
"""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
_a		       =							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 A_	:




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default="""cifar10"""				,metadata={"""help""": """Name of a dataset from the datasets package"""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """The column name of the images in the files."""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(default=lowercase__				,metadata={"""help""": """A folder containing the training data."""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(default=lowercase__				,metadata={"""help""": """A folder containing the validation data."""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[float]       =		field(
      default=0.1_5				,metadata={"""help""": """Percent to split off of train for validation."""}      )
  SCREAMING_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."""
          )
      }				,)
  SCREAMING_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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    {}
     if self.train_dir is not None:
        UpperCAmelCase_					:					Optional[int]			     =    self.train_dir
     if self.validation_dir is not None:
        UpperCAmelCase_					:					List[str]			     =    self.validation_dir
     UpperCAmelCase_					:					List[str]			     =    data_files if data_files else None




@dataclass
class A_	:




  '''simple docstring'''
  SCREAMING_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."""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}      )
  SCREAMING_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"""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}      )
  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default="""main"""				,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}				,)
  SCREAMING_SNAKE_CASE__			:						str       =		field(default=lowercase__				,metadata={"""help""": """Name or path of preprocessor config."""}      )
  SCREAMING_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)."""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						float       =		field(
      default=0.7_5				,metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}      )
  SCREAMING_SNAKE_CASE__			:						bool       =		field(
      default=lowercase__				,metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}      )




@dataclass
class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						float       =		field(
      default=1e-3				,metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}      )







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    torch.stack([example["pixel_values"] for example in examples]  )
   return {"pixel_values": pixel_values}







def 	__a     (		):
   # 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.

   UpperCAmelCase_					:					str			     =    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.
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
   else:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, 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",		__lowerCamelCase,		__lowerCamelCase  )

   # 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()

   UpperCAmelCase_					:					Any			     =    training_args.get_process_log_level()
   logger.setLevel(__lowerCamelCase  )
   transformers.utils.logging.set_verbosity(__lowerCamelCase  )
   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.
   UpperCAmelCase_					:					List[str]			     =    None
   if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
      UpperCAmelCase_					:					List[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.
   UpperCAmelCase_					:					Tuple			     =    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.
   UpperCAmelCase_					:					Optional[Any]			     =    None if "validation" in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split,		__lowerCamelCase  ) and data_args.train_val_split > 0.0:
      UpperCAmelCase_					:					List[str]			     =    ds["train"].train_test_split(data_args.train_val_split  )
      UpperCAmelCase_					:					Dict			     =    split["train"]
      UpperCAmelCase_					:					str			     =    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.
   UpperCAmelCase_					:					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:
      UpperCAmelCase_					:					Tuple			     =    ViTMAEConfig.from_pretrained(model_args.config_name,		**__lowerCamelCase  )
   elif model_args.model_name_or_path:
      UpperCAmelCase_					:					List[Any]			     =    ViTMAEConfig.from_pretrained(model_args.model_name_or_path,		**__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					Tuple			     =    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:
      UpperCAmelCase_					:					List[Any]			     =    ViTImageProcessor.from_pretrained(model_args.image_processor_name,		**__lowerCamelCase  )
   elif model_args.model_name_or_path:
      UpperCAmelCase_					:					str			     =    ViTImageProcessor.from_pretrained(model_args.model_name_or_path,		**__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					Optional[int]			     =    ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      UpperCAmelCase_					:					int			     =    ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path,		from_tf=bool(".ckpt" in model_args.model_name_or_path  ),		config=__lowerCamelCase,		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"  )
      UpperCAmelCase_					:					Dict			     =    ViTMAEForPreTraining(__lowerCamelCase  )

   if training_args.do_train:
      UpperCAmelCase_					:					Optional[int]			     =    ds["train"].column_names
   else:
      UpperCAmelCase_					:					Dict			     =    ds["validation"].column_names

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

   def preprocess_images(__lowerCamelCase  ):
      UpperCAmelCase_					:					Union[str, Any]			     =    [transforms(__lowerCamelCase  ) 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:
         UpperCAmelCase_					:					List[str]			     =    ds["train"].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
      # Set the training transforms
      ds["train"].set_transform(__lowerCamelCase  )

   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:
         UpperCAmelCase_					:					Optional[int]			     =    (
             ds["validation"].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
         )
      # Set the validation transforms
      ds["validation"].set_transform(__lowerCamelCase  )

   # Compute absolute learning rate
   UpperCAmelCase_					:					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:
      UpperCAmelCase_					:					Union[str, Any]			     =    training_args.base_learning_rate * total_train_batch_size / 256

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

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

   # Write model card and (optionally) push to hub
   UpperCAmelCase_					:					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(**__lowerCamelCase  )
   else:
      trainer.create_model_card(**__lowerCamelCase  )







def 	__a     (		__lowerCamelCase  ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_a	:    Dict         =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_a	:    List[Any]         =		{0: '''batch''', 1: '''sequence'''}
												_a	:    Union[str, Any]         =		{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]       )



