



"""simple docstring"""






import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_lowercase				:				List[Any]   		=		logging.getLogger()






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=argparse.ArgumentParser()
			parser.add_argument('''-f'''			)
			lowerCamelCase__	:	Union[str, Any]				=parser.parse_args()
			return args.f






def   snake_case__     (	__lowerCamelCase       :					List[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	List[str]				={}
			lowerCamelCase__	:	Dict				=os.path.join(__lowerCamelCase							,	'''all_results.json'''			)
			if os.path.exists(__lowerCamelCase			):
						with open(__lowerCamelCase							,	'''r'''			) as f:
									lowerCamelCase__	:	Union[str, Any]				=json.load(__lowerCamelCase			)
			else:
						raise ValueError(f'''can\'t find {path}'''			)
			return results






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	str				=torch.cuda.is_available() and torch_device == '''cuda'''
			return is_using_cuda and is_apex_available()


_lowercase				:				Tuple   		=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					@classmethod
					def 	snake_case ( cls							:						Dict  )->      Any:
								# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
								lowerCamelCase__	:	Dict				=tempfile.mkdtemp()
								lowerCamelCase__	:	int				=os.path.join(cls.tmpdir,						'''default_config.yml'''  )
								write_basic_config(save_location=cls.configPath  )
								lowerCamelCase__	:	Tuple				=['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



					@classmethod
					def 	snake_case ( cls							:						Any  )->      int:
								shutil.rmtree(cls.tmpdir  )



					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						Dict  )->      Optional[int]:
								lowerCamelCase__	:	List[Any]				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	str				=F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

								if is_cuda_and_apex_available():
											testargs.append('''--fp16'''  )

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	List[str]				=get_results(lowerCamelCase  )
								self.assertGreaterEqual(result['''eval_accuracy'''],						0.75  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''epoch_0'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''glue_no_trainer'''  )  )  )



					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						str  )->      Tuple:
								lowerCamelCase__	:	Dict				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	int				=F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

								if torch.cuda.device_count() > 1:
											# Skipping because there are not enough batches to train the model + would need a drop_last to work.
											return

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	List[str]				=get_results(lowerCamelCase  )
								self.assertLess(result['''perplexity'''],						100  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''epoch_0'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''clm_no_trainer'''  )  )  )



					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Optional[int]				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	List[str]				=F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	List[Any]				=get_results(lowerCamelCase  )
								self.assertLess(result['''perplexity'''],						42  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''epoch_0'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''mlm_no_trainer'''  )  )  )



					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						Any  )->      Any:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								lowerCamelCase__	:	Tuple				=7 if get_gpu_count() > 1 else 2

								lowerCamelCase__	:	int				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	List[Any]				=F'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	Union[str, Any]				=get_results(lowerCamelCase  )
								self.assertGreaterEqual(result['''eval_accuracy'''],						0.75  )
								self.assertLess(result['''train_loss'''],						0.5  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''epoch_0'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''ner_no_trainer'''  )  )  )



					@unittest.skip(reason='''Fix me @muellerzr'''  )
					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						Union[str, Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	Any				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	Any				=F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	int				=get_results(lowerCamelCase  )
								# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
								self.assertGreaterEqual(result['''eval_f1'''],						28  )
								self.assertGreaterEqual(result['''eval_exact'''],						28  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''epoch_0'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''qa_no_trainer'''  )  )  )



					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						List[str]  )->      int:
								lowerCamelCase__	:	Optional[int]				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	Tuple				=F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	Any				=get_results(lowerCamelCase  )
								self.assertGreaterEqual(result['''eval_accuracy'''],						0.8  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''swag_no_trainer'''  )  )  )



					@slow
					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						Tuple  )->      List[Any]:
								lowerCamelCase__	:	Dict				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	str				=F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	str				=get_results(lowerCamelCase  )
								self.assertGreaterEqual(result['''eval_rouge1'''],						10  )
								self.assertGreaterEqual(result['''eval_rouge2'''],						2  )
								self.assertGreaterEqual(result['''eval_rougeL'''],						7  )
								self.assertGreaterEqual(result['''eval_rougeLsum'''],						7  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''epoch_0'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''summarization_no_trainer'''  )  )  )



