import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''

 def __init__(	self				:						Optional[Any]				,	lowerCamelCase				:						Optional[Any]				,	lowerCamelCase				:						List[str]				,	lowerCamelCase				:						Optional[int]=False				,	lowerCamelCase				:						str=1_0_2_4				,	lowerCamelCase				:						Optional[Any]=1_0_2_4				,	lowerCamelCase				:						Union[str, Any]=3.6				,	lowerCamelCase				:						Any=False				,	)       ->							List[str]:
        _UpperCamelCase   :				Tuple       =     tokenizer
        _UpperCamelCase   :				Tuple       =     tokenizer.bos_token_id
        _UpperCamelCase   :				List[str]       =     dataset
        _UpperCamelCase   :				Dict       =     seq_length
        _UpperCamelCase   :				Optional[int]       =     0
        _UpperCamelCase   :				Dict       =     infinite
        _UpperCamelCase   :				Any       =     0
        _UpperCamelCase   :				Dict       =     tokenized

        if self.tokenized:
               _UpperCamelCase   :				List[str]       =     seq_length * num_of_sequences
               _UpperCamelCase   :				str       =     'input_ids'
        else:
               _UpperCamelCase   :				Optional[int]       =     seq_length * chars_per_token * num_of_sequences
               _UpperCamelCase   :				int       =     'content'

 def __iter__(	self				:						List[str]     )       ->							Dict:
        _UpperCamelCase   :				Optional[Any]       =     iter(self.dataset     )
        _UpperCamelCase   :				Tuple       =     True
        while more_examples:
               _UpperCamelCase  ,					_UpperCamelCase   :				Dict       =     [], 0
               while True:
                      if buffer_len >= self.max_buffer_size:
                             break
                      try:
                             buffer.append(next(lowerCamelCase     )[self.content_field]     )
                             buffer_len += len(buffer[-1]     )
                      except StopIteration:
                             if self.infinite:
                                    _UpperCamelCase   :				str       =     iter(self.dataset     )
                                    self.epoch += 1
                                    logger.info(f"Dataset epoch: {self.epoch}"     )
                             else:
                                    _UpperCamelCase   :				Optional[int]       =     False
                                    break
               if self.tokenized:
                      _UpperCamelCase   :				Any       =     buffer
               else:
                      _UpperCamelCase   :				Any       =     self.tokenizer(lowerCamelCase				,	truncation=lowerCamelCase     )['input_ids']
               _UpperCamelCase   :				Union[str, Any]       =     []
               for tokenized_input in tokenized_inputs:
                      all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
               for i in range(0				,	len(lowerCamelCase     )				,	self.seq_length     ):
                      _UpperCamelCase   :				Optional[int]       =     all_token_ids[i : i + self.seq_length]
                      if len(lowerCamelCase     ) == self.seq_length:
                             self.current_size += 1
                             yield torch.tensor(lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						Union[str, Any]=1_0_0_0     )       ->							Tuple:
        return ShufflerIterDataPipe(self				,	buffer_size=lowerCamelCase     )







def SCREAMING_SNAKE_CASE (   lowercase_:		Dict ):
       _UpperCamelCase   :				Optional[Any]       =     args.model_ckpt.split('/' )[-1]
       _UpperCamelCase   :				Optional[int]       =     logging.getLogger(__name__ )
       _UpperCamelCase   :				str       =     Path(args.save_dir ) / 'log/'
       log_dir.mkdir(exist_ok=lowercase_ )
       _UpperCamelCase   :				int       =     F"debug_{accelerator.process_index}.log"
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,  datefmt='%m/%d/%Y %H:%M:%S'						,  level=logging.INFO						,  handlers=[logging.FileHandler(log_dir / filename ), logging.StreamHandler()]						,  )
       if accelerator.is_main_process:  # we only want to setup logging once
              accelerator.init_trackers(lowercase_						,  vars(lowercase_ ) )
              _UpperCamelCase   :				Optional[int]       =     accelerator.trackers[0].run.name
              logger.setLevel(logging.INFO )
              datasets.utils.logging.set_verbosity_info()
              transformers.utils.logging.set_verbosity_info()
       else:
              _UpperCamelCase   :				Optional[int]       =     ''
              logger.setLevel(logging.ERROR )
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
       return logger, run_name


