

"""simple docstring"""




import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
A_							:			Any											= logging.getLogger(__name__)

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      np.argmax(snake_case__		,				axis=1    )
			return np.sum(outputs == labels    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				encoding="""utf_8"""    ) as f:
						SCREAMING_SNAKE_CASE__								=      csv.reader(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      []
						next(snake_case__    )  # skip the first line
						for line in tqdm(snake_case__    ):
									output.append((""" """.join(line[1:5]    ), line[5], line[6], int(line[-1]    ) - 1)    )
			return output

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      []
			for dataset in encoded_datasets:
						SCREAMING_SNAKE_CASE__								=      len(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2, input_len)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.full((n_batch, 2, input_len)		,				fill_value=-1_00		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch,)		,				dtype=np.intaa    )
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(snake_case__    ):
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      mc_label
						SCREAMING_SNAKE_CASE__								=      (input_ids, mc_token_ids, lm_labels, mc_labels)
						tensor_datasets.append(tuple(torch.tensor(snake_case__    ) for t in all_inputs    )    )
			return tensor_datasets

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      argparse.ArgumentParser()
			parser.add_argument("""--model_name"""		,				type=snake_case__		,				default="""openai-gpt"""		,				help="""pretrained model name"""    )
			parser.add_argument("""--do_train"""		,				action="""store_true"""		,				help="""Whether to run training."""    )
			parser.add_argument("""--do_eval"""		,				action="""store_true"""		,				help="""Whether to run eval on the dev set."""    )
			parser.add_argument(
			    """--output_dir"""		,				default=snake_case__		,				type=snake_case__		,				required=snake_case__		,				help="""The output directory where the model predictions and checkpoints will be written."""		,				)
			parser.add_argument("""--train_dataset"""		,				type=snake_case__		,				default=""""""    )
			parser.add_argument("""--eval_dataset"""		,				type=snake_case__		,				default=""""""    )
			parser.add_argument("""--seed"""		,				type=snake_case__		,				default=42    )
			parser.add_argument("""--num_train_epochs"""		,				type=snake_case__		,				default=3    )
			parser.add_argument("""--train_batch_size"""		,				type=snake_case__		,				default=8    )
			parser.add_argument("""--eval_batch_size"""		,				type=snake_case__		,				default=16    )
			parser.add_argument("""--adam_epsilon"""		,				default=1e-8		,				type=snake_case__		,				help="""Epsilon for Adam optimizer."""    )
			parser.add_argument("""--max_grad_norm"""		,				type=snake_case__		,				default=1    )
			parser.add_argument(
			    """--max_steps"""		,				default=-1		,				type=snake_case__		,				help=(
			        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
			    )		,				)
			parser.add_argument(
			    """--gradient_accumulation_steps"""		,				type=snake_case__		,				default=1		,				help="""Number of updates steps to accumulate before                        performing a backward/update pass."""		,				)
			parser.add_argument("""--learning_rate"""		,				type=snake_case__		,				default=6.25e-5    )
			parser.add_argument("""--warmup_steps"""		,				default=0		,				type=snake_case__		,				help="""Linear warmup over warmup_steps."""    )
			parser.add_argument("""--lr_schedule"""		,				type=snake_case__		,				default="""warmup_linear"""    )
			parser.add_argument("""--weight_decay"""		,				type=snake_case__		,				default=0.01    )
			parser.add_argument("""--lm_coef"""		,				type=snake_case__		,				default=0.9    )
			parser.add_argument("""--n_valid"""		,				type=snake_case__		,				default=3_74    )

			parser.add_argument("""--server_ip"""		,				type=snake_case__		,				default=""""""		,				help="""Can be used for distant debugging."""    )
			parser.add_argument("""--server_port"""		,				type=snake_case__		,				default=""""""		,				help="""Can be used for distant debugging."""    )
			SCREAMING_SNAKE_CASE__								=      parser.parse_args()
			print(snake_case__    )

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print("""Waiting for debugger attach"""    )
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,				redirect_output=snake_case__    )
						ptvsd.wait_for_attach()

			random.seed(args.seed    )
			np.random.seed(args.seed    )
			torch.manual_seed(args.seed    )
			torch.cuda.manual_seed_all(args.seed    )

			SCREAMING_SNAKE_CASE__								=      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
			SCREAMING_SNAKE_CASE__								=      torch.cuda.device_count()
			logger.info("""device: {}, n_gpu {}""".format(snake_case__		,				snake_case__    )    )

			if not args.do_train and not args.do_eval:
						raise ValueError("""At least one of `do_train` or `do_eval` must be True."""    )

			if not os.path.exists(args.output_dir    ):
						os.makedirs(args.output_dir    )