					@slow
					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						int  )->      Optional[Any]:
								lowerCamelCase__	:	Optional[int]				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	Optional[Any]				=F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	Dict				=get_results(lowerCamelCase  )
								self.assertGreaterEqual(result['''eval_bleu'''],						30  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''epoch_0'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''translation_no_trainer'''  )  )  )



					@slow
					def 	snake_case ( self							:						Optional[int]  )->      Dict:
								lowerCamelCase__	:	Optional[Any]				=logging.StreamHandler(sys.stdout  )
								logger.addHandler(lowerCamelCase  )

								lowerCamelCase__	:	str				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	List[str]				=F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	str				=get_results(lowerCamelCase  )
								self.assertGreaterEqual(result['''eval_overall_accuracy'''],						0.10  )




					@mock.patch.dict(os.environ,						{'''WANDB_MODE''': '''offline'''}  )
					def 	snake_case ( self							:						int  )->      int:
								lowerCamelCase__	:	Dict				=self.get_auto_remove_tmp_dir()
								lowerCamelCase__	:	Optional[Any]				=F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

								if is_cuda_and_apex_available():
											testargs.append('''--fp16'''  )

								run_command(self._launch_args + testargs  )
								lowerCamelCase__	:	Tuple				=get_results(lowerCamelCase  )
								# The base model scores a 25%
								self.assertGreaterEqual(result['''eval_accuracy'''],						0.6  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''step_1'''  )  )  )
								self.assertTrue(os.path.exists(os.path.join(lowerCamelCase,						'''image_classification_no_trainer'''  )  )  )









"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


_lowercase				:				List[str]   		=		get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

_lowercase				:				List[str]   		=		2_5_0_0_0_4
_lowercase				:				Optional[Any]   		=		2_5_0_0_2_0

@require_sentencepiece
@require_tokenizers
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							MBartTokenizer
					_a         =							MBartTokenizerFast
					_a         =							True
					_a         =							True



					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__	:	Union[str, Any]				=MBartTokenizer(lowerCamelCase,						keep_accents=lowerCamelCase  )
								tokenizer.save_pretrained(self.tmpdirname  )



					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								lowerCamelCase__	:	Any				=MBartTokenizer(lowerCamelCase,						keep_accents=lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=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]],						)

								lowerCamelCase__	:	str				=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''',
								        '''.''',
								    ],						)
								lowerCamelCase__	:	Union[str, Any]				=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]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ],						)

								lowerCamelCase__	:	str				=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>''',
								        '''.''',
								    ],						)




					def 	snake_case ( self							:						Tuple  )->      List[Any]:
								if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

								lowerCamelCase__	:	int				=(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
														lowerCamelCase__	:	Union[str, Any]				=self.rust_tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )
														lowerCamelCase__	:	str				=self.tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )

														lowerCamelCase__	:	List[str]				=tempfile.mkdtemp()

														lowerCamelCase__	:	Union[str, Any]				=tokenizer_r.save_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Optional[int]				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# Checks it save with the same files + the tokenizer.json file for the fast one
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )
														lowerCamelCase__	:	List[str]				=tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f  )
														self.assertSequenceEqual(lowerCamelCase,						lowerCamelCase  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Any				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.from_pretrained(lowerCamelCase  )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,						lowerCamelCase  )  )
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(lowerCamelCase  )

														# Save tokenizer rust, legacy_format=True
														lowerCamelCase__	:	Dict				=tempfile.mkdtemp()

														lowerCamelCase__	:	List[str]				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Tuple				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# Checks it save with the same files
														self.assertSequenceEqual(lowerCamelCase,						lowerCamelCase  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Optional[int]				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Any				=tokenizer_p.from_pretrained(lowerCamelCase  )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,						lowerCamelCase  )  )

														shutil.rmtree(lowerCamelCase  )

														# Save tokenizer rust, legacy_format=False
														lowerCamelCase__	:	Optional[int]				=tempfile.mkdtemp()

														lowerCamelCase__	:	int				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# Checks it saved the tokenizer.json file
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Dict				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	int				=tokenizer_p.from_pretrained(lowerCamelCase  )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,						lowerCamelCase  )  )

														shutil.rmtree(lowerCamelCase  )





