

"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


A_							:			Optional[Any]											= logging.get_logger(__name__)


A_							:			int											= {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
A_							:			Union[str, Any]											= {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

A_							:			List[str]											= {
    "allenai/led-base-16384": 16_384,
}

@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      (
			    list(range(ord("""!"""    )		,				ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )		,				ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )		,				ord("""ÿ"""    ) + 1    )    )
			)
			SCREAMING_SNAKE_CASE__								=      bs[:]
			SCREAMING_SNAKE_CASE__								=      0
			for b in range(2**8    ):
						if b not in bs:
									bs.append(snake_case__    )
									cs.append(2**8 + n    )
									n += 1
			SCREAMING_SNAKE_CASE__								=      [chr(snake_case__    ) for n in cs]
			return dict(zip(snake_case__		,				snake_case__    )    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      set()
			SCREAMING_SNAKE_CASE__								=      word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)    )
						SCREAMING_SNAKE_CASE__								=      char
			return pairs





class    lowerCamelCase						(A__			):
							lowerCamelCase__   :		Union[str, Any]							=							VOCAB_FILES_NAMES
							lowerCamelCase__   :		List[str]							=							PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__   :		str							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase__   :		List[str]							=							['input_ids', 'attention_mask']

							def __init__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[Any]="replace"    ,				__UpperCAmelCase				:						Tuple="<s>"    ,				__UpperCAmelCase				:						List[Any]="</s>"    ,				__UpperCAmelCase				:						Optional[int]="</s>"    ,				__UpperCAmelCase				:						Optional[Any]="<s>"    ,				__UpperCAmelCase				:						Dict="<unk>"    ,				__UpperCAmelCase				:						str="<pad>"    ,				__UpperCAmelCase				:						int="<mask>"    ,				__UpperCAmelCase				:						int=False    ,				**__UpperCAmelCase				:						Optional[Any]    ,				)   ->    List[str]:
										SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else bos_token
										SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else eos_token
										SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else sep_token
										SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else cls_token
										SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else unk_token
										SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else mask_token

										super().__init__(
										    errors=__UpperCAmelCase    ,				bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				mask_token=__UpperCAmelCase    ,				add_prefix_space=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

										with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as vocab_handle:
													SCREAMING_SNAKE_CASE__								=      json.load(__UpperCAmelCase       )
										SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.encoder.items()}
										SCREAMING_SNAKE_CASE__								=      errors  # how to handle errors in decoding
										SCREAMING_SNAKE_CASE__								=      bytes_to_unicode()
										SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.byte_encoder.items()}
										with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as merges_handle:
													SCREAMING_SNAKE_CASE__								=      merges_handle.read().split("""\n"""       )[1:-1]
										SCREAMING_SNAKE_CASE__								=      [tuple(merge.split()       ) for merge in bpe_merges]
										SCREAMING_SNAKE_CASE__								=      dict(zip(__UpperCAmelCase    ,				range(len(__UpperCAmelCase       )       )       )       )
										SCREAMING_SNAKE_CASE__								=      {}
										SCREAMING_SNAKE_CASE__								=      add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										SCREAMING_SNAKE_CASE__								=      re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )

							@property
							# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
							def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
										return len(self.encoder       )

							def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[int]:
										return dict(self.encoder    ,				**self.added_tokens_encoder       )

							def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[str]       )   ->    Optional[int]:
										if token in self.cache:
													return self.cache[token]
										SCREAMING_SNAKE_CASE__								=      tuple(__UpperCAmelCase       )
										SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )

										if not pairs:
													return token

										while True:
													SCREAMING_SNAKE_CASE__								=      min(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase       : self.bpe_ranks.get(__UpperCAmelCase    ,				float("""inf"""       )       )       )
													if bigram not in self.bpe_ranks:
																break
													SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      bigram
													SCREAMING_SNAKE_CASE__								=      []
													SCREAMING_SNAKE_CASE__								=      0
													while i < len(__UpperCAmelCase       ):
																try:
																			SCREAMING_SNAKE_CASE__								=      word.index(__UpperCAmelCase    ,				__UpperCAmelCase       )
																except ValueError:
																			new_word.extend(word[i:]       )
																			break
																else:
																			new_word.extend(word[i:j]       )
																			SCREAMING_SNAKE_CASE__								=      j

