
"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowercase				:				Optional[int]   		=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

_lowercase				:				Any   		=		list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowercase				:				Union[str, Any]   		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			with open(snake_case_							,	'''rb'''			) as f:
						lowerCamelCase__	:	List[Any]				=Image.open(snake_case_			)
						return im.convert('''RGB'''			)

@dataclass
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							field(
					    default=UpperCamelCase_	,     metadata={
					        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
					    }	,     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}     )
					_a         =							field(default=UpperCamelCase_	,     metadata={'help': 'A folder containing the training data.'}     )
					_a         =							field(default=UpperCamelCase_	,     metadata={'help': 'A folder containing the validation data.'}     )
					_a         =							field(
					    default=0.15	,     metadata={'help': 'Percent to split off of train for validation.'}     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }	,     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
					            'value if set.'
					        )
					    }	,     )



					def 	snake_case ( self							:						Dict  )->      Tuple:
								if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
											raise ValueError(
											    '''You must specify either a dataset name from the hub or a train and/or validation directory.'''  )





@dataclass
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							field(
					    default='google/vit-base-patch16-224-in21k'	,     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}	,     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCamelCase_     )}	,     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}     )
					_a         =							field(
					    default='main'	,     metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,     )
					_a         =							field(default=UpperCamelCase_	,     metadata={'help': 'Name or path of preprocessor config.'}     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }	,     )
					_a         =							field(
					    default=UpperCamelCase_	,     metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}	,     )






def   snake_case__     (	__lowerCamelCase       :					Any			):
			"""simple docstring"""
			lowerCamelCase__	:	List[str]				=torch.stack([example['''pixel_values'''] for example in examples]			)
			lowerCamelCase__	:	List[Any]				=torch.tensor([example['''labels'''] for example in examples]			)
			return {"pixel_values": pixel_values, "labels": labels}






def   snake_case__     (	):
			"""simple docstring"""
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			lowerCamelCase__	:	Tuple				=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
			if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						lowerCamelCase__	:	Optional[int]				=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
			else:
						lowerCamelCase__	:	Optional[int]				=parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry('''run_image_classification'''							,	snake_case_							,	snake_case_			)

			# Setup logging
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''							,	datefmt='''%m/%d/%Y %H:%M:%S'''							,	handlers=[logging.StreamHandler(sys.stdout			)]							,	)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			lowerCamelCase__	:	Tuple				=training_args.get_process_log_level()
			logger.setLevel(snake_case_			)
			transformers.utils.logging.set_verbosity(snake_case_			)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
			    + f'''distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'''			)
			logger.info(f'''Training/evaluation parameters {training_args}'''			)

			# Detecting last checkpoint.
			lowerCamelCase__	:	int				=None
			if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
						lowerCamelCase__	:	int				=get_last_checkpoint(training_args.output_dir			)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
									raise ValueError(
									    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
									    '''Use --overwrite_output_dir to overcome.'''			)
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
									    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''			)

									 # Set seed before initializing model.
			set_seed(training_args.seed			)

			# Initialize our dataset and prepare it for the 'image-classification' task.
			if data_args.dataset_name is not None:
						lowerCamelCase__	:	List[str]				=load_dataset(
						    data_args.dataset_name							,	data_args.dataset_config_name							,	cache_dir=model_args.cache_dir							,	task='''image-classification'''							,	use_auth_token=True if model_args.use_auth_token else None							,	)
			else:
						lowerCamelCase__	:	Any				={}
						if data_args.train_dir is not None:
									lowerCamelCase__	:	str				=os.path.join(data_args.train_dir							,	'''**'''			)
						if data_args.validation_dir is not None:
									lowerCamelCase__	:	Tuple				=os.path.join(data_args.validation_dir							,	'''**'''			)
						lowerCamelCase__	:	int				=load_dataset(
						    '''imagefolder'''							,	data_files=snake_case_							,	cache_dir=model_args.cache_dir							,	task='''image-classification'''							,	)

			# If we don't have a validation split, split off a percentage of train as validation.
			lowerCamelCase__	:	Tuple				=None if '''validation''' in dataset.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split							,	snake_case_			) and data_args.train_val_split > 0.0:
						lowerCamelCase__	:	Optional[int]				=dataset['''train'''].train_test_split(data_args.train_val_split			)
						lowerCamelCase__	:	List[Any]				=split['''train''']
						lowerCamelCase__	:	List[Any]				=split['''test''']

			# Prepare label mappings.
			# We'll include these in the model's config to get human readable labels in the Inference API.
			lowerCamelCase__	:	int				=dataset['''train'''].features['''labels'''].names
			lowerCamelCase__	:	List[str]				={}, {}
			for i, label in enumerate(snake_case_			):
						lowerCamelCase__	:	Optional[Any]				=str(snake_case_			)
						lowerCamelCase__	:	Optional[int]				=label

