





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							(					__UpperCamelCase			):
 @slow
 @require_torch
 def      A_	(      self  :      Tuple	)			->	str:
     lowerCamelCase__    :      Optional[int]					 =		EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'			,     'prajjwal1/bert-tiny'	)
     lowerCamelCase__    :      List[Any]					 =		BertTokenizer.from_pretrained('bert-base-uncased'	)

     lowerCamelCase__    :      Tuple					 =		bertabert.config.encoder.vocab_size
     lowerCamelCase__    :      Optional[int]					 =		tokenizer.sep_token_id
     lowerCamelCase__    :      List[Any]					 =		tokenizer.cls_token_id
     lowerCamelCase__    :      str					 =		128

     lowerCamelCase__    :      Optional[Any]					 =		datasets.load_dataset('cnn_dailymail'			,     '3.0.0'			,     split='train[:1%]'	)
     lowerCamelCase__    :      Any					 =		datasets.load_dataset('cnn_dailymail'			,     '3.0.0'			,     split='validation[:1%]'	)

     lowerCamelCase__    :      List[Any]					 =		train_dataset.select(range(32	)	)
     lowerCamelCase__    :      Dict					 =		val_dataset.select(range(16	)	)

     lowerCamelCase__    :      str					 =		4

     def _map_to_encoder_decoder_inputs(UpperCAmelCase  :      int	):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         lowerCamelCase__    :      List[str]					 =		tokenizer(batch['article']			,     padding='max_length'			,     truncation=UpperCAmelCase			,     max_length=512	)
         lowerCamelCase__    :      Optional[Any]					 =		tokenizer(batch['highlights']			,     padding='max_length'			,     truncation=UpperCAmelCase			,     max_length=128	)
         lowerCamelCase__    :      int					 =		inputs.input_ids
         lowerCamelCase__    :      List[str]					 =		inputs.attention_mask

         lowerCamelCase__    :      Optional[int]					 =		outputs.input_ids
         lowerCamelCase__    :      Dict					 =		outputs.input_ids.copy()
         lowerCamelCase__    :      Union[str, Any]					 =		[
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
         ]
         lowerCamelCase__    :      Union[str, Any]					 =		outputs.attention_mask

         assert all(len(UpperCAmelCase	) == 512 for x in inputs.input_ids	)
         assert all(len(UpperCAmelCase	) == 128 for x in outputs.input_ids	)

         return batch

     def _compute_metrics(UpperCAmelCase  :      Optional[int]	):
         lowerCamelCase__    :      int					 =		pred.label_ids
         lowerCamelCase__    :      Optional[int]					 =		pred.predictions

         # all unnecessary tokens are removed
         lowerCamelCase__    :      Optional[int]					 =		tokenizer.batch_decode(UpperCAmelCase			,     skip_special_tokens=UpperCAmelCase	)
         lowerCamelCase__    :      List[str]					 =		tokenizer.batch_decode(UpperCAmelCase			,     skip_special_tokens=UpperCAmelCase	)

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

         return {"accuracy": accuracy}

     # map train dataset
     lowerCamelCase__    :      int					 =		train_dataset.map(
         _map_to_encoder_decoder_inputs			,     batched=UpperCAmelCase			,     batch_size=UpperCAmelCase			,     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
     lowerCamelCase__    :      Optional[int]					 =		val_dataset.map(
         _map_to_encoder_decoder_inputs			,     batched=UpperCAmelCase			,     batch_size=UpperCAmelCase			,     remove_columns=['article', 'highlights']			,     )
     val_dataset.set_format(
         type='torch'			,     columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']			,     )

     lowerCamelCase__    :      Dict					 =		self.get_auto_remove_tmp_dir()

     lowerCamelCase__    :      List[str]					 =		SeqaSeqTrainingArguments(
         output_dir=UpperCAmelCase			,     per_device_train_batch_size=UpperCAmelCase			,     per_device_eval_batch_size=UpperCAmelCase			,     predict_with_generate=UpperCAmelCase			,     evaluation_strategy='steps'			,     do_train=UpperCAmelCase			,     do_eval=UpperCAmelCase			,     warmup_steps=0			,     eval_steps=2			,     logging_steps=2			,     )

     # instantiate trainer
     lowerCamelCase__    :      List[str]					 =		SeqaSeqTrainer(
         model=UpperCAmelCase			,     args=UpperCAmelCase			,     compute_metrics=_compute_metrics			,     train_dataset=UpperCAmelCase			,     eval_dataset=UpperCAmelCase			,     tokenizer=UpperCAmelCase			,     )

     # start training
     trainer.train()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase								=	{
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""MobileViTFeatureExtractor"""]
     UpperCamelCase								=	["""MobileViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileViTForImageClassification""",
         """MobileViTForSemanticSegmentation""",
         """MobileViTModel""",
         """MobileViTPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFMobileViTForImageClassification""",
         """TFMobileViTForSemanticSegmentation""",
         """TFMobileViTModel""",
         """TFMobileViTPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilevit import MobileViTFeatureExtractor
          from .image_processing_mobilevit import MobileViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevit import (
              MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTForImageClassification,
              MobileViTForSemanticSegmentation,
              MobileViTModel,
              MobileViTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilevit import (
              TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileViTForImageClassification,
              TFMobileViTForSemanticSegmentation,
              TFMobileViTModel,
              TFMobileViTPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




