# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


UpperCAmelCase_			    =							{
    '''Acehnese Arabic''': '''ace_Arab''',
    '''Acehnese Latin''': '''ace_Latn''',
    '''Mesopotamian Arabic''': '''acm_Arab''',
    '''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
    '''Tunisian Arabic''': '''aeb_Arab''',
    '''Afrikaans''': '''afr_Latn''',
    '''South Levantine Arabic''': '''ajp_Arab''',
    '''Akan''': '''aka_Latn''',
    '''Amharic''': '''amh_Ethi''',
    '''North Levantine Arabic''': '''apc_Arab''',
    '''Modern Standard Arabic''': '''arb_Arab''',
    '''Modern Standard Arabic Romanized''': '''arb_Latn''',
    '''Najdi Arabic''': '''ars_Arab''',
    '''Moroccan Arabic''': '''ary_Arab''',
    '''Egyptian Arabic''': '''arz_Arab''',
    '''Assamese''': '''asm_Beng''',
    '''Asturian''': '''ast_Latn''',
    '''Awadhi''': '''awa_Deva''',
    '''Central Aymara''': '''ayr_Latn''',
    '''South Azerbaijani''': '''azb_Arab''',
    '''North Azerbaijani''': '''azj_Latn''',
    '''Bashkir''': '''bak_Cyrl''',
    '''Bambara''': '''bam_Latn''',
    '''Balinese''': '''ban_Latn''',
    '''Belarusian''': '''bel_Cyrl''',
    '''Bemba''': '''bem_Latn''',
    '''Bengali''': '''ben_Beng''',
    '''Bhojpuri''': '''bho_Deva''',
    '''Banjar Arabic''': '''bjn_Arab''',
    '''Banjar Latin''': '''bjn_Latn''',
    '''Standard Tibetan''': '''bod_Tibt''',
    '''Bosnian''': '''bos_Latn''',
    '''Buginese''': '''bug_Latn''',
    '''Bulgarian''': '''bul_Cyrl''',
    '''Catalan''': '''cat_Latn''',
    '''Cebuano''': '''ceb_Latn''',
    '''Czech''': '''ces_Latn''',
    '''Chokwe''': '''cjk_Latn''',
    '''Central Kurdish''': '''ckb_Arab''',
    '''Crimean Tatar''': '''crh_Latn''',
    '''Welsh''': '''cym_Latn''',
    '''Danish''': '''dan_Latn''',
    '''German''': '''deu_Latn''',
    '''Southwestern Dinka''': '''dik_Latn''',
    '''Dyula''': '''dyu_Latn''',
    '''Dzongkha''': '''dzo_Tibt''',
    '''Greek''': '''ell_Grek''',
    '''English''': '''eng_Latn''',
    '''Esperanto''': '''epo_Latn''',
    '''Estonian''': '''est_Latn''',
    '''Basque''': '''eus_Latn''',
    '''Ewe''': '''ewe_Latn''',
    '''Faroese''': '''fao_Latn''',
    '''Fijian''': '''fij_Latn''',
    '''Finnish''': '''fin_Latn''',
    '''Fon''': '''fon_Latn''',
    '''French''': '''fra_Latn''',
    '''Friulian''': '''fur_Latn''',
    '''Nigerian Fulfulde''': '''fuv_Latn''',
    '''Scottish Gaelic''': '''gla_Latn''',
    '''Irish''': '''gle_Latn''',
    '''Galician''': '''glg_Latn''',
    '''Guarani''': '''grn_Latn''',
    '''Gujarati''': '''guj_Gujr''',
    '''Haitian Creole''': '''hat_Latn''',
    '''Hausa''': '''hau_Latn''',
    '''Hebrew''': '''heb_Hebr''',
    '''Hindi''': '''hin_Deva''',
    '''Chhattisgarhi''': '''hne_Deva''',
    '''Croatian''': '''hrv_Latn''',
    '''Hungarian''': '''hun_Latn''',
    '''Armenian''': '''hye_Armn''',
    '''Igbo''': '''ibo_Latn''',
    '''Ilocano''': '''ilo_Latn''',
    '''Indonesian''': '''ind_Latn''',
    '''Icelandic''': '''isl_Latn''',
    '''Italian''': '''ita_Latn''',
    '''Javanese''': '''jav_Latn''',
    '''Japanese''': '''jpn_Jpan''',
    '''Kabyle''': '''kab_Latn''',
    '''Jingpho''': '''kac_Latn''',
    '''Kamba''': '''kam_Latn''',
    '''Kannada''': '''kan_Knda''',
    '''Kashmiri Arabic''': '''kas_Arab''',
    '''Kashmiri Devanagari''': '''kas_Deva''',
    '''Georgian''': '''kat_Geor''',
    '''Central Kanuri Arabic''': '''knc_Arab''',
    '''Central Kanuri Latin''': '''knc_Latn''',
    '''Kazakh''': '''kaz_Cyrl''',
    '''Kabiyè''': '''kbp_Latn''',
    '''Kabuverdianu''': '''kea_Latn''',
    '''Khmer''': '''khm_Khmr''',
    '''Kikuyu''': '''kik_Latn''',
    '''Kinyarwanda''': '''kin_Latn''',
    '''Kyrgyz''': '''kir_Cyrl''',
    '''Kimbundu''': '''kmb_Latn''',
    '''Northern Kurdish''': '''kmr_Latn''',
    '''Kikongo''': '''kon_Latn''',
    '''Korean''': '''kor_Hang''',
