


import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase       			=						get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCAmelCase       			=						get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCAmelCase       			=						'''pt''' if is_torch_available() else '''tf'''





@require_sentencepiece
@require_tokenizers
class  A       (  _a   ,							unittest.TestCase					):
							UpperCamelCase_  :				List[Any]			=CamembertTokenizer
							UpperCamelCase_  :				int			=CamembertTokenizerFast
							UpperCamelCase_  :				List[Any]			=True
							UpperCamelCase_  :				Union[str, Any]			=True


							def      _A			(self   ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									__lowercase=     CamembertTokenizer(snake_case_   )
									tokenizer.save_pretrained(self.tmpdirname   )


							def      _A			(self   ):
									__lowercase=     """<pad>"""
									__lowercase=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_   ) ,							snake_case_   )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_   ) ,							snake_case_   )


							def      _A			(self   ):
									__lowercase=     list(self.get_tokenizer().get_vocab().keys()   )

									self.assertEqual(vocab_keys[0] ,							'<s>NOTUSED'   )
									self.assertEqual(vocab_keys[1] ,							'<pad>'   )
									self.assertEqual(vocab_keys[-1] ,							'<mask>'   )
									self.assertEqual(len(snake_case_   ) ,							1_0_0_4   )


							def      _A			(self   ):
									self.assertEqual(self.get_tokenizer().vocab_size ,							1_0_0_5   )


							def      _A			(self   ):
									__lowercase=     CamembertTokenizer(snake_case_   )
									tokenizer.save_pretrained(self.tmpdirname   )
									__lowercase=     CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

									__lowercase=     """I was born in 92000, and this is falsé."""

									__lowercase=     tokenizer.encode(snake_case_   )
									__lowercase=     rust_tokenizer.encode(snake_case_   )
									self.assertListEqual(snake_case_ ,							snake_case_   )

									__lowercase=     tokenizer.encode(snake_case_ ,							add_special_tokens=snake_case_   )
									__lowercase=     rust_tokenizer.encode(snake_case_ ,							add_special_tokens=snake_case_   )
									self.assertListEqual(snake_case_ ,							snake_case_   )

									# <unk> tokens are not the same for `rust` than for `slow`.
									# Because spm gives back raw token instead of `unk` in EncodeAsPieces
									# tokens = tokenizer.tokenize(sequence)
									__lowercase=     tokenizer.convert_ids_to_tokens(snake_case_   )
									__lowercase=     rust_tokenizer.tokenize(snake_case_   )
									self.assertListEqual(snake_case_ ,							snake_case_   )


							def      _A			(self   ):
									if not self.test_rust_tokenizer:
											return

									__lowercase=     self.get_tokenizer()
									__lowercase=     self.get_rust_tokenizer()

									__lowercase=     """I was born in 92000, and this is falsé."""

									__lowercase=     tokenizer.tokenize(snake_case_   )
									__lowercase=     rust_tokenizer.tokenize(snake_case_   )
									self.assertListEqual(snake_case_ ,							snake_case_   )

									__lowercase=     tokenizer.encode(snake_case_ ,							add_special_tokens=snake_case_   )
									__lowercase=     rust_tokenizer.encode(snake_case_ ,							add_special_tokens=snake_case_   )
									self.assertListEqual(snake_case_ ,							snake_case_   )

									__lowercase=     self.get_rust_tokenizer()
									__lowercase=     tokenizer.encode(snake_case_   )
									__lowercase=     rust_tokenizer.encode(snake_case_   )
									self.assertListEqual(snake_case_ ,							snake_case_   )







							@slow
							def      _A			(self   ):
									# fmt: off
									__lowercase=     {"""input_ids""": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# camembert is a french model. So we also use french texts.
									__lowercase=     [
									    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
									    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
									    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
									    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
									    """telles que la traduction et la synthèse de texte.""",
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=snake_case_ ,							model_name='camembert-base' ,							revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' ,							sequences=snake_case_ ,							)











'''simple docstring'''





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
__a  			=						logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

