
"""simple docstring"""




from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
					import datasets



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				@slow
				@require_torch
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""     , """prajjwal1/bert-tiny"""					)
											SCREAMING_SNAKE_CASE__		: str     =     BertTokenizer.from_pretrained("""bert-base-uncased"""					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     bertabert.config.encoder.vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     tokenizer.sep_token_id
											SCREAMING_SNAKE_CASE__		: Tuple     =     tokenizer.cls_token_id
											SCREAMING_SNAKE_CASE__		: List[Any]     =     1_28

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     datasets.load_dataset("""cnn_dailymail"""     , """3.0.0"""     , split="""train[:1%]"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     datasets.load_dataset("""cnn_dailymail"""     , """3.0.0"""     , split="""validation[:1%]"""					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     train_dataset.select(range(32					)					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     val_dataset.select(range(16					)					)

											SCREAMING_SNAKE_CASE__		: int     =     4

											def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE__					):
																		# Tokenizer will automatically set [BOS] <text> [EOS]
																		SCREAMING_SNAKE_CASE__		: Dict     =     tokenizer(batch["""article"""]     , padding="""max_length"""     , truncation=SCREAMING_SNAKE_CASE__     , max_length=5_12					)
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tokenizer(batch["""highlights"""]     , padding="""max_length"""     , truncation=SCREAMING_SNAKE_CASE__     , max_length=1_28					)
																		SCREAMING_SNAKE_CASE__		: int     =     inputs.input_ids
																		SCREAMING_SNAKE_CASE__		: Dict     =     inputs.attention_mask

																		SCREAMING_SNAKE_CASE__		: List[Any]     =     outputs.input_ids
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     outputs.input_ids.copy()
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																		    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
																		]
																		SCREAMING_SNAKE_CASE__		: int     =     outputs.attention_mask

																		assert all(len(SCREAMING_SNAKE_CASE__					) == 5_12 for x in inputs.input_ids					)
																		assert all(len(SCREAMING_SNAKE_CASE__					) == 1_28 for x in outputs.input_ids					)

																		return batch

											def _compute_metrics(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     pred.label_ids
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     pred.predictions

																		# all unnecessary tokens are removed
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     , skip_special_tokens=SCREAMING_SNAKE_CASE__					)
																		SCREAMING_SNAKE_CASE__		: Tuple     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     , skip_special_tokens=SCREAMING_SNAKE_CASE__					)

																		SCREAMING_SNAKE_CASE__		: int     =     sum([int(pred_str[i] == label_str[i]					) for i in range(len(SCREAMING_SNAKE_CASE__					)					)]					) / len(SCREAMING_SNAKE_CASE__					)

																		return {"accuracy": accuracy}

											# map train dataset
											SCREAMING_SNAKE_CASE__		: List[str]     =     train_dataset.map(
											    _map_to_encoder_decoder_inputs     , batched=SCREAMING_SNAKE_CASE__     , batch_size=SCREAMING_SNAKE_CASE__     , remove_columns=["""article""", """highlights"""]     , )
											train_dataset.set_format(
											    type="""torch"""     , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]     , )

											# same for validation dataset
											SCREAMING_SNAKE_CASE__		: Tuple     =     val_dataset.map(
											    _map_to_encoder_decoder_inputs     , batched=SCREAMING_SNAKE_CASE__     , batch_size=SCREAMING_SNAKE_CASE__     , remove_columns=["""article""", """highlights"""]     , )
											val_dataset.set_format(
											    type="""torch"""     , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]     , )

											SCREAMING_SNAKE_CASE__		: Tuple     =     self.get_auto_remove_tmp_dir()

											SCREAMING_SNAKE_CASE__		: Tuple     =     SeqaSeqTrainingArguments(
											    output_dir=SCREAMING_SNAKE_CASE__     , per_device_train_batch_size=SCREAMING_SNAKE_CASE__     , per_device_eval_batch_size=SCREAMING_SNAKE_CASE__     , predict_with_generate=SCREAMING_SNAKE_CASE__     , evaluation_strategy="""steps"""     , do_train=SCREAMING_SNAKE_CASE__     , do_eval=SCREAMING_SNAKE_CASE__     , warmup_steps=0     , eval_steps=2     , logging_steps=2     , )

											# instantiate trainer
											SCREAMING_SNAKE_CASE__		: Any     =     SeqaSeqTrainer(
											    model=SCREAMING_SNAKE_CASE__     , args=SCREAMING_SNAKE_CASE__     , compute_metrics=_compute_metrics     , train_dataset=SCREAMING_SNAKE_CASE__     , eval_dataset=SCREAMING_SNAKE_CASE__     , tokenizer=SCREAMING_SNAKE_CASE__     , )

											# start training
											trainer.train()






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__   :					Union[str, Any]												=				_LazyModule(__name__, globals()['__file__'], _import_structure)




