config=A			)
					__A             =			model(A			)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)			)







	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.prepare_config_and_inputs()
					__A      ,							__A             =			config_and_inputs
					__A             =			{"pixel_values": pixel_values}
					return config, inputs_dict





@require_flax
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	snake_case_    =					False
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			FlaxRegNetModelTester(self			)
					__A             =			ConfigTester(self							,config_class=A							,has_text_modality=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					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 :			Dict			):
					return






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A			)






	@unittest.skip(reason="RegNet does not use inputs_embeds"			)
	def     UpperCamelCase_  (	self :			List[str]			):
					pass






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






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

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

									__A             =			["pixel_values"]
									self.assertListEqual(arg_names[:1]							,A			)






	def     UpperCamelCase_  (	self :			str			):
					def check_hidden_states_output(A :			List[Any]							,A :			List[Any]							,A :			Any			):
									__A             =			model_class(A			)

									__A             =			model(**self._prepare_for_class(A							,A			)			)

									__A             =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									__A             =			self.model_tester.num_stages
									self.assertEqual(len(A			)							,expected_num_stages + 1			)

					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A             =			True
									check_hidden_states_output(A							,A							,A			)

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

									check_hidden_states_output(A							,A							,A			)







	def     UpperCamelCase_  (	self :			Any			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__			):
													__A             =			self._prepare_for_class(A							,A			)
													__A             =			model_class(A			)

													@jax.jit
													def model_jitted(A :			List[str]							,**A :			List[Any]			):
																	return model(pixel_values=A							,**A			)

													with self.subTest("JIT Enabled"			):
																	__A             =			model_jitted(**A			).to_tuple()

													with self.subTest("JIT Disabled"			):
																	with jax.disable_jit():
																					__A             =			model_jitted(**A			).to_tuple()

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





def 						UpperCAmelCase      (     )		->     List[str]:







				"""simple docstring"""



				__A             =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image





@require_flax
class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	@cached_property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"			) if is_vision_available() else None







	@slow
	def     UpperCamelCase_  (	self :			str			):
					__A             =			FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"			)

					__A             =			self.default_image_processor
					__A             =			prepare_img()
					__A             =			image_processor(images=A							,return_tensors="np"			)

					__A             =			model(**A			)

					# verify the logits
					__A             =			(1, 10_00)
					self.assertEqual(outputs.logits.shape							,A			)

					__A             =			jnp.array([-0.41_80, -1.50_51, -3.48_36]			)

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


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__A             =			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.
					__A             =			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.
					__A             =			numpy.random.rand(
					    4							,3			)

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

					# Real output values provided.
					__A             =			output_array

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






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			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.
					__A             =			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.
					__A             =			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     UpperCamelCase_  (	self :			str			):
					__A             =			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			)							,)
					__A             =			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			)							,)
					__A             =			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     UpperCamelCase_  (	self :			Tuple							,A :			numpy.ndarray							,A :			int							,A :			bool			):
					for iteration in range(1							,iterations + 1			):
									__A             =			self.feedforward()
									self.back_propagation()
									if give_loss:
													__A             =			numpy.mean(numpy.square(output - self.feedforward()			)			)
													print(f'''Iteration {iteration} Loss: {loss}'''			)







	def     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__A             =			input_arr

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

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

					__A             =			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      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			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.
				__A             =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,       dtype=numpy.floataa						)

				# Calling neural network class.
				__A             =			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=1_0		,       give_loss=a_						)

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


if __name__ == "__main__":
					example()