    '''Lao''': '''lao_Laoo''',
    '''Ligurian''': '''lij_Latn''',
    '''Limburgish''': '''lim_Latn''',
    '''Lingala''': '''lin_Latn''',
    '''Lithuanian''': '''lit_Latn''',
    '''Lombard''': '''lmo_Latn''',
    '''Latgalian''': '''ltg_Latn''',
    '''Luxembourgish''': '''ltz_Latn''',
    '''Luba-Kasai''': '''lua_Latn''',
    '''Ganda''': '''lug_Latn''',
    '''Luo''': '''luo_Latn''',
    '''Mizo''': '''lus_Latn''',
    '''Standard Latvian''': '''lvs_Latn''',
    '''Magahi''': '''mag_Deva''',
    '''Maithili''': '''mai_Deva''',
    '''Malayalam''': '''mal_Mlym''',
    '''Marathi''': '''mar_Deva''',
    '''Minangkabau Arabic ''': '''min_Arab''',
    '''Minangkabau Latin''': '''min_Latn''',
    '''Macedonian''': '''mkd_Cyrl''',
    '''Plateau Malagasy''': '''plt_Latn''',
    '''Maltese''': '''mlt_Latn''',
    '''Meitei Bengali''': '''mni_Beng''',
    '''Halh Mongolian''': '''khk_Cyrl''',
    '''Mossi''': '''mos_Latn''',
    '''Maori''': '''mri_Latn''',
    '''Burmese''': '''mya_Mymr''',
    '''Dutch''': '''nld_Latn''',
    '''Norwegian Nynorsk''': '''nno_Latn''',
    '''Norwegian Bokmål''': '''nob_Latn''',
    '''Nepali''': '''npi_Deva''',
    '''Northern Sotho''': '''nso_Latn''',
    '''Nuer''': '''nus_Latn''',
    '''Nyanja''': '''nya_Latn''',
    '''Occitan''': '''oci_Latn''',
    '''West Central Oromo''': '''gaz_Latn''',
    '''Odia''': '''ory_Orya''',
    '''Pangasinan''': '''pag_Latn''',
    '''Eastern Panjabi''': '''pan_Guru''',
    '''Papiamento''': '''pap_Latn''',
    '''Western Persian''': '''pes_Arab''',
    '''Polish''': '''pol_Latn''',
    '''Portuguese''': '''por_Latn''',
    '''Dari''': '''prs_Arab''',
    '''Southern Pashto''': '''pbt_Arab''',
    '''Ayacucho Quechua''': '''quy_Latn''',
    '''Romanian''': '''ron_Latn''',
    '''Rundi''': '''run_Latn''',
    '''Russian''': '''rus_Cyrl''',
    '''Sango''': '''sag_Latn''',
    '''Sanskrit''': '''san_Deva''',
    '''Santali''': '''sat_Olck''',
    '''Sicilian''': '''scn_Latn''',
    '''Shan''': '''shn_Mymr''',
    '''Sinhala''': '''sin_Sinh''',
    '''Slovak''': '''slk_Latn''',
    '''Slovenian''': '''slv_Latn''',
    '''Samoan''': '''smo_Latn''',
    '''Shona''': '''sna_Latn''',
    '''Sindhi''': '''snd_Arab''',
    '''Somali''': '''som_Latn''',
    '''Southern Sotho''': '''sot_Latn''',
    '''Spanish''': '''spa_Latn''',
    '''Tosk Albanian''': '''als_Latn''',
    '''Sardinian''': '''srd_Latn''',
    '''Serbian''': '''srp_Cyrl''',
    '''Swati''': '''ssw_Latn''',
    '''Sundanese''': '''sun_Latn''',
    '''Swedish''': '''swe_Latn''',
    '''Swahili''': '''swh_Latn''',
    '''Silesian''': '''szl_Latn''',
    '''Tamil''': '''tam_Taml''',
    '''Tatar''': '''tat_Cyrl''',
    '''Telugu''': '''tel_Telu''',
    '''Tajik''': '''tgk_Cyrl''',
    '''Tagalog''': '''tgl_Latn''',
    '''Thai''': '''tha_Thai''',
    '''Tigrinya''': '''tir_Ethi''',
    '''Tamasheq Latin''': '''taq_Latn''',
    '''Tamasheq Tifinagh''': '''taq_Tfng''',
    '''Tok Pisin''': '''tpi_Latn''',
    '''Tswana''': '''tsn_Latn''',
    '''Tsonga''': '''tso_Latn''',
    '''Turkmen''': '''tuk_Latn''',
    '''Tumbuka''': '''tum_Latn''',
    '''Turkish''': '''tur_Latn''',
    '''Twi''': '''twi_Latn''',
    '''Central Atlas Tamazight''': '''tzm_Tfng''',
    '''Uyghur''': '''uig_Arab''',
    '''Ukrainian''': '''ukr_Cyrl''',
    '''Umbundu''': '''umb_Latn''',
    '''Urdu''': '''urd_Arab''',
    '''Northern Uzbek''': '''uzn_Latn''',
    '''Venetian''': '''vec_Latn''',
    '''Vietnamese''': '''vie_Latn''',
    '''Waray''': '''war_Latn''',
    '''Wolof''': '''wol_Latn''',
    '''Xhosa''': '''xho_Latn''',
    '''Eastern Yiddish''': '''ydd_Hebr''',
    '''Yoruba''': '''yor_Latn''',
    '''Yue Chinese''': '''yue_Hant''',
    '''Chinese Simplified''': '''zho_Hans''',
    '''Chinese Traditional''': '''zho_Hant''',
    '''Standard Malay''': '''zsm_Latn''',
    '''Zulu''': '''zul_Latn''',
}