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







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						field(
        default="cifar10"						,						metadata={"help": "Name of a dataset from the datasets package"}      )
    lowercase   =						field(
        default=_a						,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
    lowercase   =						field(
        default=_a						,						metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}						,						)
    lowercase   =						field(default=_a						,						metadata={"help": "A folder containing the training data."}      )
    lowercase   =						field(default=_a						,						metadata={"help": "A folder containing the validation data."}      )
    lowercase   =						field(
        default=0.15						,						metadata={"help": "Percent to split off of train for validation."}      )
    lowercase   =						field(default=32						,						metadata={"help": "The size of the square patches to use for masking."}      )
    lowercase   =						field(
        default=0.6						,						metadata={"help": "Percentage of patches to mask."}						,						)
    lowercase   =						field(
        default=_a						,						metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }						,						)
    lowercase   =						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   :	Any		):
          snake_case__    :       str       		= {}
          if self.train_dir is not None:
                snake_case__    :       Optional[Any]       		= self.train_dir
          if self.validation_dir is not None:
                snake_case__    :       Union[str, Any]       		= self.validation_dir
          snake_case__    :       Union[str, Any]       		= data_files if data_files else None







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						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."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_a      )}						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
    lowercase   =						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"
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}						,						)
    lowercase   =						field(
        default="main"						,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,						)
    lowercase   =						field(default=_a						,						metadata={"help": "Name or path of preprocessor config."}      )
    lowercase   =						field(
        default=_a						,						metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={
            "help": (
                "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={
            "help": (
                "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Stride to use for the encoder."}						,						)







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	str=192					,	snake_case_   :	Tuple=32					,	snake_case_   :	List[str]=4					,	snake_case_   :	Tuple=0.6		):
          snake_case__    :       int       		= input_size
          snake_case__    :       int       		= mask_patch_size
          snake_case__    :       List[Any]       		= model_patch_size
          snake_case__    :       int       		= 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"""		)

          snake_case__    :       List[Any]       		= self.input_size // self.mask_patch_size
          snake_case__    :       int       		= self.mask_patch_size // self.model_patch_size

          snake_case__    :       List[str]       		= self.rand_size**2
          snake_case__    :       int       		= int(np.ceil(self.token_count * self.mask_ratio		)		)




    def __call__(       self   :	str		):
          snake_case__    :       Optional[int]       		= np.random.permutation(self.token_count		)[: self.mask_count]
          snake_case__    :       str       		= np.zeros(self.token_count					,	dtype=snake_case_		)
          snake_case__    :       Union[str, Any]       		= 1

          snake_case__    :       List[str]       		= mask.reshape((self.rand_size, self.rand_size)		)
          snake_case__    :       str       		= mask.repeat(self.scale					,	axis=0		).repeat(self.scale					,	axis=1		)

          return torch.tensor(mask.flatten()		)





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       List[str]       		= torch.stack([example["""pixel_values"""] for example in examples]						)
      snake_case__    :       Optional[int]       		= torch.stack([example["""mask"""] for example in examples]						)
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def 		__snake_case(							)						->					Optional[Any]:
      # 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.

      snake_case__    :       Optional[int]       		= 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.
            snake_case__		,							snake_case__		,							snake_case__    :       Optional[int]       		= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
      else:
            snake_case__		,							snake_case__		,							snake_case__    :       str       		= 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"""	,  _lowerCAmelCase	,  _lowerCAmelCase						)

      # Setup logging
      logging.basicConfig(
          format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,  datefmt="""%m/%d/%Y %H:%M:%S"""	,  handlers=[logging.StreamHandler(sys.stdout						)]	,  )

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      snake_case__    :       int       		= training_args.get_process_log_level()
      logger.setLevel(_lowerCAmelCase						)
      transformers.utils.logging.set_verbosity(_lowerCAmelCase						)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}"						)
      logger.info(f"Training/evaluation parameters {training_args}"						)

      # Detecting last checkpoint.
      snake_case__    :       Optional[Any]       		= None
      if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
            snake_case__    :       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.
      snake_case__    :       Dict       		= 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.
      snake_case__    :       Tuple       		= None if """validation""" in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split	,  _lowerCAmelCase						) and data_args.train_val_split > 0.0:
            snake_case__    :       Optional[Any]       		= ds["""train"""].train_test_split(data_args.train_val_split						)
            snake_case__    :       Optional[int]       		= split["""train"""]
            snake_case__    :       Any       		= split["""test"""]

