

from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.xglm.modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						)



@require_tf
class   __lowercase    :




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					XGLMConfig
			_UpperCAmelCase       :							Any      							=					{}
			_UpperCAmelCase       :							Any      							=					'''gelu'''



			def __init__(			self  :					str       ,						lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[Any]=14       ,						lowerCAmelCase__  :					str=7       ,						lowerCAmelCase__  :					Dict=True       ,						lowerCAmelCase__  :					Union[str, Any]=True       ,						lowerCAmelCase__  :					List[str]=True       ,						lowerCAmelCase__  :					List[str]=99       ,						lowerCAmelCase__  :					Optional[int]=32       ,						lowerCAmelCase__  :					Tuple=2       ,						lowerCAmelCase__  :					int=4       ,						lowerCAmelCase__  :					List[Any]=37       ,						lowerCAmelCase__  :					Union[str, Any]="gelu"       ,						lowerCAmelCase__  :					Optional[int]=0.1       ,						lowerCAmelCase__  :					Tuple=0.1       ,						lowerCAmelCase__  :					Dict=512       ,						lowerCAmelCase__  :					Union[str, Any]=0.02       ,						):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	parent
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	batch_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	seq_length
									SCREAMING_SNAKE_CASE_:			List[str]  		=	is_training
									SCREAMING_SNAKE_CASE_:			Tuple  		=	use_input_mask
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	use_labels
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	d_model
									SCREAMING_SNAKE_CASE_:			Dict  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			int  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ffn_dim
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	activation_function
									SCREAMING_SNAKE_CASE_:			str  		=	activation_dropout
									SCREAMING_SNAKE_CASE_:			Dict  		=	attention_dropout
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			str  		=	None
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	0
									SCREAMING_SNAKE_CASE_:			int  		=	2
									SCREAMING_SNAKE_CASE_:			Tuple  		=	1



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									return XGLMConfig.from_pretrained("facebook/xglm-564M")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tf.clip_by_value(
									    ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size)       ,						clip_value_min=0       ,						clip_value_max=3)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	None
									if self.use_input_mask:
															SCREAMING_SNAKE_CASE_:			Tuple  		=	random_attention_mask([self.batch_size, self.seq_length])

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.get_config()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	floats_tensor([self.num_hidden_layers, self.num_attention_heads]       ,						2)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									return XGLMConfig(
									    vocab_size=self.vocab_size       ,						d_model=self.hidden_size       ,						num_layers=self.num_hidden_layers       ,						attention_heads=self.num_attention_heads       ,						ffn_dim=self.ffn_dim       ,						activation_function=self.activation_function       ,						activation_dropout=self.activation_dropout       ,						attention_dropout=self.attention_dropout       ,						max_position_embeddings=self.max_position_embeddings       ,						initializer_range=self.initializer_range       ,						use_cache=lowerCAmelCase__       ,						bos_token_id=self.bos_token_id       ,						eos_token_id=self.eos_token_id       ,						pad_token_id=self.pad_token_id       ,						return_dict=lowerCAmelCase__       ,						)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.prepare_config_and_inputs()

									(
									    (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   (
									    SCREAMING_SNAKE_CASE_
									)		,   
									):			Optional[Any]  		=	config_and_inputs

									SCREAMING_SNAKE_CASE_:			Any  		=	{
									    "input_ids": input_ids,
									    "head_mask": head_mask,
									}

									return config, inputs_dict







@require_tf
class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[Any]      							=					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			_UpperCAmelCase       :							Dict      							=					(TFXGLMForCausalLM,) if is_tf_available() else ()
			_UpperCAmelCase       :							Any      							=					(
			    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
			)
			_UpperCAmelCase       :							Optional[int]      							=					False
			_UpperCAmelCase       :							str      							=					False
			_UpperCAmelCase       :							Optional[int]      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			str  		=	TFXGLMModelTester(self)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ConfigTester(self       ,						config_class=lowerCAmelCase__       ,						n_embd=37)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									self.config_tester.run_common_tests()



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	TFXGLMModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)




			@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									super().test_resize_token_embeddings()







@require_tf
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					List[Any]=True):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tf.convert_to_tensor([[2, 268, 9865]]       ,						dtype=tf.intaa)  # The dog
									# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
									# fmt: off
									SCREAMING_SNAKE_CASE_:			int  		=	[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
									# fmt: on
									SCREAMING_SNAKE_CASE_:			List[str]  		=	model.generate(lowerCAmelCase__       ,						do_sample=lowerCAmelCase__       ,						num_beams=1)
									if verify_outputs:
															self.assertListEqual(output_ids[0].numpy().tolist()       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			int  		=	XGLMTokenizer.from_pretrained("facebook/xglm-564M")
									SCREAMING_SNAKE_CASE_:			Dict  		=	TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")

									tf.random.set_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer("Today is a nice day and"       ,						return_tensors="tf")
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenized.input_ids
									# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
									with tf.device(":/CPU:0"):
															SCREAMING_SNAKE_CASE_:			Dict  		=	model.generate(lowerCAmelCase__       ,						do_sample=lowerCAmelCase__       ,						seed=[7, 0])
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer.decode(output_ids[0]       ,						skip_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	(
									    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
									)
									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	XGLMTokenizer.from_pretrained("facebook/xglm-564M")

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	"left"

									# use different length sentences to test batching
									SCREAMING_SNAKE_CASE_:			str  		=	[
									    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
									    "left-padding, such as in batched generation. The output for the sequence below should be the same "
									    "regardless of whether left padding is applied or not. When",
									    "Hello, my dog is a little",
									]

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer(lowerCAmelCase__       ,						return_tensors="tf"       ,						padding=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	inputs["input_ids"]

									SCREAMING_SNAKE_CASE_:			int  		=	model.generate(input_ids=lowerCAmelCase__       ,						attention_mask=inputs["attention_mask"]       ,						max_new_tokens=12)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(sentences[0]       ,						return_tensors="tf").input_ids
									SCREAMING_SNAKE_CASE_:			List[str]  		=	model.generate(input_ids=lowerCAmelCase__       ,						max_new_tokens=12)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer(sentences[1]       ,						return_tensors="tf").input_ids
									SCREAMING_SNAKE_CASE_:			Tuple  		=	model.generate(input_ids=lowerCAmelCase__       ,						max_new_tokens=12)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.batch_decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.decode(output_non_padded[0]       ,						skip_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(output_padded[0]       ,						skip_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	[
									    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
									    "left-padding, such as in batched generation. The output for the sequence below should be the same "
									    "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
									    "a single",
									    "Hello, my dog is a little bit of a shy one, but he is very friendly",
									]
									self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__       ,						[non_padded_sentence, padded_sentence])









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,
									    }       ,						)






