

import numpy



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					int       ,						lowerCAmelCase__  :					numpy.ndarray       ,						lowerCAmelCase__  :					numpy.ndarray):
									SCREAMING_SNAKE_CASE_:			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.
									SCREAMING_SNAKE_CASE_:			List[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.
									SCREAMING_SNAKE_CASE_:			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.
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	numpy.random.rand(3       ,						1)

									# Real output values provided.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	output_array

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Dict  		=	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.
									SCREAMING_SNAKE_CASE_:			Any  		=	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.
									SCREAMING_SNAKE_CASE_:			List[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 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	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)       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	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)       ,						)
									SCREAMING_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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					numpy.ndarray       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					bool):
									for iteration in range(1       ,						iterations + 1):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.feedforward()
															self.back_propagation()
															if give_loss:
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	numpy.mean(numpy.square(output - self.feedforward()))
																					print(F"Iteration {iteration} Loss: {loss}")




			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					numpy.ndarray):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	input_arr

									SCREAMING_SNAKE_CASE_:			Dict  		=	sigmoid(
									    numpy.dot(self.array       ,						self.input_layer_and_first_hidden_layer_weights))

									SCREAMING_SNAKE_CASE_:			Any  		=	sigmoid(
									    numpy.dot(
									        self.layer_between_input_and_first_hidden_layer       ,						self.first_hidden_layer_and_second_hidden_layer_weights       ,						))

									SCREAMING_SNAKE_CASE_:			Optional[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       A_				(   _UpperCAmelCase   ):
						return 1 / (1 + numpy.exp(-value   ))







def       A_				(   _UpperCAmelCase   ):
						return (value) * (1 - (value))







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			int  		=	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.
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,     dtype=numpy.floataa   )

						# Calling neural network class.
						SCREAMING_SNAKE_CASE_:			Dict  		=	TwoHiddenLayerNeuralNetwork(
						    input_array=_UpperCAmelCase    ,     output_array=_UpperCAmelCase   )

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=_UpperCAmelCase    ,     iterations=10    ,     give_loss=_UpperCAmelCase   )

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


if __name__ == "__main__":
						example()









import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:  Any            =						get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
						from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowerCAmelCase							:  Optional[Any]            =						50003
lowerCAmelCase							:  List[str]            =						50002



@require_sentencepiece
@require_tokenizers
class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					PLBartTokenizer
			_UpperCAmelCase       :							Any      							=					None
			_UpperCAmelCase       :							Optional[Any]      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE_:			Tuple  		=	PLBartTokenizer(lowerCAmelCase__       ,						language_codes="base"       ,						keep_accents=lowerCAmelCase__)
									tokenizer.save_pretrained(self.tmpdirname)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	PLBartTokenizer(lowerCAmelCase__       ,						language_codes="base"       ,						keep_accents=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer.tokenize("This is a test")
									self.assertListEqual(lowerCAmelCase__       ,						["▁This", "▁is", "▁a", "▁t", "est"])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)       ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,						)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.tokenize("I was born in 92000, and this is falsé.")
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]       ,						)
									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]       ,						)

									SCREAMING_SNAKE_CASE_:			int  		=	tokenizer.vocab_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[tokenizer.convert_ids_to_tokens(lowerCAmelCase__) for x in range(end - 4       ,						lowerCAmelCase__)]

									self.assertListEqual(lowerCAmelCase__       ,						["__java__", "__python__", "__en_XX__", "<mask>"])

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer(lowerCAmelCase__).input_ids
									self.assertEqual(
									    tokenizer.decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__       ,						clean_up_tokenization_spaces=lowerCAmelCase__)       ,						lowerCAmelCase__       ,						)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	PLBartTokenizer(lowerCAmelCase__       ,						language_codes="multi"       ,						keep_accents=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer.tokenize("This is a test")
									self.assertListEqual(lowerCAmelCase__       ,						["▁This", "▁is", "▁a", "▁t", "est"])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)       ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,						)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.tokenize("I was born in 92000, and this is falsé.")
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__       ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer.vocab_size
									SCREAMING_SNAKE_CASE_:			int  		=	[tokenizer.convert_ids_to_tokens(lowerCAmelCase__) for x in range(end - 7       ,						lowerCAmelCase__)]

									self.assertListEqual(
									    lowerCAmelCase__       ,						["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"])
									SCREAMING_SNAKE_CASE_:			str  		=	"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(lowerCAmelCase__).input_ids
									self.assertEqual(
									    tokenizer.decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__       ,						clean_up_tokenization_spaces=lowerCAmelCase__)       ,						lowerCAmelCase__       ,						)







