
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class     a		(unittest.TestCase				):




							"""simple docstring"""


							def    __snake_case (							self	:  List[Any]    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       tempfile.mkdtemp()

														# fmt: off
														__snake_case		:      int										=       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
														# fmt: on
														__snake_case		:      Dict										=       os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["vocab_file"]    )
														with open(self.vocab_file   ,     "w"   ,     encoding="utf-8"    ) as vocab_writer:
																					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]    )    )

														__snake_case		:      Optional[Any]										=       {
														    "do_resize": True,
														    "size": {"height": 18, "width": 18},
														    "do_normalize": True,
														    "image_mean": [0.5, 0.5, 0.5],
														    "image_std": [0.5, 0.5, 0.5],
														}
														__snake_case		:      str										=       os.path.join(self.tmpdirname   ,     lowerCamelCase    )
														with open(self.image_processor_file   ,     "w"   ,     encoding="utf-8"    ) as fp:
																					json.dump(lowerCamelCase   ,     lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     **lowerCamelCase	:  List[Any]    )     ->   List[str]:
														return BertTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     **lowerCamelCase	:  int    )     ->   Optional[int]:
														return ViTImageProcessor.from_pretrained(self.tmpdirname   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														shutil.rmtree(self.tmpdirname    )

							def    __snake_case (							self	:  Optional[Any]    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       [np.random.randint(255   ,     size=(3, 30, 400)   ,     dtype=np.uinta    )]

														__snake_case		:      Optional[Any]										=       [Image.fromarray(np.moveaxis(lowerCamelCase   ,     0   ,     -1    )    ) for x in image_inputs]

														return image_inputs

							def    __snake_case (							self	:  Any    )     ->   str:
														__snake_case		:      Optional[Any]										=       self.get_tokenizer()
														__snake_case		:      str										=       self.get_image_processor()

														__snake_case		:      Any										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														processor.save_pretrained(self.tmpdirname    )
														__snake_case		:      List[Any]										=       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname    )

														self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()    )
														self.assertIsInstance(processor.tokenizer   ,     (BertTokenizer, BertTokenizerFast)    )

														self.assertEqual(processor.image_processor.to_json_string()   ,     image_processor.to_json_string()    )
														self.assertIsInstance(processor.image_processor   ,     lowerCamelCase    )

							def    __snake_case (							self	:  Optional[int]    )     ->   List[Any]:
														__snake_case		:      Optional[int]										=       VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor()    )
														processor.save_pretrained(self.tmpdirname    )

														__snake_case		:      Tuple										=       self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"    )
														__snake_case		:      Tuple										=       self.get_image_processor(do_normalize=lowerCamelCase   ,     padding_value=1.0    )

														__snake_case		:      List[Any]										=       VisionTextDualEncoderProcessor.from_pretrained(
														    self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=lowerCamelCase   ,     padding_value=1.0    )

														self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()    )
														self.assertIsInstance(processor.tokenizer   ,     (BertTokenizer, BertTokenizerFast)    )

														self.assertEqual(processor.image_processor.to_json_string()   ,     image_processor_add_kwargs.to_json_string()    )
														self.assertIsInstance(processor.image_processor   ,     lowerCamelCase    )

							def    __snake_case (							self	:  Dict    )     ->   List[Any]:
														__snake_case		:      Dict										=       self.get_image_processor()
														__snake_case		:      List[Any]										=       self.get_tokenizer()

														__snake_case		:      Union[str, Any]										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      List[str]										=       self.prepare_image_inputs()

														__snake_case		:      Dict										=       image_processor(lowerCamelCase   ,     return_tensors="np"    )
														__snake_case		:      Any										=       processor(images=lowerCamelCase   ,     return_tensors="np"    )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1E-2    )

							def    __snake_case (							self	:  Any    )     ->   Any:
														__snake_case		:      Tuple										=       self.get_image_processor()
														__snake_case		:      str										=       self.get_tokenizer()

														__snake_case		:      int										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      int										=       "lower newer"

														__snake_case		:      str										=       processor(text=lowerCamelCase    )

														__snake_case		:      Tuple										=       tokenizer(lowerCamelCase    )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]    )

							def    __snake_case (							self	:  Tuple    )     ->   Optional[int]:
														__snake_case		:      str										=       self.get_image_processor()
														__snake_case		:      List[str]										=       self.get_tokenizer()

														__snake_case		:      str										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      Dict										=       "lower newer"
														__snake_case		:      Dict										=       self.prepare_image_inputs()

														__snake_case		:      int										=       processor(text=lowerCamelCase   ,     images=lowerCamelCase    )

														self.assertListEqual(list(inputs.keys()    )   ,     ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]    )

														# test if it raises when no input is passed
														with self.assertRaises(lowerCamelCase    ):
																					processor()

							def    __snake_case (							self	:  Any    )     ->   Optional[Any]:
														__snake_case		:      List[Any]										=       self.get_image_processor()
														__snake_case		:      int										=       self.get_tokenizer()

														__snake_case		:      Optional[Any]										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      Any										=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														__snake_case		:      str										=       processor.batch_decode(lowerCamelCase    )
														__snake_case		:      Dict										=       tokenizer.batch_decode(lowerCamelCase    )

														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )






							def    __snake_case (							self	:  List[str]    )     ->   List[str]:
														__snake_case		:      Dict										=       self.get_image_processor()
														__snake_case		:      Any										=       self.get_tokenizer()

														__snake_case		:      Tuple										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       "lower newer"
														__snake_case		:      List[str]										=       self.prepare_image_inputs()

														__snake_case		:      Dict										=       processor(text=lowerCamelCase   ,     images=lowerCamelCase    )

														self.assertListEqual(list(inputs.keys()    )   ,     processor.model_input_names    )





import numpy

class     a		:




							"""simple docstring"""


							def __init__(							self	:  str   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  numpy.ndarray    )     ->   None:
														__snake_case		:      Any										=       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.
														__snake_case		:      int										=       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.
														__snake_case		:      Optional[int]										=       numpy.random.rand(
														    4   ,     3    )

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

														# Real output values provided.
														__snake_case		:      Optional[Any]										=       output_array

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

							def    __snake_case (							self	:  List[Any]    )     ->   numpy.ndarray:
														__snake_case		:      List[str]										=       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.
														__snake_case		:      str										=       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.
														__snake_case		:      str										=       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	:  Union[str, Any]    )     ->   None:
														__snake_case		:      Optional[int]										=       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    )   ,     )
														__snake_case		:      Dict										=       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    )   ,     )
														__snake_case		:      Optional[Any]										=       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	:  List[str]   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  bool    )     ->   None:
														for iteration in range(1   ,     iterations + 1    ):
																					__snake_case		:      Any										=       self.feedforward()
																					self.back_propagation()
																					if give_loss:
																												__snake_case		:      str										=       numpy.mean(numpy.square(output - self.feedforward()    )    )
																												print(F'Iteration {iteration} Loss: {loss}'    )






							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  numpy.ndarray    )     ->   int:
														__snake_case		:      Any										=       input_arr

														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														__snake_case		:      List[Any]										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														__snake_case		:      Any										=       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     lowerCAmelCase_					( __lowerCamelCase     ):
							return 1 / (1 + numpy.exp(-value     ))







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return (value) * (1 - (value))







def     lowerCAmelCase_					( ):
							__snake_case		:      Dict										=       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.
							__snake_case		:      Union[str, Any]										=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,     dtype=numpy.floataa     )

							# Calling neural network class.
							__snake_case		:      int										=       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()