			# Load the accuracy metric from the datasets package
			lowerCamelCase__	:	List[Any]				=evaluate.load('''accuracy'''			)

			# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(__lowerCamelCase       :					Tuple			):
						return metric.compute(predictions=np.argmax(p.predictions							,	axis=1			)							,	references=p.label_ids			)

			lowerCamelCase__	:	int				=AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path							,	num_labels=len(snake_case_			)							,	labelaid=snake_case_							,	idalabel=snake_case_							,	finetuning_task='''image-classification'''							,	cache_dir=model_args.cache_dir							,	revision=model_args.model_revision							,	use_auth_token=True if model_args.use_auth_token else None							,	)
			lowerCamelCase__	:	int				=AutoModelForImageClassification.from_pretrained(
			    model_args.model_name_or_path							,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)							,	config=snake_case_							,	cache_dir=model_args.cache_dir							,	revision=model_args.model_revision							,	use_auth_token=True if model_args.use_auth_token else None							,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,	)
			lowerCamelCase__	:	Optional[int]				=AutoImageProcessor.from_pretrained(
			    model_args.image_processor_name or model_args.model_name_or_path							,	cache_dir=model_args.cache_dir							,	revision=model_args.model_revision							,	use_auth_token=True if model_args.use_auth_token else None							,	)

			# Define torchvision transforms to be applied to each image.
			if "shortest_edge" in image_processor.size:
						lowerCamelCase__	:	Optional[Any]				=image_processor.size['''shortest_edge''']
			else:
						lowerCamelCase__	:	Optional[Any]				=(image_processor.size['''height'''], image_processor.size['''width'''])
			lowerCamelCase__	:	int				=Normalize(mean=image_processor.image_mean							,	std=image_processor.image_std			)
			lowerCamelCase__	:	Tuple				=Compose(
			    [
			        RandomResizedCrop(snake_case_			),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]			)
			lowerCamelCase__	:	Union[str, Any]				=Compose(
			    [
			        Resize(snake_case_			),
			        CenterCrop(snake_case_			),
			        ToTensor(),
			        normalize,
			    ]			)

			def train_transforms(__lowerCamelCase       :					Any			):
						lowerCamelCase__	:	Any				=[
						    _train_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch['''image''']
						]
						return example_batch

			def val_transforms(__lowerCamelCase       :					int			):
						lowerCamelCase__	:	int				=[_val_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch['''image''']]
						return example_batch

			if training_args.do_train:
						if "train" not in dataset:
									raise ValueError('''--do_train requires a train dataset'''			)
						if data_args.max_train_samples is not None:
									lowerCamelCase__	:	Optional[Any]				=(
									    dataset['''train'''].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
									)
						# Set the training transforms
						dataset["train"].set_transform(snake_case_			)

			if training_args.do_eval:
						if "validation" not in dataset:
									raise ValueError('''--do_eval requires a validation dataset'''			)
						if data_args.max_eval_samples is not None:
									lowerCamelCase__	:	List[str]				=(
									    dataset['''validation'''].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
									)
						# Set the validation transforms
						dataset["validation"].set_transform(snake_case_			)

			# Initalize our trainer
			lowerCamelCase__	:	Optional[Any]				=Trainer(
			    model=snake_case_							,	args=snake_case_							,	train_dataset=dataset['''train'''] if training_args.do_train else None							,	eval_dataset=dataset['''validation'''] if training_args.do_eval else None							,	compute_metrics=snake_case_							,	tokenizer=snake_case_							,	data_collator=snake_case_							,	)

			# Training
			if training_args.do_train:
						lowerCamelCase__	:	List[str]				=None
						if training_args.resume_from_checkpoint is not None:
									lowerCamelCase__	:	str				=training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									lowerCamelCase__	:	List[Any]				=last_checkpoint
						lowerCamelCase__	:	str				=trainer.train(resume_from_checkpoint=snake_case_			)
						trainer.save_model()
						trainer.log_metrics('''train'''							,	train_result.metrics			)
						trainer.save_metrics('''train'''							,	train_result.metrics			)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						lowerCamelCase__	:	Optional[int]				=trainer.evaluate()
						trainer.log_metrics('''eval'''							,	snake_case_			)
						trainer.save_metrics('''eval'''							,	snake_case_			)

			# Write model card and (optionally) push to hub
			lowerCamelCase__	:	List[Any]				={
			    '''finetuned_from''': model_args.model_name_or_path,
			    '''tasks''': '''image-classification''',
			    '''dataset''': data_args.dataset_name,
			    '''tags''': ['''image-classification''', '''vision'''],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**snake_case_			)
			else:
						trainer.create_model_card(**snake_case_			)


if __name__ == "__main__":
		main()









"""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,
								    },						)