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    List[Any]  			=   '''facebook/nllb-200-distilled-600M'''
   lowerCAmelCase      :    List[str]  			=   (
       '''This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '''
       '''be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '''
       '''which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '''
       '''plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'''
   )
   lowerCAmelCase      :    List[Any]  			=   '''translator'''
   lowerCAmelCase      :    Dict  			=   AutoTokenizer
   lowerCAmelCase      :    Dict  			=   AutoModelForSeqaSeqLM
   lowerCAmelCase      :    str  			=   LANGUAGE_CODES

   lowerCAmelCase      :    Tuple  			=   ['''text''', '''text''', '''text''']
   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  str		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  int							):



    """simple docstring"""



    if src_lang not in self.lang_to_code:
     raise ValueError(f"""{src_lang} is not a supported language."""							)
    if tgt_lang not in self.lang_to_code:
     raise ValueError(f"""{tgt_lang} is not a supported language."""							)
    _UpperCamelCase:     Any							= self.lang_to_code[src_lang]
    _UpperCamelCase:     str							= self.lang_to_code[tgt_lang]
    return self.pre_processor._build_translation_inputs(
        _lowercase		,  return_tensors='''pt'''		,  src_lang=_lowercase		,  tgt_lang=_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.model.generate(**_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    return self.post_processor.decode(outputs[0].tolist()		,  skip_special_tokens=_lowercase							)
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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''')

UpperCAmelCase_			    =							list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase_			    =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class  __magic_name__  :





   """simple docstring"""