																if word[i] == first and i < len(__UpperCAmelCase       ) - 1 and word[i + 1] == second:
																			new_word.append(first + second       )
																			i += 2
																else:
																			new_word.append(word[i]       )
																			i += 1
													SCREAMING_SNAKE_CASE__								=      tuple(__UpperCAmelCase       )
													SCREAMING_SNAKE_CASE__								=      new_word
													if len(__UpperCAmelCase       ) == 1:
																break
													else:
																SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )
										SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )
										SCREAMING_SNAKE_CASE__								=      word
										return word

							def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						List[str]       )   ->    Tuple:
										SCREAMING_SNAKE_CASE__								=      []
										for token in re.findall(self.pat    ,				__UpperCAmelCase       ):
													SCREAMING_SNAKE_CASE__								=      """""".join(
													    self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCAmelCase       ).split(""" """       )       )
										return bpe_tokens

							def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						int       )   ->    Dict:
										return self.encoder.get(__UpperCAmelCase    ,				self.encoder.get(self.unk_token       )       )

							def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Any       )   ->    Dict:
										return self.decoder.get(__UpperCAmelCase       )

							def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						int       )   ->    Tuple:
										SCREAMING_SNAKE_CASE__								=      """""".join(__UpperCAmelCase       )
										SCREAMING_SNAKE_CASE__								=      bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""    ,				errors=self.errors       )
										return text

							def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
										if not os.path.isdir(__UpperCAmelCase       ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
													return
										SCREAMING_SNAKE_CASE__								=      os.path.join(
										    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
										SCREAMING_SNAKE_CASE__								=      os.path.join(
										    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

										with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as f:
													f.write(json.dumps(self.encoder    ,				indent=2    ,				sort_keys=__UpperCAmelCase    ,				ensure_ascii=__UpperCAmelCase       ) + """\n"""       )

										SCREAMING_SNAKE_CASE__								=      0
										with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as writer:
													writer.write("""#version: 0.2\n"""       )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,				key=lambda __UpperCAmelCase       : kv[1]       ):
																if index != token_index:
																			logger.warning(
																			    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																			    """ Please check that the tokenizer is not corrupted!"""       )
																			SCREAMING_SNAKE_CASE__								=      token_index
																writer.write(""" """.join(__UpperCAmelCase       ) + """\n"""       )
																index += 1

										return vocab_file, merge_file

							def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										SCREAMING_SNAKE_CASE__								=      [self.cls_token_id]
										SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep

							def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None    ,				__UpperCAmelCase				:						bool = False       )   ->    List[int]:
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase       )

										if token_ids_a is None:
													return [1] + ([0] * len(__UpperCAmelCase       )) + [1]
										return [1] + ([0] * len(__UpperCAmelCase       )) + [1, 1] + ([0] * len(__UpperCAmelCase       )) + [1]

							def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
										SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
										SCREAMING_SNAKE_CASE__								=      [self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]

							def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						List[str]=False    ,				**__UpperCAmelCase				:						Tuple       )   ->    Union[str, Any]:
										SCREAMING_SNAKE_CASE__								=      kwargs.pop("""add_prefix_space"""    ,				self.add_prefix_space       )
										if (is_split_into_words or add_prefix_space) and (len(__UpperCAmelCase       ) > 0 and not text[0].isspace()):
													SCREAMING_SNAKE_CASE__								=      """ """ + text
										return (text, kwargs)





							def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[Dict[str, EncodedInput], BatchEncoding]    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				)   ->    dict:
										SCREAMING_SNAKE_CASE__								=      super()._pad(
										    encoded_inputs=__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding_strategy=__UpperCAmelCase    ,				pad_to_multiple_of=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)

										# Load from model defaults
										if return_attention_mask is None:
													SCREAMING_SNAKE_CASE__								=      """attention_mask""" in self.model_input_names

										if return_attention_mask and "global_attention_mask" in encoded_inputs:
													SCREAMING_SNAKE_CASE__								=      encoded_inputs[self.model_input_names[0]]
													# `global_attention_mask` need to have the same length as other (sequential) inputs.
													SCREAMING_SNAKE_CASE__								=      len(encoded_inputs["""global_attention_mask"""]       ) != len(__UpperCAmelCase       )

													if needs_to_be_padded:
																SCREAMING_SNAKE_CASE__								=      len(__UpperCAmelCase       ) - len(encoded_inputs["""global_attention_mask"""]       )

																if self.padding_side == "right":
																			# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																			SCREAMING_SNAKE_CASE__								=      (
																			    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																			)
																elif self.padding_side == "left":
																			SCREAMING_SNAKE_CASE__								=      [-1] * difference + encoded_inputs[
																			    """global_attention_mask"""
																			]
																else:
																			raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

										return encoded_inputs





"""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()