@require_torch
@require_sentencepiece
@require_tokenizers
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					'''uclanlp/plbart-python-en_XX'''
			_UpperCAmelCase       :							List[str]      							=					[
			    '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
			    '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
			]
			_UpperCAmelCase       :							int      							=					[
			    '''Returns the maximum value of a b c.''',
			    '''Sums the values of a b c.''',
			]
			_UpperCAmelCase       :							Optional[Any]      							=					[
			    134,
			    5452,
			    3_3460,
			    3_3441,
			    3_3463,
			    3_3465,
			    3_3463,
			    3_3449,
			    988,
			    20,
			    3_3456,
			    19,
			    3_3456,
			    771,
			    39,
			    4258,
			    889,
			    3318,
			    3_3441,
			    3_3463,
			    3_3465,
			    3_3463,
			    3_3449,
			    2471,
			    2,
			    PYTHON_CODE,
			]



			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (			cls  :					Dict):
									SCREAMING_SNAKE_CASE_:			PLBartTokenizer  		=	PLBartTokenizer.from_pretrained(
									    cls.checkpoint_name       ,						language_codes="base"       ,						src_lang="python"       ,						tgt_lang="en_XX")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	1
									return cls



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]       ,						5_0001)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]       ,						5_0002)
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]       ,						5_0003)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
									self.assertListEqual(self.expected_src_tokens       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									self.assertIn(lowerCAmelCase__       ,						self.tokenizer.all_special_ids)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
									SCREAMING_SNAKE_CASE_:			int  		=	self.tokenizer.decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.tokenizer.decode(generated_ids[1:]       ,						skip_special_tokens=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)
									self.assertNotIn(self.tokenizer.eos_token       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
									self.assertIsInstance(src_text[0]       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	10
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.tokenizer(lowerCAmelCase__       ,						max_length=lowerCAmelCase__       ,						truncation=lowerCAmelCase__).input_ids[0]
									self.assertEqual(ids[-2]       ,						2)
									self.assertEqual(ids[-1]       ,						lowerCAmelCase__)
									self.assertEqual(len(lowerCAmelCase__)       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"])       ,						[5_0004, 5_0001])



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	PLBartTokenizer.from_pretrained(lowerCAmelCase__)
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,						lowerCAmelCase__)



			@require_torch
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.tokenizer(self.src_text       ,						text_target=self.tgt_text       ,						padding=lowerCAmelCase__       ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_:			Any  		=	shift_tokens_right(batch["labels"]       ,						self.tokenizer.pad_token_id)

									# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
									self.assertEqual(batch.input_ids[1][-2:].tolist()       ,						[2, PYTHON_CODE])
									self.assertEqual(batch.decoder_input_ids[1][0]       ,						lowerCAmelCase__)
									self.assertEqual(batch.decoder_input_ids[1][-1]       ,						2)
									self.assertEqual(batch.labels[1][-2:].tolist()       ,						[2, EN_CODE])



			@require_torch
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.tokenizer(
									    self.src_text       ,						text_target=self.tgt_text       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__       ,						max_length=len(self.expected_src_tokens)       ,						return_tensors="pt"       ,						)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	shift_tokens_right(batch["labels"]       ,						self.tokenizer.pad_token_id)

									self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

									self.assertEqual((2, 26)       ,						batch.input_ids.shape)
									self.assertEqual((2, 26)       ,						batch.attention_mask.shape)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens       ,						lowerCAmelCase__)
									self.assertEqual(2       ,						batch.decoder_input_ids[0, -1])  # EOS
									# Test that special tokens are reset
									self.assertEqual(self.tokenizer.prefix_tokens       ,						[])
									self.assertEqual(self.tokenizer.suffix_tokens       ,						[self.tokenizer.eos_token_id, PYTHON_CODE])



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.tokenizer(self.src_text       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__       ,						max_length=3       ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_:			Any  		=	self.tokenizer(
									    text_target=self.tgt_text       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__       ,						max_length=10       ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	targets["input_ids"]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	shift_tokens_right(lowerCAmelCase__       ,						self.tokenizer.pad_token_id)

									self.assertEqual(batch.input_ids.shape[1]       ,						3)
									self.assertEqual(batch.decoder_input_ids.shape[1]       ,						10)




			@require_torch
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.tokenizer._build_translation_inputs(
									    "A test"       ,						return_tensors="pt"       ,						src_lang="en_XX"       ,						tgt_lang="java")

									self.assertEqual(
									    nested_simplify(lowerCAmelCase__)       ,						{
									        # A, test, EOS, en_XX
									        "input_ids": [[150, 242, 2, 5_0003]],
									        "attention_mask": [[1, 1, 1, 1]],
									        # java
									        "forced_bos_token_id": 5_0001,
									    }       ,						)