			# Load tokenizer and model
			# This loading functions also add new tokens and embeddings called `special tokens`
			# These new embeddings will be fine-tuned on the RocStories dataset
			SCREAMING_SNAKE_CASE__								=      ["""_start_""", """_delimiter_""", """_classify_"""]
			SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.model_name    )
			tokenizer.add_tokens(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.convert_tokens_to_ids(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
			model.resize_token_embeddings(len(snake_case__    )    )
			model.to(snake_case__    )

			# Load and encode the datasets
			def tokenize_and_encode(snake_case__    ):
						if isinstance(snake_case__		,				snake_case__    ):
									return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(snake_case__    )    )
						elif isinstance(snake_case__		,				snake_case__    ):
									return obj
						return [tokenize_and_encode(snake_case__    ) for o in obj]

			logger.info("""Encoding dataset..."""    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.train_dataset    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.eval_dataset    )
			SCREAMING_SNAKE_CASE__								=      (train_dataset, eval_dataset)
			SCREAMING_SNAKE_CASE__								=      tokenize_and_encode(snake_case__    )

			# Compute the max input length for the Transformer
			SCREAMING_SNAKE_CASE__								=      model.config.n_positions // 2 - 2
			SCREAMING_SNAKE_CASE__								=      max(
			    len(story[:max_length]    ) + max(len(conta[:max_length]    )		,				len(conta[:max_length]    )    ) + 3
			    for dataset in encoded_datasets
			    for story, conta, conta, _ in dataset    )
			SCREAMING_SNAKE_CASE__								=      min(snake_case__		,				model.config.n_positions    )  # Max size of input for the pre-trained model

			# Prepare inputs tensors and dataloaders
			SCREAMING_SNAKE_CASE__								=      pre_process_datasets(snake_case__		,				snake_case__		,				snake_case__		,				*snake_case__    )
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      tensor_datasets[0], tensor_datasets[1]

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      RandomSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.train_batch_size    )

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      SequentialSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.eval_batch_size    )

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									SCREAMING_SNAKE_CASE__								=      args.max_steps
									SCREAMING_SNAKE_CASE__								=      args.max_steps // (len(snake_case__    ) // args.gradient_accumulation_steps) + 1
						else:
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) // args.gradient_accumulation_steps * args.num_train_epochs

						SCREAMING_SNAKE_CASE__								=      list(model.named_parameters()    )
						SCREAMING_SNAKE_CASE__								=      ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
						SCREAMING_SNAKE_CASE__								=      [
						    {
						        """params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay    )],
						        """weight_decay""": args.weight_decay,
						    },
						    {"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay    )], """weight_decay""": 0.0},
						]
						SCREAMING_SNAKE_CASE__								=      AdamW(snake_case__		,				lr=args.learning_rate		,				eps=args.adam_epsilon    )
						SCREAMING_SNAKE_CASE__								=      get_linear_schedule_with_warmup(
						    snake_case__		,				num_warmup_steps=args.warmup_steps		,				num_training_steps=snake_case__    )

			if args.do_train:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs    )		,				desc="""Epoch"""    ):
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      tqdm(snake_case__		,				desc="""Training"""    )
									for step, batch in enumerate(snake_case__    ):
												SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
												SCREAMING_SNAKE_CASE__								=      model(snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )
												SCREAMING_SNAKE_CASE__								=      args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												SCREAMING_SNAKE_CASE__								=      (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												SCREAMING_SNAKE_CASE__								=      """Training loss: {:.2e} lr: {:.2e}""".format(snake_case__		,				scheduler.get_lr()[0]    )

    # Save a trained model
			if args.do_train:
						# Save a trained model, configuration and tokenizer
						SCREAMING_SNAKE_CASE__								=      model.module if hasattr(snake_case__		,				"""module"""    ) else model  # Only save the model itself

						# If we save using the predefined names, we can load using `from_pretrained`
						SCREAMING_SNAKE_CASE__								=      os.path.join(args.output_dir		,				snake_case__    )
						SCREAMING_SNAKE_CASE__								=      os.path.join(args.output_dir		,				snake_case__    )

						torch.save(model_to_save.state_dict()		,				snake_case__    )
						model_to_save.config.to_json_file(snake_case__    )
						tokenizer.save_vocabulary(args.output_dir    )

						# Load a trained model and vocabulary that you have fine-tuned
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
						model.to(snake_case__    )

			if args.do_eval:
						model.eval()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						for batch in tqdm(snake_case__		,				desc="""Evaluating"""    ):
									SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      model(
												    snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )

									SCREAMING_SNAKE_CASE__								=      mc_logits.detach().cpu().numpy()
									SCREAMING_SNAKE_CASE__								=      mc_labels.to("""cpu"""    ).numpy()
									SCREAMING_SNAKE_CASE__								=      accuracy(snake_case__		,				snake_case__    )

									eval_loss += mc_loss.mean().item()
									eval_accuracy += tmp_eval_accuracy

									nb_eval_examples += input_ids.size(0    )
									nb_eval_steps += 1

						SCREAMING_SNAKE_CASE__								=      eval_loss / nb_eval_steps
						SCREAMING_SNAKE_CASE__								=      eval_accuracy / nb_eval_examples
						SCREAMING_SNAKE_CASE__								=      tr_loss / nb_tr_steps if args.do_train else None
						SCREAMING_SNAKE_CASE__								=      {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