   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. If not set, will try to use \'image\' or \'img\'.'''}      ,	)
   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      :    int  			=   field(default=3_2      ,	metadata={'''help''': '''The size of the square patches to use for masking.'''}    )
   lowerCAmelCase      :    float  			=   field(
       default=0.6      ,	metadata={'''help''': '''Percentage of patches to mask.'''}      ,	)
   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   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= {}
    if self.train_dir is not None:
     _UpperCamelCase:     Tuple							= self.train_dir
    if self.validation_dir is not None:
     _UpperCamelCase:     Dict							= self.validation_dir
    _UpperCamelCase:     Any							= data_files if data_files else None




@dataclass
class  __magic_name__  :





   """simple docstring"""




   lowerCAmelCase      :    str  			=   field(
       default=__a      ,	metadata={
           '''help''': (
               '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
               '''checkpoint identifier on the hub. '''
               '''Don\'t set if you want to train a model from scratch.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[str]  			=   field(
       default=__a      ,	metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__a    )}      ,	)
   lowerCAmelCase      :    Optional[str]  			=   field(
       default=__a      ,	metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
   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 (cache) the pretrained models/datasets downloaded from the hub'''}      ,	)
   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      :    Optional[int]  			=   field(
       default=__a      ,	metadata={
           '''help''': (
               '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   field(
       default=__a      ,	metadata={
           '''help''': (
               '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
           )
       }      ,	)
   lowerCAmelCase      :    Optional[int]  			=   field(
       default=__a      ,	metadata={'''help''': '''Stride to use for the encoder.'''}      ,	)




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  str=192		,  _lowercase					:  Optional[Any]=32		,  _lowercase					:  str=4		,  _lowercase					:  Union[str, Any]=0.6							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= input_size
    _UpperCamelCase:     int							= mask_patch_size
    _UpperCamelCase:     Tuple							= model_patch_size
    _UpperCamelCase:     List[str]							= mask_ratio

    if self.input_size % self.mask_patch_size != 0:
     raise ValueError('''Input size must be divisible by mask patch size'''							)
    if self.mask_patch_size % self.model_patch_size != 0:
     raise ValueError('''Mask patch size must be divisible by model patch size'''							)

    _UpperCamelCase:     str							= self.input_size // self.mask_patch_size
    _UpperCamelCase:     Optional[Any]							= self.mask_patch_size // self.model_patch_size

    _UpperCamelCase:     Dict							= self.rand_size**2
    _UpperCamelCase:     int							= int(np.ceil(self.token_count * self.mask_ratio							)							)



   def __call__(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= np.random.permutation(self.token_count							)[: self.mask_count]
    _UpperCamelCase:     int							= np.zeros(self.token_count		,  dtype=_lowercase							)
    _UpperCamelCase:     Optional[Any]							= 1

    _UpperCamelCase:     List[Any]							= mask.reshape((self.rand_size, self.rand_size)							)
    _UpperCamelCase:     Tuple							= mask.repeat(self.scale		,  axis=0							).repeat(self.scale		,  axis=1							)

    return torch.tensor(mask.flatten()							)




def       lowerCAmelCase_     (		lowercase:							Union[str, Any]		)    ->				Tuple:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.stack([example['''pixel_values'''] for example in examples]		)
 _UpperCamelCase:     List[str]							= torch.stack([example['''mask'''] for example in examples]		)
 return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def       lowerCAmelCase_     (		)    ->				Union[str, Any]:







 '''simple docstring'''
 # 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:     Any							= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
 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:     List[str]							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  _UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase:     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_mim'''  ,					lowercase  ,					lowercase		)

 # 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:     Union[str, Any]							= training_args.get_process_log_level()
 logger.setLevel(lowercase		)
 transformers.utils.logging.set_verbosity(lowercase		)
 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:     Any							= None
 if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
  _UpperCamelCase:     Optional[Any]							= 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:     Union[str, 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.
 _UpperCamelCase:     Union[str, Any]							= None if '''validation''' in ds.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split  ,					lowercase		) and data_args.train_val_split > 0.0:
  _UpperCamelCase:     Any							= ds['''train'''].train_test_split(data_args.train_val_split		)
  _UpperCamelCase:     Dict							= split['''train''']
  _UpperCamelCase:     Any							= split['''test''']

 # Create config
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 _UpperCamelCase:     Tuple							= {
     '''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_or_path:
  _UpperCamelCase:     Tuple							= AutoConfig.from_pretrained(model_args.config_name_or_path  ,					**lowercase		)
 elif model_args.model_name_or_path:
  _UpperCamelCase:     str							= AutoConfig.from_pretrained(model_args.model_name_or_path  ,					**lowercase		)
 else:
  _UpperCamelCase:     Union[str, Any]							= CONFIG_MAPPING[model_args.model_type]()
  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}"""		)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
 if hasattr(lowercase  ,					'''decoder_type'''		):
  _UpperCamelCase:     str							= '''simmim'''