def SCREAMING_SNAKE_CASE (   lowercase_:		Any ):
       _UpperCamelCase   :				List[Any]       =     {'streaming': True}
       _UpperCamelCase   :				int       =     load_dataset(args.dataset_name_train						,  split='train'						,  **lowercase_ )
       _UpperCamelCase   :				Optional[Any]       =     train_data.shuffle(buffer_size=args.shuffle_buffer						,  seed=args.seed )
       _UpperCamelCase   :				Tuple       =     load_dataset(args.dataset_name_valid						,  split='train'						,  **lowercase_ )
       _UpperCamelCase   :				str       =     ConstantLengthDataset(
           lowercase_						,  lowercase_						,  infinite=lowercase_						,  seq_length=args.seq_length						,  tokenized=args.tokenized )
       _UpperCamelCase   :				str       =     ConstantLengthDataset(
           lowercase_						,  lowercase_						,  infinite=lowercase_						,  seq_length=args.seq_length						,  tokenized=args.tokenized )
       _UpperCamelCase   :				Tuple       =     train_dataset.shuffle(buffer_size=args.shuffle_buffer )
       _UpperCamelCase   :				List[Any]       =     DataLoader(lowercase_						,  batch_size=args.train_batch_size						,  shuffle=lowercase_ )
       _UpperCamelCase   :				Optional[int]       =     DataLoader(lowercase_						,  batch_size=args.valid_batch_size )
       return train_dataloader, eval_dataloader


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Any]						,  lowercase_:		Dict						,  lowercase_:		List[str]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] ):
       _UpperCamelCase  ,					_UpperCamelCase   :				Union[str, Any]       =     [], []
       for n, p in model.named_parameters():
              if any(nd in n for nd in no_decay ):
                     params_without_wd.append(lowercase_ )
              else:
                     params_with_wd.append(lowercase_ )
       return [
           {"params": params_with_wd, "weight_decay": args.weight_decay},
           {"params": params_without_wd, "weight_decay": 0.0},
       ]


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		List[str] ):
       logger.info(F"Step {step}: {metrics}" )
       if accelerator.is_main_process:
              accelerator.log(lowercase_						,  lowercase_ )


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		Any						,  lowercase_:		str ):
       # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
       _UpperCamelCase   :				int       =     accelerator.unwrap_model(lowercase_ ).config
       _UpperCamelCase   :				Tuple       =     4 if args.gradient_checkpointing else 3
       _UpperCamelCase   :				List[str]       =     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
       _UpperCamelCase   :				Tuple       =     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
       _UpperCamelCase   :				str       =     factor * (
           1.0
           + (args.seq_length / (6.0 * config_model.n_embd))
           + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
       )
       _UpperCamelCase   :				str       =     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
       return tflops


def SCREAMING_SNAKE_CASE (   lowercase_:		int ):
       model.eval()
       _UpperCamelCase   :				List[str]       =     []
       for step, batch in enumerate(lowercase_ ):
              with torch.no_grad():
                     _UpperCamelCase   :				Tuple       =     model(lowercase_						,  labels=lowercase_ )
              _UpperCamelCase   :				Optional[Any]       =     outputs.loss.repeat(args.valid_batch_size )
              losses.append(accelerator.gather(lowercase_ ) )
              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       _UpperCamelCase   :				Union[str, Any]       =     torch.cat(lowercase_ )
       _UpperCamelCase   :				str       =     losses[: eval_dataloader.dataset.current_size].mean()
       try:
              _UpperCamelCase   :				Any       =     torch.exp(lowercase_ )
       except OverflowError:
              _UpperCamelCase   :				Dict       =     float('inf' )
       return loss.item(), perplexity.item()


# Settings
UpperCAmelCase							= HfArgumentParser(TrainingArguments)
UpperCAmelCase							= parser.parse_args()

# Accelerator
UpperCAmelCase							= ProjectConfiguration(project_dir=args.save_dir, logging_dir='''log''')
UpperCAmelCase							= Accelerator(log_with=['''wandb''', '''tensorboard'''], project_config=config)
UpperCAmelCase							= {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