						SCREAMING_SNAKE_CASE__								=      os.path.join(args.output_dir		,				"""eval_results.txt"""    )
						with open(snake_case__		,				"""w"""    ) as writer:
									logger.info("""***** Eval results *****"""    )
									for key in sorted(result.keys()    ):
												logger.info("""  %s = %s"""		,				snake_case__		,				str(result[key]    )    )
												writer.write("""%s = %s\n""" % (key, str(result[key]    ))    )


if __name__ == "__main__":
						main()





"""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_							:			List[Any]											= 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    lowerCamelCase						:
			lowerCamelCase__   :		Optional[str]							=							field(
			    default='cifar10'			,metadata={'help': 'Name of a dataset from the datasets package'}			)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'The column name of the images in the files.'}			)
			lowerCamelCase__   :		Optional[str]							=							field(default=A__			,metadata={'help': 'A folder containing the training data.'}			)
			lowerCamelCase__   :		Optional[str]							=							field(default=A__			,metadata={'help': 'A folder containing the validation data.'}			)
			lowerCamelCase__   :		Optional[float]							=							field(
			    default=0.1_5			,metadata={'help': 'Percent to split off of train for validation.'}			)
			lowerCamelCase__   :		Optional[int]							=							field(
			    default=A__			,metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }			,)
			lowerCamelCase__   :		Optional[int]							=							field(
			    default=A__			,metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
			            'value if set.'
			        )
			    }			,)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      {}
						if self.train_dir is not None:
									SCREAMING_SNAKE_CASE__								=      self.train_dir
						if self.validation_dir is not None:
									SCREAMING_SNAKE_CASE__								=      self.validation_dir
						SCREAMING_SNAKE_CASE__								=      data_files if data_files else None





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





@dataclass
class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		float							=							field(
			    default=1E-3			,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      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.
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
			else:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      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()

			SCREAMING_SNAKE_CASE__								=      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.
			SCREAMING_SNAKE_CASE__								=      None
			if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_SNAKE_CASE__								=      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.
			SCREAMING_SNAKE_CASE__								=      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.
			SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      ds["""train"""].train_test_split(data_args.train_val_split    )
						SCREAMING_SNAKE_CASE__								=      split["""train"""]
						SCREAMING_SNAKE_CASE__								=      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.
			SCREAMING_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,
			}
			if model_args.config_name:
						SCREAMING_SNAKE_CASE__								=      ViTMAEConfig.from_pretrained(model_args.config_name		,				**snake_case__    )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,				**snake_case__    )
			else:
						SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(model_args.image_processor_name		,				**snake_case__    )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,				**snake_case__    )
			else:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      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"""    )
						SCREAMING_SNAKE_CASE__								=      ViTMAEForPreTraining(snake_case__    )

			if training_args.do_train:
						SCREAMING_SNAKE_CASE__								=      ds["""train"""].column_names
			else:
						SCREAMING_SNAKE_CASE__								=      ds["""validation"""].column_names

			if data_args.image_column_name is not None:
						SCREAMING_SNAKE_CASE__								=      data_args.image_column_name
			elif "image" in column_names:
						SCREAMING_SNAKE_CASE__								=      """image"""
			elif "img" in column_names:
						SCREAMING_SNAKE_CASE__								=      """img"""
			else:
						SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      image_processor.size["""shortest_edge"""]
			else:
						SCREAMING_SNAKE_CASE__								=      (image_processor.size["""height"""], image_processor.size["""width"""])
			SCREAMING_SNAKE_CASE__								=      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__    ):
						SCREAMING_SNAKE_CASE__								=      [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:
									SCREAMING_SNAKE_CASE__								=      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:
									SCREAMING_SNAKE_CASE__								=      (
									    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
			SCREAMING_SNAKE_CASE__								=      (
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						SCREAMING_SNAKE_CASE__								=      training_args.base_learning_rate * total_train_batch_size / 2_56

			# Initialize our trainer
			SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      None
						if training_args.resume_from_checkpoint is not None:
									SCREAMING_SNAKE_CASE__								=      training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									SCREAMING_SNAKE_CASE__								=      last_checkpoint
						SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      trainer.evaluate()
						trainer.log_metrics("""eval"""		,				snake_case__    )
						trainer.save_metrics("""eval"""		,				snake_case__    )

			# Write model card and (optionally) push to hub
			SCREAMING_SNAKE_CASE__								=      {
			    """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 	A       (     snake_case__    ):




			'''simple docstring'''


			main()


if __name__ == "__main__":
						main()


