


"""simple docstring"""
from __future__ import annotations

def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      list[str] | None = None   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		word_bank or []
   # create a table
   _lowerCamelCase	:					int   =		len(__snake_case   ) + 1

   _lowerCamelCase	:					list[list[list[str]]]   =		[]
   for _ in range(__snake_case   ):
      table.append([]   )
   # seed value
   _lowerCamelCase	:					List[Any]   =		[[]]  # because empty string has empty combination

   # iterate through the indices
   for i in range(__snake_case   ):
      # condition
      if table[i] != []:
         for word in word_bank:
            # slice condition
            if target[i : i + len(__snake_case   )] == word:
               _lowerCamelCase	:					list[list[str]]   =		[
                   [word, *way] for way in table[i]
               ]
               # adds the word to every combination the current position holds
               # now,push that combination to the table[i+len(word)]
               table[i + len(__snake_case   )] += new_combinations

    # combinations are in reverse order so reverse for better output
   for combination in table[len(__snake_case   )]:
      combination.reverse()

   return table[len(__snake_case   )]


if __name__ == "__main__":
 print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
 print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
 print(
     all_construct(
         """hexagonosaurus""",
         ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
     )
 )





"""simple docstring"""
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				=						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 	lowercase__				:
   __UpperCAmelCase							       =					field(
       default='''cifar10''' ,metadata={'''help''': '''Name of a dataset from the datasets package'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''The column name of the images in the files.'''}				)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''A folder containing the training data.'''}				)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''A folder containing the validation data.'''}				)
   __UpperCAmelCase							       =					field(
       default=0.1_5 ,metadata={'''help''': '''Percent to split off of train for validation.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of training examples to this '''
               '''value if set.'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
               '''value if set.'''
           )
       } ,)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Any   =		{}
      if self.train_dir is not None:
         _lowerCamelCase	:					int   =		self.train_dir
      if self.validation_dir is not None:
         _lowerCamelCase	:					Tuple   =		self.validation_dir
      _lowerCamelCase	:					Optional[int]   =		data_files if data_files else None







@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,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'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}				)
   __UpperCAmelCase							       =					field(
       default='''main''' ,metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''Name or path of preprocessor config.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=0.7_5 ,metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}				)







@dataclass
class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					field(
       default=1e-3 ,metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}				)







def 				_snake_case      (   __snake_case							:      Optional[Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		torch.stack([example["""pixel_values"""] for example in examples]   )
   return {"pixel_values": pixel_values}

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		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.
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		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"""						, __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	:					Union[str, Any]   =		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	:					List[Any]   =		None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      _lowerCamelCase	:					Optional[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."""   )

    # Initialize our dataset.
   _lowerCamelCase	:					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.
   _lowerCamelCase	:					Tuple   =		None if """validation""" in ds.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	:					List[str]   =		ds["""train"""].train_test_split(data_args.train_val_split   )
      _lowerCamelCase	:					Union[str, Any]   =		split["""train"""]
      _lowerCamelCase	:					Optional[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.
   _lowerCamelCase	:					str   =		{
       """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:
      _lowerCamelCase	:					Dict   =		ViTMAEConfig.from_pretrained(model_args.config_name						, **__snake_case   )
   elif model_args.model_name_or_path:
      _lowerCamelCase	:					Union[str, Any]   =		ViTMAEConfig.from_pretrained(model_args.model_name_or_path						, **__snake_case   )
   else:
      _lowerCamelCase	:					Optional[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:
      _lowerCamelCase	:					str   =		ViTImageProcessor.from_pretrained(model_args.image_processor_name						, **__snake_case   )
   elif model_args.model_name_or_path:
      _lowerCamelCase	:					Dict   =		ViTImageProcessor.from_pretrained(model_args.model_name_or_path						, **__snake_case   )
   else:
      _lowerCamelCase	:					Union[str, Any]   =		ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      _lowerCamelCase	:					List[Any]   =		ViTMAEForPreTraining.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						, )
   else:
      logger.info("""Training new model from scratch"""   )
      _lowerCamelCase	:					Union[str, Any]   =		ViTMAEForPreTraining(__snake_case   )

   if training_args.do_train:
      _lowerCamelCase	:					List[Any]   =		ds["""train"""].column_names
   else:
      _lowerCamelCase	:					Union[str, Any]   =		ds["""validation"""].column_names

   if data_args.image_column_name is not None:
      _lowerCamelCase	:					str   =		data_args.image_column_name
   elif "image" in column_names:
      _lowerCamelCase	:					Optional[Any]   =		"""image"""
   elif "img" in column_names:
      _lowerCamelCase	:					List[Any]   =		"""img"""
   else:
      _lowerCamelCase	:					str   =		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:
      _lowerCamelCase	:					Dict   =		image_processor.size["""shortest_edge"""]
   else:
      _lowerCamelCase	:					List[Any]   =		(image_processor.size["""height"""], image_processor.size["""width"""])
   _lowerCamelCase	:					Tuple   =		Compose(
       [
           Lambda(lambda __snake_case   : img.convert("""RGB"""   ) if img.mode != "RGB" else img   ),
           RandomResizedCrop(__snake_case						, scale=(0.2, 1.0)						, interpolation=InterpolationMode.BICUBIC   ),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean						, std=image_processor.image_std   ),
       ]   )

   def preprocess_images(__snake_case							:      Optional[Any]   ):
      _lowerCamelCase	:					Dict   =		[transforms(__snake_case   ) 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:
         _lowerCamelCase	:					int   =		ds["""train"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
      # Set the training transforms
      ds["train"].set_transform(__snake_case   )

   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:
         _lowerCamelCase	:					Union[str, Any]   =		(
             ds["""validation"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
         )
      # Set the validation transforms
      ds["validation"].set_transform(__snake_case   )

   # Compute absolute learning rate
   _lowerCamelCase	:					Optional[Any]   =		(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      _lowerCamelCase	:					Tuple   =		training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   _lowerCamelCase	:					Optional[Any]   =		Trainer(
       model=__snake_case						, args=__snake_case						, train_dataset=ds["""train"""] if training_args.do_train else None						, eval_dataset=ds["""validation"""] if training_args.do_eval else None						, tokenizer=__snake_case						, data_collator=__snake_case						, )

   # Training
   if training_args.do_train:
      _lowerCamelCase	:					Any   =		None
      if training_args.resume_from_checkpoint is not None:
         _lowerCamelCase	:					List[Any]   =		training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _lowerCamelCase	:					Union[str, Any]   =		last_checkpoint
      _lowerCamelCase	:					Optional[Any]   =		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	:					int   =		trainer.evaluate()
      trainer.log_metrics("""eval"""						, __snake_case   )
      trainer.save_metrics("""eval"""						, __snake_case   )

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

def 				_snake_case      (   __snake_case							:      Dict   ):




   """simple docstring"""







   main()


if __name__ == "__main__":
 main()

