



import numpy
class     a__					:






						def __init__(     self	,    _A	,    _A    ):




											"""simple docstring"""



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

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

											# Real output values provided.
											__lowerCAmelCase            = output_array

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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.
											__lowerCAmelCase            = 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.
											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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    )	,    )
											__lowerCAmelCase            = 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    )	,    )
											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = input_arr

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

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

											__lowerCAmelCase            = 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 			_a   (		SCREAMING_SNAKE_CASE_							:       numpy.ndarray  ):
					return 1 / (1 + numpy.exp(-value  ))







def 			_a   (		SCREAMING_SNAKE_CASE_							:       numpy.ndarray  ):
					return (value) * (1 - (value))







def 			_a   (		):
					__lowerCAmelCase            = 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.
					__lowerCAmelCase            = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,   dtype=numpy.floataa  )

					# Calling neural network class.
					__lowerCAmelCase            = TwoHiddenLayerNeuralNetwork(
					    input_array=SCREAMING_SNAKE_CASE_   ,   output_array=SCREAMING_SNAKE_CASE_  )

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

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


if __name__ == "__main__":
					example()










from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class     a__					:
						_a							:					List[str]      =					MBartConfig
						_a							:					Union[str, Any]      =					{}
						_a							:					Tuple      =					"""gelu"""






						def __init__(     self	,    _A	,    _A=1_3	,    _A=7	,    _A=True	,    _A=False	,    _A=9_9	,    _A=3_2	,    _A=2	,    _A=4	,    _A=3_7	,    _A=0.1	,    _A=0.1	,    _A=2_0	,    _A=2	,    _A=1	,    _A=0	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = parent
											__lowerCAmelCase            = batch_size
											__lowerCAmelCase            = seq_length
											__lowerCAmelCase            = is_training
											__lowerCAmelCase            = use_labels
											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = hidden_size
											__lowerCAmelCase            = num_hidden_layers
											__lowerCAmelCase            = num_attention_heads
											__lowerCAmelCase            = intermediate_size
											__lowerCAmelCase            = hidden_dropout_prob
											__lowerCAmelCase            = attention_probs_dropout_prob
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = eos_token_id
											__lowerCAmelCase            = pad_token_id
											__lowerCAmelCase            = bos_token_id






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size    )
											__lowerCAmelCase            = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )	,    1    )
											__lowerCAmelCase            = tf.concat([input_ids, eos_tensor]	,    axis=1    )

											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size    )

											__lowerCAmelCase            = self.config_cls(
											    vocab_size=self.vocab_size	,    d_model=self.hidden_size	,    encoder_layers=self.num_hidden_layers	,    decoder_layers=self.num_hidden_layers	,    encoder_attention_heads=self.num_attention_heads	,    decoder_attention_heads=self.num_attention_heads	,    encoder_ffn_dim=self.intermediate_size	,    decoder_ffn_dim=self.intermediate_size	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    eos_token_ids=[2]	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    decoder_start_token_id=self.pad_token_id	,    **self.config_updates	,    )
											__lowerCAmelCase            = prepare_mbart_inputs_dict(_A	,    _A	,    _A    )
											return config, inputs_dict






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFMBartModel(config=_A    ).get_decoder()
											__lowerCAmelCase            = inputs_dict["input_ids"]

											__lowerCAmelCase            = input_ids[:1, :]
											__lowerCAmelCase            = inputs_dict["attention_mask"][:1, :]
											__lowerCAmelCase            = inputs_dict["head_mask"]
											__lowerCAmelCase            = 1

											# first forward pass
											__lowerCAmelCase            = model(_A	,    attention_mask=_A	,    head_mask=_A	,    use_cache=_A    )

											__lowerCAmelCase     ,							__lowerCAmelCase            = outputs.to_tuple()
											__lowerCAmelCase            = past_key_values[1]







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[int]   ,   SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       Any=None   ,   SCREAMING_SNAKE_CASE_							:       int=None   ,   SCREAMING_SNAKE_CASE_							:       List[Any]=None   ,   SCREAMING_SNAKE_CASE_							:       List[Any]=None   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=None   ,   ):
					if attention_mask is None:
										__lowerCAmelCase            = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_   ,   config.pad_token_id  )   ,   tf.inta  )
					if decoder_attention_mask is None:
										__lowerCAmelCase            = tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape   ,   dtype=tf.inta  ),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,   config.pad_token_id  )   ,   tf.inta  ),
										    ]   ,   axis=-1   ,   )
					if head_mask is None:
										__lowerCAmelCase            = tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										__lowerCAmelCase            = tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										__lowerCAmelCase            = tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}
@require_tf
class     a__					(      snake_case__				,       snake_case__				,       unittest.TestCase   ):
						_a							:					Optional[Any]      =					(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						_a							:					Any      =					(TFMBartForConditionalGeneration,) if is_tf_available() else ()
						_a							:					Tuple      =					(
						    {
						        """conversational""": TFMBartForConditionalGeneration,
						        """feature-extraction""": TFMBartModel,
						        """summarization""": TFMBartForConditionalGeneration,
						        """text2text-generation""": TFMBartForConditionalGeneration,
						        """translation""": TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:					Optional[int]      =					True
						_a							:					Optional[int]      =					False
						_a							:					List[Any]      =					False






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																# Exception encountered when calling layer '...'
																return True

											return False






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFMBartModelTester(self    )
											__lowerCAmelCase            = ConfigTester(self	,    config_class=_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*_A    )






@require_sentencepiece
@require_tokenizers
@require_tf
class     a__					(      unittest.TestCase   ):
						_a							:					Any      =					[
						    """ UN Chief Says There Is No Military Solution in Syria""",
						]
						_a							:					Optional[Any]      =					[
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						]
						_a							:					Any      =					"""facebook/mbart-large-en-ro"""






						@cached_property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return AutoTokenizer.from_pretrained(self.model_name    )






						@cached_property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
											return model






						def        __SCREAMING_SNAKE_CASE(     self	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.translate_src_text(**_A    )
											self.assertListEqual(self.expected_text	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.tokenizer(self.src_text	,    **_A	,    return_tensors="tf"    )
											__lowerCAmelCase            = self.model.generate(
											    model_inputs.input_ids	,    attention_mask=model_inputs.attention_mask	,    num_beams=2    )
											__lowerCAmelCase            = self.tokenizer.batch_decode(_A	,    skip_special_tokens=_A    )
											return generated_words






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self._assert_generated_batch_equal_expected()




