


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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
UpperCAmelCase_				:			Dict   =	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-pretraining/requirements.txt')
@dataclass
class 			SCREAMING_SNAKE_CASE__  :
      snake_case__    :					Optional[str]					       =						field(
          default='''cifar10'''						,   metadata={'''help''': '''Name of a dataset from the datasets package'''}      )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''The column name of the images in the files.'''}      )
      snake_case__    :					Optional[str]					       =						field(default=lowercase__						,   metadata={'''help''': '''A folder containing the training data.'''}      )
      snake_case__    :					Optional[str]					       =						field(default=lowercase__						,   metadata={'''help''': '''A folder containing the validation data.'''}      )
      snake_case__    :					Optional[float]					       =						field(
          default=0.15						,   metadata={'''help''': '''Percent to split off of train for validation.'''}      )
      snake_case__    :					Optional[int]					       =						field(
          default=lowercase__						,   metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                  '''value if set.'''
              )
          }						,   )
      snake_case__    :					Optional[int]					       =						field(
          default=lowercase__						,   metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                  '''value if set.'''
              )
          }						,   )
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Tuple:
             a_ :		Optional[int]							=  {}
             if self.train_dir is not None:
                    a_ :		List[str]							=  self.train_dir
             if self.validation_dir is not None:
                    a_ :		Dict							=  self.validation_dir
             a_ :		Dict							=  data_files if data_files else None
@dataclass
class 			SCREAMING_SNAKE_CASE__  :
      snake_case__    :					str					       =						field(
          default=lowercase__						,   metadata={
              '''help''': (
                  '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
              )
          }						,   )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}      )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={
              '''help''': (
                  '''Override some existing default config settings when a model is trained from scratch. Example: '''
                  '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
              )
          }						,   )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}      )
      snake_case__    :					str					       =						field(
          default='''main'''						,   metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}						,   )
      snake_case__    :					str					       =						field(default=lowercase__						,   metadata={'''help''': '''Name or path of preprocessor config.'''}      )
      snake_case__    :					bool					       =						field(
          default=lowercase__						,   metadata={
              '''help''': (
                  '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                  '''with private models).'''
              )
          }						,   )
      snake_case__    :					float					       =						field(
          default=0.75						,   metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}      )
      snake_case__    :					bool					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}      )
@dataclass
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					float					       =						field(
          default=1e-3						,   metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}      )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		str							=  torch.stack([example['pixel_values'] for example in examples]    )
       return {"pixel_values": pixel_values}







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Dict:

       """simple docstring"""

       a_ :		List[Any]							=  HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)    )
       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.
              a_							,		a_							,		a_ :		int							=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              a_							,		a_							,		a_ :		List[Any]							=  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_mae'  ,   __A  ,   __A    )

       # 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()

       a_ :		Optional[int]							=  training_args.get_process_log_level()
       logger.setLevel(__A    )
       transformers.utils.logging.set_verbosity(__A    )
       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.
       a_ :		List[str]							=  None
       if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
              a_ :		str							=  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.'    )

    # Initialize our dataset.
       a_ :		Optional[Any]							=  load_dataset(
           data_args.dataset_name  ,   data_args.dataset_config_name  ,   data_files=data_args.data_files  ,   cache_dir=model_args.cache_dir  ,   use_auth_token=True if model_args.use_auth_token else None  ,   )

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

       # Load pretrained model and image processor
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       a_ :		Dict							=  {
           'cache_dir': model_args.cache_dir,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              a_ :		Optional[Any]							=  ViTMAEConfig.from_pretrained(model_args.config_name  ,   **__A    )
       elif model_args.model_name_or_path:
              a_ :		Any							=  ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,   **__A    )
       else:
              a_ :		Union[str, Any]							=  ViTMAEConfig()
              logger.warning('You are instantiating a new config instance from scratch.'    )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""    )
                     config.update_from_string(model_args.config_overrides    )
                     logger.info(F"""New config: {config}"""    )

    # adapt config
       config.update(
           {
               'mask_ratio': model_args.mask_ratio,
               'norm_pix_loss': model_args.norm_pix_loss,
           }    )

       # create image processor
       if model_args.image_processor_name:
              a_ :		Union[str, Any]							=  ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,   **__A    )
       elif model_args.model_name_or_path:
              a_ :		Optional[int]							=  ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,   **__A    )
       else:
              a_ :		Dict							=  ViTImageProcessor()

