





import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


SCREAMING_SNAKE_CASE_		     = get_logger(__name__)


SCREAMING_SNAKE_CASE_		     = r"""\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"""

class 	UpperCamelCase__				:



     '''simple docstring'''

     @add_start_docstrings(_a )
     def __call__(							self		:					List[str]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )

class 	UpperCamelCase__				:



     '''simple docstring'''

     @add_start_docstrings(_a )
     def __call__(							self		:					int		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     @add_start_docstrings(_a )
     def __call__(							self		:					Any		,lowerCamelCase__		:					int		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Tuple		,**lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           for processor in self:
                 SCREAMING_SNAKE_CASE	=		inspect.signature(processor.__call__ ).parameters
                 if len(_a ) > 3:
                       if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
                             raise ValueError(
                                 F"""Make sure that all the required parameters: {list(function_args.keys() )} for """
                                 F"""{processor.__class__} are passed to the logits processor.""" )
                       SCREAMING_SNAKE_CASE	=		processor(_a		,_a		,_a		,**_a )
                 else:
                       SCREAMING_SNAKE_CASE	=		processor(_a		,_a		,_a )
           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					Optional[int]		,lowerCamelCase__		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           if not isinstance(_a		,_a ) or not (temperature > 0):
                 raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}""" )

           SCREAMING_SNAKE_CASE	=		temperature






     def __call__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Any )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		scores / self.temperature
           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Union[str, Any] = -float("""Inf""" )		,lowerCamelCase__		:					Optional[Any] = 1 )  ->	Optional[Any]:




           '''simple docstring'''


           if not isinstance(_a		,_a ) or (top_p < 0 or top_p > 1.0):
                 raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}""" )
           if not isinstance(_a		,_a ) or (min_tokens_to_keep < 1):
                 raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}""" )

           SCREAMING_SNAKE_CASE	=		top_p
           SCREAMING_SNAKE_CASE	=		filter_value
           SCREAMING_SNAKE_CASE	=		min_tokens_to_keep






     def __call__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		lax.top_k(_a		,scores.shape[-1] )

           SCREAMING_SNAKE_CASE	=		jnp.full_like(_a		,self.filter_value )
           SCREAMING_SNAKE_CASE	=		jax.nn.softmax(_a		,axis=-1 ).cumsum(axis=-1 )
           SCREAMING_SNAKE_CASE	=		cumulative_probs < self.top_p

           # include the token that is higher than top_p as well
           SCREAMING_SNAKE_CASE	=		jnp.roll(_a		,1 )
           score_mask |= score_mask.at[:, 0].set(_a )

           # min tokens to keep
           SCREAMING_SNAKE_CASE	=		score_mask.at[:, : self.min_tokens_to_keep].set(_a )

           SCREAMING_SNAKE_CASE	=		jnp.where(_a		,_a		,_a )
           SCREAMING_SNAKE_CASE	=		jax.lax.sort_key_val(_a		,_a )[-1]

           return next_scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Optional[int] = -float("""Inf""" )		,lowerCamelCase__		:					Dict = 1 )  ->	List[str]:




           '''simple docstring'''


           if not isinstance(_a		,_a ) or top_k <= 0:
                 raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}""" )

           SCREAMING_SNAKE_CASE	=		max(_a		,_a )
           SCREAMING_SNAKE_CASE	=		filter_value






     def __call__(							self		:					List[str]		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		scores.shape
           SCREAMING_SNAKE_CASE	=		jnp.full(batch_size * vocab_size		,self.filter_value )

           SCREAMING_SNAKE_CASE	=		min(self.top_k		,scores.shape[-1] )  # Safety check
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		lax.top_k(_a		,_a )
           SCREAMING_SNAKE_CASE	=		jnp.broadcast_to((jnp.arange(_a ) * vocab_size)[:, None]		,(batch_size, topk) ).flatten()
           SCREAMING_SNAKE_CASE	=		topk_scores.flatten()
           SCREAMING_SNAKE_CASE	=		topk_indices.flatten() + shift

