




import numpy





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	numpy.ndarray  ,      SCREAMING_SNAKE_CASE__	:	numpy.ndarray  ) ->			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   a (			self	:	Union[str, Any]  ) ->			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   a (			self	:	str  ) ->			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   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	numpy.ndarray  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	bool  ) ->			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   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	numpy.ndarray  ) ->			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 				_A  (		lowerCAmelCase_	:   numpy.ndarray     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   numpy.ndarray     ):
     """simple docstring"""

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



def 				_A  (		):
     """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=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=10  ,							give_loss=lowerCAmelCase_     )

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


if __name__ == "__main__":
     example()





import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     GPTSanJapaneseTokenizer
				A_   :     Optional[int]								=     False
				A_   :     List[str]								=     {'do_clean_text': False, 'add_prefix_space': False}


				def __UpperCamelCase						(							self      :  Tuple       )		->     Any:
											super().setUp()

											# fmt: off
											A      						=    ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
											# fmt: on
											A      						=    {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['emoji_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
											with open(self.emoji_file						, 'w'       ) as emoji_writer:
																		emoji_writer.write(json.dumps(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Any						, **__UpperCamelCase      :  Any       )		->     Tuple:
											kwargs.update(self.special_tokens_map       )
											return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  Dict       )		->     List[str]:
											A      						=    'こんにちは、世界。 \nこんばんは、㔺界。😀'
											A      						=    'こんにちは、世界。 \nこんばんは、世界。😀'
											return input_text, output_text


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  Optional[Any]       )		->     List[str]:
											A						,   A      						=    self.get_input_output_texts(__UpperCamelCase       )
											A      						=    tokenizer.encode(__UpperCamelCase						, add_special_tokens=__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase						, clean_up_tokenization_spaces=__UpperCamelCase       )
											return text, ids


				def __UpperCamelCase						(							self      :  Any       )		->     int:
											pass  # TODO add if relevant


				def __UpperCamelCase						(							self      :  List[str]       )		->     Tuple:
											pass  # TODO add if relevant


				def __UpperCamelCase						(							self      :  List[str]       )		->     List[Any]:
											pass  # TODO add if relevant


				def __UpperCamelCase						(							self      :  List[str]       )		->     int:
											A      						=    self.get_tokenizer()

											# Testing tokenization
											A      						=    'こんにちは、世界。　こんばんは、㔺界。'
											A      						=    ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
											A      						=    tokenizer.tokenize(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											# Testing conversion to ids without special tokens
											A      						=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
											A      						=    tokenizer.convert_tokens_to_ids(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											# Testing conversion to ids with special tokens
											A      						=    tokens + [tokenizer.unk_token]
											A      						=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
											A      						=    tokenizer.convert_tokens_to_ids(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											A      						=    self.get_tokenizer()

											# Testing tokenization
											A      						=    'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
											A      						=    'こんにちは、、、、世界。こんばんは、、、、世界。'
											A      						=    tokenizer.encode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											# Testing tokenization
											A      						=    'こんにちは、世界。'
											A      						=    'こんばんは、㔺界。😀'
											A      						=    'こんにちは、世界。こんばんは、世界。😀'
											A      						=    tokenizer.encode(prefix_text + input_text       )
											A      						=    tokenizer.encode(''						, prefix_text=prefix_text + input_text       )
											A      						=    tokenizer.encode(__UpperCamelCase						, prefix_text=__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											# Testing tokenization
											A      						=    'こんにちは、世界。'
											A      						=    'こんばんは、㔺界。😀'

											A      						=    len(tokenizer.encode(__UpperCamelCase       )       ) - 2
											A      						=    len(tokenizer.encode(__UpperCamelCase       )       ) - 2

											A      						=    [1] + [0] * (len_prefix + len_text + 1)
											A      						=    [1] * (len_prefix + len_text + 1) + [0]
											A      						=    [1] + [1] * (len_prefix) + [0] * (len_text + 1)

											A      						=    tokenizer(prefix_text + input_text       ).token_type_ids
											A      						=    tokenizer(''						, prefix_text=prefix_text + input_text       ).token_type_ids
											A      						=    tokenizer(__UpperCamelCase						, prefix_text=__UpperCamelCase       ).token_type_ids
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											A      						=    tokenizer.encode('あンいワ'       )
											A      						=    tokenizer.encode(''						, prefix_text='あンいワ'       )
											A      						=    tokenizer.encode('いワ'						, prefix_text='あン'       )

											self.assertEqual(tokenizer.decode(__UpperCamelCase       )						, tokenizer.decode(__UpperCamelCase       )       )
											self.assertEqual(tokenizer.decode(__UpperCamelCase       )						, tokenizer.decode(__UpperCamelCase       )       )
											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(x_token_a[1]						, x_token_a[-1]       )  # SEG token
											self.assertEqual(x_token_a[1]						, x_token_a[3]       )  # SEG token


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     List[Any]:
											A      						=    self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'       )

											A      						=    [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
											A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase       )
											A      						=    tokenizer.batch_encode_plus(__UpperCamelCase						, padding=__UpperCamelCase       )

											# fmt: off
											A      						=    [[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
											A      						=    [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
											A      						=    [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
											# fmt: on
											self.assertListEqual(x_token.input_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token.token_type_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token.attention_mask						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.input_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.token_type_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.attention_mask						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											# Intentionally convert some words to accommodate character fluctuations unique to Japanese
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     int:
											# tokenizer has no padding token
											pass