       # create model
       if model_args.model_name_or_path:
              a_ :		Union[str, Any]							=  ViTMAEForPreTraining.from_pretrained(
                  model_args.model_name_or_path  ,   from_tf=bool('.ckpt' in model_args.model_name_or_path    )  ,   config=__A  ,   cache_dir=model_args.cache_dir  ,   revision=model_args.model_revision  ,   use_auth_token=True if model_args.use_auth_token else None  ,   )
       else:
              logger.info('Training new model from scratch'    )
              a_ :		Tuple							=  ViTMAEForPreTraining(__A    )

       if training_args.do_train:
              a_ :		List[str]							=  ds['train'].column_names
       else:
              a_ :		Dict							=  ds['validation'].column_names

       if data_args.image_column_name is not None:
              a_ :		Any							=  data_args.image_column_name
       elif "image" in column_names:
              a_ :		Any							=  'image'
       elif "img" in column_names:
              a_ :		Any							=  'img'
       else:
              a_ :		List[Any]							=  column_names[0]

       # transformations as done in original MAE paper
       # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
       if "shortest_edge" in image_processor.size:
              a_ :		Dict							=  image_processor.size['shortest_edge']
       else:
              a_ :		Union[str, Any]							=  (image_processor.size['height'], image_processor.size['width'])
       a_ :		Dict							=  Compose(
           [
               Lambda(lambda __A    : img.convert('RGB'    ) if img.mode != "RGB" else img    ),
               RandomResizedCrop(__A  ,   scale=(0.2, 1.0)  ,   interpolation=InterpolationMode.BICUBIC    ),
               RandomHorizontalFlip(),
               ToTensor(),
               Normalize(mean=image_processor.image_mean  ,   std=image_processor.image_std    ),
           ]    )

       def preprocess_images(__A			:      Dict    ):
              a_ :		str							=  [transforms(__A    ) for image in examples[image_column_name]]
              return examples

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

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

       # Compute absolute learning rate
       a_ :		Optional[int]							=  (
           training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
       )
       if training_args.base_learning_rate is not None:
              a_ :		Optional[Any]							=  training_args.base_learning_rate * total_train_batch_size / 2_56

       # Initialize our trainer
       a_ :		Dict							=  Trainer(
           model=__A  ,   args=__A  ,   train_dataset=ds['train'] if training_args.do_train else None  ,   eval_dataset=ds['validation'] if training_args.do_eval else None  ,   tokenizer=__A  ,   data_collator=__A  ,   )

       # Training
       if training_args.do_train:
              a_ :		Any							=  None
              if training_args.resume_from_checkpoint is not None:
                     a_ :		Any							=  training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     a_ :		Optional[Any]							=  last_checkpoint
              a_ :		Optional[int]							=  trainer.train(resume_from_checkpoint=__A    )
              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:
              a_ :		Union[str, Any]							=  trainer.evaluate()
              trainer.log_metrics('eval'  ,   __A    )
              trainer.save_metrics('eval'  ,   __A    )

       # Write model card and (optionally) push to hub
       a_ :		Any							=  {
           'tasks': 'masked-auto-encoding',
           'dataset': data_args.dataset_name,
           'tags': ['masked-auto-encoding'],
       }
       if training_args.push_to_hub:
              trainer.push_to_hub(**__A    )
       else:
              trainer.create_model_card(**__A    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str    )     ->		Optional[int]:

       """simple docstring"""

       main()


if __name__ == "__main__":
   main()






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_				:			str   =	logging.get_logger(__name__)

UpperCAmelCase_				:			str   =	{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					int					       =						'''instructblip_vision_model'''
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	List[Any]=1_4_0_8					,							SCREAMING_SNAKE_CASE__    :	int=6_1_4_4					,							SCREAMING_SNAKE_CASE__    :	Dict=3_9					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1_6					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=2_2_4					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=1_4					,							SCREAMING_SNAKE_CASE__    :	List[Any]="gelu"					,							SCREAMING_SNAKE_CASE__    :	List[str]=1E-6					,							SCREAMING_SNAKE_CASE__    :	Tuple=0.0					,							SCREAMING_SNAKE_CASE__    :	List[str]=1E-10					,							SCREAMING_SNAKE_CASE__    :	str=True					,							**SCREAMING_SNAKE_CASE__    :	Dict					,							)		->      int:
             super().__init__(**SCREAMING_SNAKE_CASE__					)

             a_ :		List[str]							=  hidden_size
             a_ :		Any							=  intermediate_size
             a_ :		str							=  num_hidden_layers
             a_ :		Dict							=  num_attention_heads
             a_ :		str							=  patch_size
             a_ :		Any							=  image_size
             a_ :		Dict							=  initializer_range
             a_ :		List[Any]							=  attention_dropout
             a_ :		Union[str, Any]							=  layer_norm_eps
             a_ :		Optional[Any]							=  hidden_act
             a_ :		Optional[Any]							=  qkv_bias

      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, os.PathLike]					,							**SCREAMING_SNAKE_CASE__    :	List[str]					)		->      "PretrainedConfig":
             cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__					)

             a_							,		a_ :		Dict							=  cls.get_config_dict(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