           SCREAMING_SNAKE_CASE	=		next_scores_flat.at[topk_indices_flat].set(_a )
           SCREAMING_SNAKE_CASE	=		next_scores_flat.reshape(_a		,_a )
           return next_scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					List[Any] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		bos_token_id






     def __call__(							self		:					List[str]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					List[str] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		jnp.full(scores.shape		,-float("""inf""" ) )

           SCREAMING_SNAKE_CASE	=		1 - jnp.bool_(cur_len - 1 )

           SCREAMING_SNAKE_CASE	=		jnp.where(_a		,new_scores.at[:, self.bos_token_id].set(0 )		,_a )

           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		max_length
           SCREAMING_SNAKE_CASE	=		eos_token_id






     def __call__(							self		:					Optional[int]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Optional[int] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		jnp.full(scores.shape		,-float("""inf""" ) )

           SCREAMING_SNAKE_CASE	=		1 - jnp.bool_(cur_len - self.max_length + 1 )

           SCREAMING_SNAKE_CASE	=		jnp.where(_a		,new_scores.at[:, self.eos_token_id].set(0 )		,_a )

           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					Any		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					List[Any] )  ->	List[str]:




           '''simple docstring'''


           if not isinstance(_a		,_a ) or min_length < 0:
                 raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}""" )

           if not isinstance(_a		,_a ) or eos_token_id < 0:
                 raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}""" )

           SCREAMING_SNAKE_CASE	=		min_length
           SCREAMING_SNAKE_CASE	=		eos_token_id






     def __call__(							self		:					Optional[Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Optional[int] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		1 - jnp.clip(cur_len - self.min_length		,0		,1 )

           SCREAMING_SNAKE_CASE	=		jnp.where(_a		,scores.at[:, self.eos_token_id].set(-float("""inf""" ) )		,_a )

           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					Dict		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[int] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		list(_a )
           SCREAMING_SNAKE_CASE	=		begin_index






     def __call__(							self		:					Tuple		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					str		,lowerCamelCase__		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		1 - jnp.bool_(cur_len - self.begin_index )

           SCREAMING_SNAKE_CASE	=		jnp.where(_a		,scores.at[:, self.begin_suppress_tokens].set(-float("""inf""" ) )		,_a )

           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		list(_a )






     def __call__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Optional[int] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		scores.at[..., self.suppress_tokens].set(-float("""inf""" ) )

           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					int		,lowerCamelCase__		:					int )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		dict(_a )
           # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
           # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
           # Indexes without forced tokens will have a negative value.
           SCREAMING_SNAKE_CASE	=		jnp.ones((max(force_token_map.keys() ) + 1)		,dtype=jnp.intaa ) * -1
           for index, token in force_token_map.items():
                 if token is not None:
                       SCREAMING_SNAKE_CASE	=		force_token_array.at[index].set(_a )
           SCREAMING_SNAKE_CASE	=		jnp.intaa(_a )






     def __call__(							self		:					List[Any]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Optional[Any] )  ->	Tuple:




           '''simple docstring'''


           def _force_token(lowerCamelCase__		:					str ):
                 SCREAMING_SNAKE_CASE	=		scores.shape[0]
                 SCREAMING_SNAKE_CASE	=		self.force_token_array[generation_idx]

                 SCREAMING_SNAKE_CASE	=		jnp.ones_like(_a		,dtype=scores.dtype ) * -float("""inf""" )
                 SCREAMING_SNAKE_CASE	=		jnp.zeros((batch_size, 1)		,dtype=scores.dtype )
                 SCREAMING_SNAKE_CASE	=		lax.dynamic_update_slice(_a		,_a		,(0, current_token) )
                 return new_scores

