



'''simple docstring'''

import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase     		=   logging.get_logger(__name__)


UpperCAmelCase     		=   {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

UpperCAmelCase     		=   {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

UpperCAmelCase     		=   '</w>'
UpperCAmelCase     		=   '@@ '






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     set()
	lowerCAmelCase   =     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)							)
		lowerCAmelCase   =     char
	return pairs


# Speech2Text2 has no max input length
UpperCAmelCase     		=   {'facebook/s2t-wav2vec2-large-en-de': 1024}





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Tuple									=		VOCAB_FILES_NAMES
						UpperCAmelCase							:   Tuple									=		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:   Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase							:   Any									=		["input_ids", "attention_mask"]


						def __init__( self     ,	A_     ,	A_="<s>"     ,	A_="<pad>"     ,	A_="</s>"     ,	A_="<unk>"     ,	A_=False     ,	A_=None     ,	**A_     ,	)     ->     Tuple:
							super().__init__(
							    unk_token=A_     ,	bos_token=A_     ,	eos_token=A_     ,	pad_token=A_     ,	do_lower_case=A_     ,	**A_     ,	)

							lowerCAmelCase   =     do_lower_case

							with open(A_     ,	encoding="""utf-8"""				) as vocab_handle:
								lowerCAmelCase   =     json.load(A_				)
							lowerCAmelCase   =     {v: k for k, v in self.encoder.items()}

							if merges_file is None:
								logger.info(f'No merges files provided. {self.__class__.__name__} can only be used for decoding.'				)

								lowerCAmelCase   =     None
								lowerCAmelCase   =     None
							else:
								with open(A_     ,	encoding="""utf-8"""				) as merges_handle:
									lowerCAmelCase   =     merges_handle.read().split("""\n"""				)[:-1]

								lowerCAmelCase   =     [tuple(merge.split()[:2]				) for merge in merges]
								lowerCAmelCase   =     dict(zip(A_     ,	range(len(A_				)				)				)				)
								lowerCAmelCase   =     {}


						@property
						def     __snake_case   ( self				)     ->     int:
							return len(self.decoder				)


						def     __snake_case   ( self				)     ->     Dict:
							return dict(self.encoder     ,	**self.added_tokens_encoder				)


						def     __snake_case   ( self     ,	A_				)     ->     Optional[Any]:
							lowerCAmelCase   =     tuple(token[:-1]				) + (token[-1] + BPE_TOKEN_MERGES,)
							if token in self.cache:
								return self.cache[token]
							lowerCAmelCase   =     get_pairs(A_				)

							if not pairs:
								return token

							while True:
								lowerCAmelCase   =     min(A_     ,	key=lambda A_				: self.bpe_ranks.get(A_     ,	float("""inf"""				)				)				)
								if bigram not in self.bpe_ranks:
									break
								lowerCAmelCase,       lowerCAmelCase   =     bigram
								lowerCAmelCase   =     []
								lowerCAmelCase   =     0
								while i < len(A_				):
									try:
										lowerCAmelCase   =     word.index(A_     ,	A_				)
									except ValueError:
										new_word.extend(word[i:]				)
										break
									else:
										new_word.extend(word[i:j]				)
										lowerCAmelCase   =     j

									if word[i] == first and i < len(A_				) - 1 and word[i + 1] == second:
										new_word.append(first + second				)
										i += 2
									else:
										new_word.append(word[i]				)
										i += 1
								lowerCAmelCase   =     tuple(A_				)
								lowerCAmelCase   =     new_word
								if len(A_				) == 1:
									break
								else:
									lowerCAmelCase   =     get_pairs(A_				)
							lowerCAmelCase   =     """ """.join(A_				)
							if word == "\n  " + BPE_TOKEN_MERGES:
								lowerCAmelCase   =     """\n""" + BPE_TOKEN_MERGES

							if word.endswith(A_				):
								lowerCAmelCase   =     word.replace(A_     ,	""""""				)

							lowerCAmelCase   =     word.replace(""" """     ,	A_				)
							lowerCAmelCase   =     word
							return word


						def     __snake_case   ( self     ,	A_				)     ->     List[Any]:

							if self.bpe_ranks is None:
								raise ValueError(
								    """This tokenizer was instantiated without a `merges.txt` file, so"""
								    """ that it can only be used for decoding, not for encoding."""
								    """Make sure to provide `merges.txt` file at instantiation to enable """
								    """encoding."""				)

							if self.do_lower_case:
								lowerCAmelCase   =     text.lower()

							lowerCAmelCase   =     text.split()

							lowerCAmelCase   =     []
							for token in text:
								if token:
									split_tokens.extend(list(self.bpe(A_				).split(""" """				)				)				)

							return split_tokens


						def     __snake_case   ( self     ,	A_				)     ->     int:
							return self.encoder.get(A_     ,	self.encoder.get(self.unk_token				)				)


						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     self.decoder.get(A_     ,	self.unk_token				)
							return result


						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     """ """.join(A_				)

							# make sure @@ tokens are concatenated
							lowerCAmelCase   =     """""".join(string.split(A_				)				)

							return string




						def     __snake_case   ( self     ,	A_     ,	A_ = None				)     ->     Tuple[str]:
							if not os.path.isdir(A_				):
								logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
								return
							lowerCAmelCase   =     os.path.join(
							    A_     ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
							lowerCAmelCase   =     os.path.join(
							    A_     ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]				)

							with open(A_     ,	"""w"""     ,	encoding="""utf-8"""				) as f:
								f.write(json.dumps(self.encoder     ,	indent=2     ,	sort_keys=A_     ,	ensure_ascii=A_				) + """\n"""				)

							lowerCAmelCase   =     0
							if self.bpe_ranks is None:
								return (vocab_file,)

							with open(A_     ,	"""w"""     ,	encoding="""utf-8"""				) as writer:
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,	key=lambda A_				: kv[1]				):
									if index != token_index:
										logger.warning(
										    f'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
										    """ Please check that the tokenizer is not corrupted!"""				)
										lowerCAmelCase   =     token_index
									writer.write(""" """.join(A_				) + """\n"""				)
									index += 1

							return (vocab_file, merges_file)




'''simple docstring'''

import numpy

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_				)     ->     None:
							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     __snake_case   ( self				)     ->     numpy.ndarray:
							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     __snake_case   ( self				)     ->     None:
							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     None:
							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     __snake_case   ( self     ,	A_				)     ->     int:
							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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return 1 / (1 + numpy.exp(-value							))






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return (value) * (1 - (value))






def 		_snake_case		( )      ->						int:







	"""simple docstring"""
	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()