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


					'''simple docstring'''





					_a         =							'facebook/mbart-large-en-ro'
					_a         =							[
					    ' UN Chief Says There Is No Military Solution in Syria',
					    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
					]
					_a         =							[
					    'Şeful ONU declară că nu există o soluţie militară în Siria',
					    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
					    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
					    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
					]
					_a         =							[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]



					@classmethod
					def 	snake_case ( cls							:						List[Any]  )->      Optional[int]:
								lowerCamelCase__	:	MBartTokenizer				=MBartTokenizer.from_pretrained(
								    cls.checkpoint_name,						src_lang='''en_XX''',						tgt_lang='''ro_RO'''  )
								lowerCamelCase__	:	Optional[int]				=1
								return cls



					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''],						25_0001  )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''],						25_0004  )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''],						25_0020  )



					def 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	Union[str, Any]				=self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens,						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      str:
								self.assertIn(lowerCamelCase,						self.tokenizer.all_special_ids  )
								lowerCamelCase__	:	Optional[int]				=[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
								lowerCamelCase__	:	Any				=self.tokenizer.decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase  )
								lowerCamelCase__	:	str				=self.tokenizer.decode(generated_ids[1:],						skip_special_tokens=lowerCamelCase  )
								self.assertEqual(lowerCamelCase,						lowerCamelCase  )
								self.assertNotIn(self.tokenizer.eos_token,						lowerCamelCase  )



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Optional[int]				=['''this is gunna be a long sentence ''' * 20]
								assert isinstance(src_text[0],						lowerCamelCase  )
								lowerCamelCase__	:	Dict				=10
								lowerCamelCase__	:	Optional[int]				=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 	snake_case ( self							:						int  )->      Any:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']  ),						[25_0026, 25_0001]  )



					def 	snake_case ( self							:						Tuple  )->      Optional[Any]:
								lowerCamelCase__	:	int				=tempfile.mkdtemp()
								lowerCamelCase__	:	Optional[int]				=self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=MBartTokenizer.from_pretrained(lowerCamelCase  )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids,						lowerCamelCase  )



					@require_torch
					def 	snake_case ( self							:						Optional[Any]  )->      Tuple:
								lowerCamelCase__	:	Optional[Any]				=self.tokenizer(self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Dict				=shift_tokens_right(batch['''labels'''],						self.tokenizer.pad_token_id  )

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
								assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
								assert batch.decoder_input_ids[1][-1] == 2
								assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



					@require_torch
					def 	snake_case ( self							:						Optional[Any]  )->      Any:
								lowerCamelCase__	:	str				=self.tokenizer(
								    self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=len(self.expected_src_tokens  ),						return_tensors='''pt''',						)

								lowerCamelCase__	:	List[Any]				=shift_tokens_right(batch['''labels'''],						self.tokenizer.pad_token_id  )

								self.assertIsInstance(lowerCamelCase,						lowerCamelCase  )

								self.assertEqual((2, 14),						batch.input_ids.shape  )
								self.assertEqual((2, 14),						batch.attention_mask.shape  )
								lowerCamelCase__	:	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, EN_CODE]  )



					def 	snake_case ( self							:						List[Any]  )->      Dict:
								lowerCamelCase__	:	Any				=self.tokenizer(self.src_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=3,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Tuple				=self.tokenizer(
								    text_target=self.tgt_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=10,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Union[str, Any]				=targets['''input_ids''']
								lowerCamelCase__	:	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 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	str				=self.tokenizer._build_translation_inputs(
								    '''A test''',						return_tensors='''pt''',						src_lang='''en_XX''',						tgt_lang='''ar_AR'''  )

								self.assertEqual(
								    nested_simplify(lowerCamelCase  ),						{
								        # A, test, EOS, en_XX
								        '''input_ids''': [[62, 3034, 2, 25_0004]],
								        '''attention_mask''': [[1, 1, 1, 1]],
								        # ar_AR
								        '''forced_bos_token_id''': 25_0001,
								    },						)