           SCREAMING_SNAKE_CASE	=		lax.cond(
               cur_len >= self.force_token_array.shape[0]		,lambda: scores		,lambda: lax.cond(
                   self.force_token_array[cur_len] >= 0		,lambda: _force_token(_a )		,lambda: scores		,)		,)
           return scores

class 	UpperCamelCase__				(   __SCREAMING_SNAKE_CASE     ):



     '''simple docstring'''

     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Union[str, Any] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		generate_config.eos_token_id
           SCREAMING_SNAKE_CASE	=		generate_config.no_timestamps_token_id
           SCREAMING_SNAKE_CASE	=		generate_config.no_timestamps_token_id + 1

           SCREAMING_SNAKE_CASE	=		decoder_input_length + 1

           if generate_config.is_multilingual:
                 # room for language token and task token
                 self.begin_index += 2
           if hasattr(_a		,"""max_initial_timestamp_index""" ):
                 SCREAMING_SNAKE_CASE	=		generate_config.max_initial_timestamp_index
           else:
                 SCREAMING_SNAKE_CASE	=		model_config.vocab_size
           if self.max_initial_timestamp_index is None:
                 SCREAMING_SNAKE_CASE	=		model_config.vocab_size






     def __call__(							self		:					Dict		,lowerCamelCase__		:					str		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Tuple )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		scores.at[:, self.no_timestamps_token_id].set(-float("""inf""" ) )

           def handle_pairs(lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					List[str] ):
                 SCREAMING_SNAKE_CASE	=		jnp.where((cur_len - self.begin_index) >= 1		,_a		,_a )
                 SCREAMING_SNAKE_CASE	=		jnp.where(
                     input_ids_k[cur_len - 1] >= self.timestamp_begin		,True and last_was_timestamp		,_a		,)

                 SCREAMING_SNAKE_CASE	=		jnp.where((cur_len - self.begin_index) < 2		,_a		,_a )
                 SCREAMING_SNAKE_CASE	=		jnp.where(
                     input_ids_k[cur_len - 2] >= self.timestamp_begin		,_a		,_a		,)

                 return jnp.where(
                     _a		,jnp.where(
                         penultimate_was_timestamp > 0		,scores_k.at[self.timestamp_begin :].set(-float("""inf""" ) )		,scores_k.at[: self.eos_token_id].set(-float("""inf""" ) )		,)		,_a		,)

           SCREAMING_SNAKE_CASE	=		jax.vmap(_a )(_a		,_a )

           SCREAMING_SNAKE_CASE	=		jnp.where(cur_len == self.begin_index		,_a		,_a )
           SCREAMING_SNAKE_CASE	=		jnp.where(
               self.max_initial_timestamp_index is not None		,True and apply_max_initial_timestamp		,_a		,)

           SCREAMING_SNAKE_CASE	=		self.timestamp_begin + self.max_initial_timestamp_index

           SCREAMING_SNAKE_CASE	=		jnp.where(
               _a		,scores.at[:, last_allowed + 1 :].set(-float("""inf""" ) )		,_a		,)

           # if sum of probability over timestamps is above any other token, sample timestamp
           SCREAMING_SNAKE_CASE	=		jax.nn.log_softmax(_a		,axis=-1 )

           def handle_cumulative_probs(lowerCamelCase__		:					Dict		,lowerCamelCase__		:					List[str] ):
                 SCREAMING_SNAKE_CASE	=		jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]		,axis=-1 )
                 SCREAMING_SNAKE_CASE	=		jnp.max(logprobs_k[: self.timestamp_begin] )
                 return jnp.where(
                     timestamp_logprob > max_text_token_logprob		,scores_k.at[: self.timestamp_begin].set(-float("""inf""" ) )		,_a		,)

           SCREAMING_SNAKE_CASE	=		jax.vmap(_a )(_a		,_a )

           return scores







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
SCREAMING_SNAKE_CASE_		     = 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 	UpperCamelCase__				:



