





"""simple docstring"""


import numpy






class 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			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				:		Optional[Any]				   =			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				:		Union[str, Any]				   =			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				:		Dict				   =			numpy.random.rand(3						,   1    )

									# Real output values provided.
									UpperCamelCase				:		Dict				   =			output_array

									# Predicted output values by the neural network.
									# Predicted_output array initially consists of zeroes.
									UpperCamelCase				:		Optional[Any]				   =			numpy.zeros(output_array.shape    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			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				:		Optional[int]				   =			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				:		Any				   =			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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			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				:		List[str]				   =			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				:		List[str]				   =			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   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			input_arr

									UpperCamelCase				:		int				   =			sigmoid(
									    numpy.dot(self.array						,   self.input_layer_and_first_hidden_layer_weights    )    )

									UpperCamelCase				:		str				   =			sigmoid(
									    numpy.dot(
									        self.layer_between_input_and_first_hidden_layer						,   self.first_hidden_layer_and_second_hidden_layer_weights						,   )    )

									UpperCamelCase				:		int				   =			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							(SCREAMING_SNAKE_CASE     ):
						return 1 / (1 + numpy.exp(-value     ))

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return (value) * (1 - (value))

def      UpperCamelCase							():
						UpperCamelCase				:		str				   =			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				:		Optional[int]				   =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,     dtype=numpy.floataa     )

						# Calling neural network class.
						UpperCamelCase				:		Dict				   =			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()











"""simple docstring"""





from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


__lowerCamelCase							=  logging.get_logger(__name__)



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Any					)			->							List[Any]:
											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Dict							=       [label.strip() for label in labels.split(""","""					) if label.strip()]
											return labels






					def __call__(			self  :      Optional[Any]     ,		__snake_case  :      str     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[Any]					)			->							int:
											if len(__snake_case					) == 0 or len(__snake_case					) == 0:
																	raise ValueError("""You must include at least one label and at least one sequence."""					)
											if hypothesis_template.format(labels[0]					) == hypothesis_template:
																	raise ValueError(
																	    (
																	        """The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. """
																	        """Make sure the passed template includes formatting syntax such as {{}} where the label should go."""
																	    ).format(__snake_case					)					)

											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Any							=       [sequences]

											__magic_name__:	List[Any]							=       []
											for sequence in sequences:
																	sequence_pairs.extend([[sequence, hypothesis_template.format(__snake_case					)] for label in labels]					)

											return sequence_pairs, sequences



@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Dict     ,		__snake_case  :      Dict=ZeroShotClassificationArgumentHandler()     ,		*__snake_case  :      Dict     ,		**__snake_case  :      Optional[int]					)			->							int:
											__magic_name__:	List[str]							=       args_parser
											super().__init__(*__snake_case     ,		**__snake_case					)
											if self.entailment_id == -1:
																	logger.warning(
																	    """Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to """
																	    """-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."""					)

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											for label, ind in self.model.config.labelaid.items():
																	if label.lower().startswith("""entail"""					):
																							return ind
											return -1

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Tuple     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      Tuple=True     ,		__snake_case  :      Tuple=TruncationStrategy.ONLY_FIRST     ,		**__snake_case  :      Union[str, Any]					)			->							int:
											__magic_name__:	Optional[int]							=       self.framework
											if self.tokenizer.pad_token is None:
																	# Override for tokenizers not supporting padding
																	logger.error(
																	    """Tokenizer was not supporting padding necessary for zero-shot, attempting to use """
																	    """ `pad_token=eos_token`"""					)
																	__magic_name__:	Tuple							=       self.tokenizer.eos_token
											try:
																	__magic_name__:	List[Any]							=       self.tokenizer(
																	    __snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		)
											except Exception as e:
																	if "too short" in str(__snake_case					):
																							# tokenizers might yell that we want to truncate
																							# to a value that is not even reached by the input.
																							# In that case we don't want to truncate.
																							# It seems there's not a really better way to catch that
																							# exception.