             # get the vision config dict if we are loading from InstructBlipConfig
             if config_dict.get('model_type'					) == "instructblip":
                    a_ :		List[Any]							=  config_dict['vision_config']

             if "model_type" in config_dict and hasattr(cls					,							'model_type'					) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

             return cls.from_dict(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					List[Any]					       =						'''instructblip_qformer'''
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=3_0_5_2_2					,							SCREAMING_SNAKE_CASE__    :	str=7_6_8					,							SCREAMING_SNAKE_CASE__    :	List[str]=1_2					,							SCREAMING_SNAKE_CASE__    :	Any=1_2					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=3_0_7_2					,							SCREAMING_SNAKE_CASE__    :	List[str]="gelu"					,							SCREAMING_SNAKE_CASE__    :	Dict=0.1					,							SCREAMING_SNAKE_CASE__    :	int=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=5_1_2					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.02					,							SCREAMING_SNAKE_CASE__    :	int=1E-12					,							SCREAMING_SNAKE_CASE__    :	Dict=0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]="absolute"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=2					,							SCREAMING_SNAKE_CASE__    :	str=1_4_0_8					,							**SCREAMING_SNAKE_CASE__    :	Dict					,							)		->      Optional[Any]:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

             a_ :		Union[str, Any]							=  vocab_size
             a_ :		List[str]							=  hidden_size
             a_ :		Any							=  num_hidden_layers
             a_ :		Dict							=  num_attention_heads
             a_ :		Optional[int]							=  hidden_act
             a_ :		List[str]							=  intermediate_size
             a_ :		Tuple							=  hidden_dropout_prob
             a_ :		Union[str, Any]							=  attention_probs_dropout_prob
             a_ :		List[str]							=  max_position_embeddings
             a_ :		List[str]							=  initializer_range
             a_ :		Any							=  layer_norm_eps
             a_ :		Tuple							=  position_embedding_type
             a_ :		List[str]							=  cross_attention_frequency
             a_ :		Union[str, Any]							=  encoder_hidden_size

      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	str					,							SCREAMING_SNAKE_CASE__    :	Union[str, os.PathLike]					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      "PretrainedConfig":
             cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__					)

             a_							,		a_ :		Union[str, Any]							=  cls.get_config_dict(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

             # get the qformer config dict if we are loading from InstructBlipConfig
             if config_dict.get('model_type'					) == "instructblip":
                    a_ :		Dict							=  config_dict['qformer_config']

             if "model_type" in config_dict and hasattr(cls					,							'model_type'					) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

             return cls.from_dict(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Union[str, Any]					       =						'''instructblip'''
      snake_case__    :					List[str]					       =						True
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=None					,							SCREAMING_SNAKE_CASE__    :	Dict=None					,							SCREAMING_SNAKE_CASE__    :	List[Any]=None					,							SCREAMING_SNAKE_CASE__    :	int=3_2					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Tuple:
             super().__init__(**SCREAMING_SNAKE_CASE__					)

             if vision_config is None:
                    a_ :		Dict							=  {}
                    logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

             if qformer_config is None:
                    a_ :		List[str]							=  {}
                    logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

             if text_config is None:
                    a_ :		Any							=  {}
                    logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

             a_ :		int							=  InstructBlipVisionConfig(**SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  InstructBlipQFormerConfig(**SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[Any]							=  text_config['model_type'] if 'model_type' in text_config else 'opt'
             a_ :		List[Any]							=  CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  self.text_config.tie_word_embeddings
             a_ :		List[Any]							=  self.text_config.is_encoder_decoder

             a_ :		Optional[Any]							=  num_query_tokens
             a_ :		int							=  self.vision_config.hidden_size
             a_ :		List[str]							=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
             a_ :		Optional[int]							=  1.0
             a_ :		Any							=  0.02
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	InstructBlipVisionConfig					,							SCREAMING_SNAKE_CASE__    :	InstructBlipQFormerConfig					,							SCREAMING_SNAKE_CASE__    :	PretrainedConfig					,							**SCREAMING_SNAKE_CASE__    :	List[str]					,							)		->      Any:
             return cls(
                 vision_config=vision_config.to_dict()					,							qformer_config=qformer_config.to_dict()					,							text_config=text_config.to_dict()					,							**SCREAMING_SNAKE_CASE__					,							)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Union[str, Any]:
             a_ :		List[Any]							=  copy.deepcopy(self.__dict__					)
             a_ :		Optional[int]							=  self.vision_config.to_dict()
             a_ :		Optional[Any]							=  self.qformer_config.to_dict()
             a_ :		Union[str, Any]							=  self.text_config.to_dict()
             a_ :		Optional[Any]							=  self.__class__.model_type
             return output