     '''simple docstring'''

     __snake_case					:			Optional[str]				=    field(
         default="cifar10"							,   metadata={"help": "Name of a dataset from the datasets package"}     )
     __snake_case					:			Optional[str]				=    field(
         default=lowerCAmelCase_							,   metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}     )
     __snake_case					:			Optional[str]				=    field(
         default=lowerCAmelCase_							,   metadata={"help": "The column name of the images in the files."}     )
     __snake_case					:			Optional[str]				=    field(default=lowerCAmelCase_							,   metadata={"help": "A folder containing the training data."}     )
     __snake_case					:			Optional[str]				=    field(default=lowerCAmelCase_							,   metadata={"help": "A folder containing the validation data."}     )
     __snake_case					:			Optional[float]				=    field(
         default=0.15							,   metadata={"help": "Percent to split off of train for validation."}     )
     __snake_case					:			Optional[int]				=    field(
         default=lowerCAmelCase_							,   metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }							,   )
     __snake_case					:			Optional[int]				=    field(
         default=lowerCAmelCase_							,   metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }							,   )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	int:




           '''simple docstring'''


           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 	UpperCamelCase__				:



     '''simple docstring'''

     __snake_case					:			str				=    field(
         default=lowerCAmelCase_							,   metadata={
             "help": (
                 "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
             )
         }							,   )
     __snake_case					:			Optional[str]				=    field(
         default=lowerCAmelCase_							,   metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}     )
     __snake_case					:			Optional[str]				=    field(
         default=lowerCAmelCase_							,   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"
             )
         }							,   )
     __snake_case					:			Optional[str]				=    field(
         default=lowerCAmelCase_							,   metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}     )
     __snake_case					:			str				=    field(
         default="main"							,   metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							,   )
     __snake_case					:			str				=    field(default=lowerCAmelCase_							,   metadata={"help": "Name or path of preprocessor config."}     )
     __snake_case					:			bool				=    field(
         default=lowerCAmelCase_							,   metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }							,   )
     __snake_case					:			float				=    field(
         default=0.75							,   metadata={"help": "The ratio of the number of masked tokens in the input sequence."}     )
     __snake_case					:			bool				=    field(
         default=lowerCAmelCase_							,   metadata={"help": "Whether or not to train with normalized pixel values as target."}     )

@dataclass
class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			float				=    field(
         default=1E-3							,   metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}     )



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


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



def      __lowercase     (   )				->     List[Any]:


      '''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"""				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_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(_SCREAMING_SNAKE_CASE			)
      transformers.utils.logging.set_verbosity(_SCREAMING_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				,		_SCREAMING_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				,		**_SCREAMING_SNAKE_CASE			)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE	=		ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,		**_SCREAMING_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				,		**_SCREAMING_SNAKE_CASE			)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE	=		ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,		**_SCREAMING_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=_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				,		)
      else:
            logger.info("""Training new model from scratch"""			)
            SCREAMING_SNAKE_CASE	=		ViTMAEForPreTraining(_SCREAMING_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 _SCREAMING_SNAKE_CASE			: img.convert("""RGB"""			) if img.mode != "RGB" else img			),
              RandomResizedCrop(_SCREAMING_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(_SCREAMING_SNAKE_CASE			):
            SCREAMING_SNAKE_CASE	=		[transforms(_SCREAMING_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(_SCREAMING_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(_SCREAMING_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=_SCREAMING_SNAKE_CASE				,		args=_SCREAMING_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=_SCREAMING_SNAKE_CASE				,		data_collator=_SCREAMING_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=_SCREAMING_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"""				,		_SCREAMING_SNAKE_CASE			)
            trainer.save_metrics("""eval"""				,		_SCREAMING_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(**_SCREAMING_SNAKE_CASE			)
      else:
            trainer.create_model_card(**_SCREAMING_SNAKE_CASE			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()