																							__magic_name__:	List[str]							=       self.tokenizer(
																							    __snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		padding=__snake_case     ,		truncation=TruncationStrategy.DO_NOT_TRUNCATE     ,		)
																	else:
																							raise e

											return inputs

					def   lowerCamelCase__   (			self  :      Any     ,		**__snake_case  :      Optional[Any]					)			->							int:
											if kwargs.get("""multi_class"""     ,		__snake_case					) is not None:
																	__magic_name__:	List[str]							=       kwargs["""multi_class"""]
																	logger.warning(
																	    """The `multi_class` argument has been deprecated and renamed to `multi_label`. """
																	    """`multi_class` will be removed in a future version of Transformers."""					)
											__magic_name__:	Optional[Any]							=       {}
											if "candidate_labels" in kwargs:
																	__magic_name__:	str							=       self._args_parser._parse_labels(kwargs["""candidate_labels"""]					)
											if "hypothesis_template" in kwargs:
																	__magic_name__:	Optional[Any]							=       kwargs["""hypothesis_template"""]

											__magic_name__:	int							=       {}
											if "multi_label" in kwargs:
																	__magic_name__:	int							=       kwargs["""multi_label"""]
											return preprocess_params, {}, postprocess_params

					def __call__(			self  :      Any     ,		__snake_case  :      Union[str, List[str]]     ,		*__snake_case  :      Optional[int]     ,		**__snake_case  :      List[str]     ,		)			->							List[str]:
											if len(__snake_case					) == 0:
																	pass
											elif len(__snake_case					) == 1 and "candidate_labels" not in kwargs:
																	__magic_name__:	str							=       args[0]
											else:
																	raise ValueError(F'Unable to understand extra arguments {args}'					)

											return super().__call__(__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      int     ,		__snake_case  :      List[Any]=None     ,		__snake_case  :      Tuple="This example is {}."					)			->							List[Any]:
											__magic_name__,			__magic_name__:	int							=       self._args_parser(__snake_case     ,		__snake_case     ,		__snake_case					)

											for i, (candidate_label, sequence_pair) in enumerate(zip(__snake_case     ,		__snake_case					)					):
																	__magic_name__:	Dict							=       self._parse_and_tokenize([sequence_pair]					)

																	yield {
																	    "candidate_label": candidate_label,
																	    "sequence": sequences[0],
																	    "is_last": i == len(__snake_case					) - 1,
																	    **model_input,
																	}

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      int					)			->							Tuple:
											__magic_name__:	Optional[Any]							=       inputs["""candidate_label"""]
											__magic_name__:	List[str]							=       inputs["""sequence"""]
											__magic_name__:	Any							=       {k: inputs[k] for k in self.tokenizer.model_input_names}
											__magic_name__:	Optional[int]							=       self.model(**__snake_case					)

											__magic_name__:	Optional[Any]							=       {
											    """candidate_label""": candidate_label,
											    """sequence""": sequence,
											    """is_last""": inputs["""is_last"""],
											    **outputs,
											}
											return model_outputs






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      str     ,		__snake_case  :      List[Any]=False					)			->							Any:
											__magic_name__:	List[Any]							=       [outputs["""candidate_label"""] for outputs in model_outputs]
											__magic_name__:	Any							=       [outputs["""sequence"""] for outputs in model_outputs]
											__magic_name__:	Tuple							=       np.concatenate([output["""logits"""].numpy() for output in model_outputs]					)
											__magic_name__:	Any							=       logits.shape[0]
											__magic_name__:	Any							=       len(__snake_case					)
											__magic_name__:	List[str]							=       N // n
											__magic_name__:	int							=       logits.reshape((num_sequences, n, -1)					)

											if multi_label or len(__snake_case					) == 1:
																	# softmax over the entailment vs. contradiction dim for each label independently
																	__magic_name__:	List[Any]							=       self.entailment_id
																	__magic_name__:	List[str]							=       -1 if entailment_id == 0 else 0
																	__magic_name__:	Tuple							=       reshaped_outputs[..., [contradiction_id, entailment_id]]
																	__magic_name__:	int							=       np.exp(__snake_case					) / np.exp(__snake_case					).sum(-1     ,		keepdims=__snake_case					)
																	__magic_name__:	List[str]							=       scores[..., 1]
											else:
																	# softmax the "entailment" logits over all candidate labels
																	__magic_name__:	List[str]							=       reshaped_outputs[..., self.entailment_id]
																	__magic_name__:	Tuple							=       np.exp(__snake_case					) / np.exp(__snake_case					).sum(-1     ,		keepdims=__snake_case					)

											__magic_name__:	Optional[Any]							=       list(reversed(scores[0].argsort()					)					)
											return {
											    "sequence": sequences[0],
											    "labels": [candidate_labels[i] for i in top_inds],
											    "scores": scores[0, top_inds].tolist(),
											}