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      snake_case__    :       List[str]       		= {
          """cache_dir""": model_args.cache_dir,
          """revision""": model_args.model_revision,
          """use_auth_token""": True if model_args.use_auth_token else None,
      }
      if model_args.config_name_or_path:
            snake_case__    :       str       		= AutoConfig.from_pretrained(model_args.config_name_or_path	,  **_lowerCAmelCase						)
      elif model_args.model_name_or_path:
            snake_case__    :       Tuple       		= AutoConfig.from_pretrained(model_args.model_name_or_path	,  **_lowerCAmelCase						)
      else:
            snake_case__    :       List[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(_lowerCAmelCase	,  """decoder_type"""						):
            snake_case__    :       Optional[int]       		= """simmim"""

      # adapt config
      snake_case__    :       Dict       		= model_args.image_size if model_args.image_size is not None else config.image_size
      snake_case__    :       Dict       		= model_args.patch_size if model_args.patch_size is not None else config.patch_size
      snake_case__    :       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:
            snake_case__    :       str       		= AutoImageProcessor.from_pretrained(model_args.image_processor_name	,  **_lowerCAmelCase						)
      elif model_args.model_name_or_path:
            snake_case__    :       Union[str, Any]       		= AutoImageProcessor.from_pretrained(model_args.model_name_or_path	,  **_lowerCAmelCase						)
      else:
            snake_case__    :       Tuple       		= {
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            snake_case__    :       Optional[Any]       		= IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            snake_case__    :       str       		= AutoModelForMaskedImageModeling.from_pretrained(
                model_args.model_name_or_path	,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)	,  config=_lowerCAmelCase	,  cache_dir=model_args.cache_dir	,  revision=model_args.model_revision	,  use_auth_token=True if model_args.use_auth_token else None	,  )
      else:
            logger.info("""Training new model from scratch"""						)
            snake_case__    :       Any       		= AutoModelForMaskedImageModeling.from_config(_lowerCAmelCase						)

      if training_args.do_train:
            snake_case__    :       Tuple       		= ds["""train"""].column_names
      else:
            snake_case__    :       Any       		= ds["""validation"""].column_names

      if data_args.image_column_name is not None:
            snake_case__    :       List[Any]       		= data_args.image_column_name
      elif "image" in column_names:
            snake_case__    :       int       		= """image"""
      elif "img" in column_names:
            snake_case__    :       Any       		= """img"""
      else:
            snake_case__    :       Dict       		= column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      snake_case__    :       Optional[int]       		= Compose(
          [
              Lambda(lambda _lowerCAmelCase						: 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
      snake_case__    :       List[str]       		= 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(_lowerCAmelCase						):
            snake_case__    :       List[Any]       		= [transforms(_lowerCAmelCase						) for image in examples[image_column_name]]
            snake_case__    :       List[str]       		= [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:
                  snake_case__    :       Optional[Any]       		= ds["""train"""].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
            # Set the training transforms
            ds["train"].set_transform(_lowerCAmelCase						)

      if training_args.do_eval:
            if "validation" not in ds:
                  raise ValueError("""--do_eval requires a validation dataset"""						)
            if data_args.max_eval_samples is not None:
                  snake_case__    :       Tuple       		= (
                      ds["""validation"""].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(_lowerCAmelCase						)

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

      # Training
      if training_args.do_train:
            snake_case__    :       str       		= None
            if training_args.resume_from_checkpoint is not None:
                  snake_case__    :       List[Any]       		= training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  snake_case__    :       List[str]       		= last_checkpoint
            snake_case__    :       Dict       		= trainer.train(resume_from_checkpoint=_lowerCAmelCase						)
            trainer.save_model()
            trainer.log_metrics("""train"""	,  train_result.metrics						)
            trainer.save_metrics("""train"""	,  train_result.metrics						)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            snake_case__    :       List[Any]       		= trainer.evaluate()
            trainer.log_metrics("""eval"""	,  _lowerCAmelCase						)
            trainer.save_metrics("""eval"""	,  _lowerCAmelCase						)

      # Write model card and (optionally) push to hub
      snake_case__    :       Optional[int]       		= {
          """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(**_lowerCAmelCase						)
      else:
            trainer.create_model_card(**_lowerCAmelCase						)


if __name__ == "__main__":
       main()
