


from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata


__a     =			""""""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):






	class   __lowercase				(  tr.AbstractTransform  ):





							def __init__(		self						:					Any					,       __lowerCamelCase						:					str = " "       )    ->   List[Any]:






										"""simple docstring"""







										UpperCAmelCase							=     sentence_delimiter





							def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					str       )    ->   int:






										"""simple docstring"""







										return list(__lowerCamelCase       )





							def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					List[str]       )    ->   List[str]:






										"""simple docstring"""







										UpperCAmelCase							=     []
										for sent_idx, sentence in enumerate(__lowerCamelCase       ):
													chars.extend(self.process_string(__lowerCamelCase       )       )
													if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowerCamelCase       ) - 1:
																chars.append(self.sentence_delimiter       )
										return chars

	__a     =			tr.Compose(
	    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
	)
else:
	__a     =			tr.Compose(
	    [
	        tr.RemoveMultipleSpaces(),
	        tr.Strip(),
	        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
	        tr.ReduceToListOfListOfChars(),
	    ]
	)


__a     =			"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

__a     =			"""\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""

__a     =			"""
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,					_KWARGS_DESCRIPTION  )
class   __lowercase				(  datasets.Metric  ):





						def        _lowercase				(		self						:					Tuple       )    ->   int:






									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION					,       citation=_CITATION					,       inputs_description=_KWARGS_DESCRIPTION					,       features=datasets.Features(
									        {
									            """predictions""": datasets.Value("""string"""					,       id="""sequence"""       ),
									            """references""": datasets.Value("""string"""					,       id="""sequence"""       ),
									        }       )					,       codebase_urls=["""https://github.com/jitsi/jiwer/"""]					,       reference_urls=[
									        """https://en.wikipedia.org/wiki/Word_error_rate""",
									        """https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
									    ]					,       )





						def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Union[str, Any]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Optional[Any]=False       )    ->   List[str]:






									"""simple docstring"""







									if concatenate_texts:
												return jiwer.compute_measures(
												    __lowerCamelCase					,       __lowerCamelCase					,       truth_transform=__lowerCamelCase					,       hypothesis_transform=__lowerCamelCase					,       )["wer"]

									UpperCAmelCase							=     0
									UpperCAmelCase							=     0
									for prediction, reference in zip(__lowerCamelCase					,       __lowerCamelCase       ):
												UpperCAmelCase							=     jiwer.compute_measures(
												    __lowerCamelCase					,       __lowerCamelCase					,       truth_transform=__lowerCamelCase					,       hypothesis_transform=__lowerCamelCase					,       )
												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
												total += measures["substitutions"] + measures["deletions"] + measures["hits"]

									return incorrect / total







import numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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							=     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							=     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							=     numpy.random.rand(3					,       1       )

         # Real output values provided.
         UpperCAmelCase							=     output_array

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





      def        _lowercase				(		self						:					List[str]       )    ->   numpy.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     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							=     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							=     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						:					Optional[Any]       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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							=     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							=     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						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   None:






         """simple docstring"""







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





      def        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     input_arr

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

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

         UpperCAmelCase							=     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				(						lowerCAmelCase_   )						->numpy.ndarray:
   return 1 / (1 + numpy.exp(-value   ))
def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return (value) * (1 - (value))
def   _UpperCamelCase				(						)						->int:
   UpperCAmelCase							=     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							=     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,						dtype=numpy.floataa   )

   # Calling neural network class.
   UpperCAmelCase							=     TwoHiddenLayerNeuralNetwork(
       input_array=lowerCAmelCase_  ,						output_array=lowerCAmelCase_   )

   # Calling training function.
   # Set give_loss to True if you want to see loss in every iteration.
   neural_network.train(output=lowerCAmelCase_  ,						iterations=1_0  ,						give_loss=lowerCAmelCase_   )

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


if __name__ == "__main__":
 example()