 # adapt config
 _UpperCamelCase:     Tuple							= model_args.image_size if model_args.image_size is not None else config.image_size
 _UpperCamelCase:     Union[str, Any]							= model_args.patch_size if model_args.patch_size is not None else config.patch_size
 _UpperCamelCase:     List[str]							= (
     model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
 )

 config.update(
     {
         '''image_size''': model_args.image_size,
         '''patch_size''': model_args.patch_size,
         '''encoder_stride''': model_args.encoder_stride,
     }		)

 # create image processor
 if model_args.image_processor_name:
  _UpperCamelCase:     Dict							= AutoImageProcessor.from_pretrained(model_args.image_processor_name  ,					**lowercase		)
 elif model_args.model_name_or_path:
  _UpperCamelCase:     int							= AutoImageProcessor.from_pretrained(model_args.model_name_or_path  ,					**lowercase		)
 else:
  _UpperCamelCase:     Dict							= {
      conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
  }
  _UpperCamelCase:     Tuple							= IMAGE_PROCESSOR_TYPES[model_args.model_type]()

 # create model
 if model_args.model_name_or_path:
  _UpperCamelCase:     Optional[int]							= AutoModelForMaskedImageModeling.from_pretrained(
      model_args.model_name_or_path  ,					from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)  ,					config=lowercase  ,					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:     Any							= AutoModelForMaskedImageModeling.from_config(lowercase		)

 if training_args.do_train:
  _UpperCamelCase:     Tuple							= ds['''train'''].column_names
 else:
  _UpperCamelCase:     Tuple							= ds['''validation'''].column_names

 if data_args.image_column_name is not None:
  _UpperCamelCase:     Optional[Any]							= data_args.image_column_name
 elif "image" in column_names:
  _UpperCamelCase:     Optional[int]							= '''image'''
 elif "img" in column_names:
  _UpperCamelCase:     Optional[Any]							= '''img'''
 else:
  _UpperCamelCase:     Union[str, Any]							= column_names[0]

 # transformations as done in original SimMIM paper
 # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
 _UpperCamelCase:     Dict							= Compose(
     [
         Lambda(lambda lowercase		: img.convert('''RGB'''		) if img.mode != "RGB" else img		),
         RandomResizedCrop(model_args.image_size  ,					scale=(0.67, 1.0)  ,					ratio=(3.0 / 4.0, 4.0 / 3.0)		),
         RandomHorizontalFlip(),
         ToTensor(),
         Normalize(mean=image_processor.image_mean  ,					std=image_processor.image_std		),
     ]		)

 # create mask generator
 _UpperCamelCase:     Tuple							= MaskGenerator(
     input_size=model_args.image_size  ,					mask_patch_size=data_args.mask_patch_size  ,					model_patch_size=model_args.patch_size  ,					mask_ratio=data_args.mask_ratio  ,					)

 def preprocess_images(lowercase:							Dict		):
  _UpperCamelCase:     Optional[Any]							= [transforms(lowercase		) for image in examples[image_column_name]]
  _UpperCamelCase:     Optional[int]							= [mask_generator() for i in range(len(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:     Tuple							= ds['''train'''].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
  # Set the training transforms
  ds["train"].set_transform(lowercase		)

 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:     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(lowercase		)

 # Initialize our trainer
 _UpperCamelCase:     Union[str, Any]							= Trainer(
     model=lowercase  ,					args=lowercase  ,					train_dataset=ds['''train'''] if training_args.do_train else None  ,					eval_dataset=ds['''validation'''] if training_args.do_eval else None  ,					tokenizer=lowercase  ,					data_collator=lowercase  ,					)

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

 # Write model card and (optionally) push to hub
 _UpperCamelCase:     Union[str, Any]							= {
     '''finetuned_from''': model_args.model_name_or_path,
     '''tasks''': '''masked-image-modeling''',
     '''dataset''': data_args.dataset_name,
     '''tags''': ['''masked-image-modeling'''],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**lowercase		)
 else:
  trainer.create_model_card(**lowercase		)


if __name__ == "__main__":
  main()