UpperCAmelCase							= Namespace(**vars(args), **acc_state)
UpperCAmelCase							= accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
 UpperCAmelCase							= Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
UpperCAmelCase						,    UpperCAmelCase							= setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
 hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
UpperCAmelCase							= AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
 model.gradient_checkpointing_enable()
UpperCAmelCase							= AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
UpperCAmelCase						,    UpperCAmelCase							= create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCAmelCase							= AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCAmelCase							= get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)


def SCREAMING_SNAKE_CASE (   ):
       return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
UpperCAmelCase						,    UpperCAmelCase						,    UpperCAmelCase						,    UpperCAmelCase							= accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
 if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
  accelerator.load_state(args.resume_from_checkpoint)
  UpperCAmelCase							= os.path.basename(args.resume_from_checkpoint)
 else:
  # Get the most recent checkpoint
  UpperCAmelCase							= [f.name for f in os.scandir(args.save_dir) if f.is_dir() and '''step''' in str(f)]
  dirs.sort(key=os.path.getctime)
  UpperCAmelCase							= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
 # Extract the step of the checkpoint to continue from there
 UpperCAmelCase							= os.path.splitext(path)[0]
 UpperCAmelCase							= int(training_difference.replace('''step_''', ''''''))

# Train model
model.train()
UpperCAmelCase							= 0
UpperCAmelCase							= time.time()
UpperCAmelCase							= 0
for step, batch in enumerate(train_dataloader, start=1):
 if args.resume_from_checkpoint and step < resume_step:
  continue  # we need to skip steps until we reach the resumed step
 UpperCAmelCase							= model(batch, labels=batch, use_cache=False).loss
 UpperCAmelCase							= accelerator.gather(loss.repeat(args.train_batch_size)).mean()
 loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
 log_metrics(step, {'''samples''': step * samples_per_step, '''loss_per_step/train''': loss.item()})
 UpperCAmelCase							= loss / args.gradient_accumulation_steps
 if step % args.gradient_accumulation_steps != 0:
  # Prevent backward from doing gradient all_reduce in every step
  if accelerator.distributed_type == DistributedType.MULTI_GPU:
   with model.no_sync():
    accelerator.backward(loss)
  else:
   accelerator.backward(loss)
 else:
  UpperCAmelCase							= get_lr()
  accelerator.backward(loss)
  accelerator.clip_grad_norm_(model.parameters(), 1.0)
  optimizer.step()
  lr_scheduler.step()
  optimizer.zero_grad()
  UpperCAmelCase							= time.time() - t_start
  UpperCAmelCase							= compute_tflops(elapsed_time, accelerator, args)
  log_metrics(
      step,
      {
          '''steps''': completed_steps,
          '''loss/train''': loss_tracking,
          '''lr''': lr,
          '''tflops''': tflops,
          '''time_per_iteration''': elapsed_time,
      },
  )
  UpperCAmelCase							= time.time()
  UpperCAmelCase							= 0
  completed_steps += 1
 if step % args.save_checkpoint_steps == 0:
  logger.info('''Evaluating and saving model checkpoint''')
  UpperCAmelCase						,    UpperCAmelCase							= evaluate(args)
  log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
  accelerator.wait_for_everyone()
  UpperCAmelCase							= os.path.join(args.save_dir, f'''step_{step}''')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
   hf_repo.push_to_hub(commit_message=f'''step {step}''')
  model.train()
 if completed_steps >= args.max_train_steps:
  break

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
UpperCAmelCase						,    UpperCAmelCase							= evaluate(args)
log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
accelerator.wait_for_everyone()
UpperCAmelCase							= accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
UpperCAmelCase							= os.path.join(args.save_dir, f'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
 hf_repo.push_to_hub(commit_message='''final model''')




import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


UpperCAmelCase							= logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
UpperCAmelCase							= direct_transformers_import(Path(__file__).parent)


UpperCAmelCase							= {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int] 			=       ['feature_extractor', 'tokenizer']
 # Names need to be attr_class for attr in attributes
 _lowerCamelCase  :		List[Any] 			=       None
 _lowerCamelCase  :		Optional[Any] 			=       None
 _lowerCamelCase  :		Tuple 			=       None

 def __init__(	self				:						Tuple				,	*lowerCamelCase				:						Optional[Any]				,	**lowerCamelCase				:						List[Any]     )       ->							str:
        # Sanitize args and kwargs
        for key in kwargs:
               if key not in self.attributes:
                      raise TypeError(f"Unexpected keyword argument {key}."     )
        for arg, attribute_name in zip(lowerCamelCase				,	self.attributes     ):
               if attribute_name in kwargs:
                      raise TypeError(f"Got multiple values for argument {attribute_name}."     )
               else:
                      _UpperCamelCase   :				Any       =     arg

        if len(lowerCamelCase     ) != len(self.attributes     ):
               raise ValueError(
                   f"This processor requires {len(self.attributes     )} arguments: {', '.join(self.attributes     )}. Got "
                   f"{len(lowerCamelCase     )} arguments instead."     )

        # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
        for attribute_name, arg in kwargs.items():
               _UpperCamelCase   :				Union[str, Any]       =     getattr(self				,	f"{attribute_name}_class"     )
               # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
               _UpperCamelCase   :				str       =     AUTO_TO_BASE_CLASS_MAPPING.get(lowerCamelCase				,	lowerCamelCase     )
               if isinstance(lowerCamelCase				,	lowerCamelCase     ):
                      _UpperCamelCase   :				int       =     tuple(getattr(lowerCamelCase				,	lowerCamelCase     ) for n in class_name if n is not None     )
               else:
                      _UpperCamelCase   :				Optional[int]       =     getattr(lowerCamelCase				,	lowerCamelCase     )

               if not isinstance(lowerCamelCase				,	lowerCamelCase     ):
                      raise ValueError(
                          f"Received a {type(lowerCamelCase     ).__name__} for argument {attribute_name}, but a {class_name} was expected."     )

               setattr(self				,	lowerCamelCase				,	lowerCamelCase     )

 def __repr__(	self				:						Dict     )       ->							str:
        _UpperCamelCase   :				List[str]       =     [f"- {name}: {repr(getattr(self				,	lowerCamelCase     )     )}" for name in self.attributes]
        _UpperCamelCase   :				int       =     '\n'.join(lowerCamelCase     )
        return f"{self.__class__.__name__}:\n{attributes_repr}"

 def     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						int				,	lowerCamelCase				:						bool = False				,	**lowerCamelCase				:						Optional[Any]     )       ->							Any:
        os.makedirs(lowerCamelCase				,	exist_ok=lowerCamelCase     )

        if push_to_hub:
               _UpperCamelCase   :				Optional[int]       =     kwargs.pop('commit_message'				,	lowerCamelCase     )
               _UpperCamelCase   :				List[Any]       =     kwargs.pop('repo_id'				,	save_directory.split(os.path.sep     )[-1]     )
               _UpperCamelCase   :				Optional[int]       =     self._create_repo(lowerCamelCase				,	**lowerCamelCase     )
               _UpperCamelCase   :				List[str]       =     self._get_files_timestamps(lowerCamelCase     )
        # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
        # loaded from the Hub.
        if self._auto_class is not None:
               _UpperCamelCase   :				Optional[int]       =     [getattr(self				,	lowerCamelCase     ) for attribute_name in self.attributes]
               _UpperCamelCase   :				int       =     [(a.init_kwargs if isinstance(lowerCamelCase				,	lowerCamelCase     ) else a) for a in attrs]
               custom_object_save(self				,	lowerCamelCase				,	config=lowerCamelCase     )

        for attribute_name in self.attributes:
               _UpperCamelCase   :				int       =     getattr(self				,	lowerCamelCase     )
               # Include the processor class in the attribute config so this processor can then be reloaded with the
               # `AutoProcessor` API.
               if hasattr(lowerCamelCase				,	'_set_processor_class'     ):
                      attribute._set_processor_class(self.__class__.__name__     )
               attribute.save_pretrained(lowerCamelCase     )

        if self._auto_class is not None:
               # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
               for attribute_name in self.attributes:
                      _UpperCamelCase   :				Optional[Any]       =     getattr(self				,	lowerCamelCase     )
                      if isinstance(lowerCamelCase				,	lowerCamelCase     ):
                             del attribute.init_kwargs["auto_map"]

        if push_to_hub:
               self._upload_modified_files(
                   lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	commit_message=lowerCamelCase				,	token=kwargs.get('use_auth_token'     )				,	)

 @classmethod
 def     __snake_case    (	cls				:						Tuple				,	lowerCamelCase				:						Union[str, os.PathLike]				,	lowerCamelCase				:						Optional[Union[str, os.PathLike]] = None				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						Optional[Union[str, bool]] = None				,	lowerCamelCase				:						str = "main"				,	**lowerCamelCase				:						Tuple				,	)       ->							Optional[Any]:
        _UpperCamelCase   :				Any       =     cache_dir
        _UpperCamelCase   :				Optional[int]       =     force_download
        _UpperCamelCase   :				int       =     local_files_only
        _UpperCamelCase   :				Dict       =     revision

        _UpperCamelCase   :				Optional[Any]       =     kwargs.pop('use_auth_token'				,	lowerCamelCase     )
        if use_auth_token is not None:
               warnings.warn(
                   'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'				,	lowerCamelCase     )
               if token is not None:
                      raise ValueError(
                          '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'     )
               _UpperCamelCase   :				Optional[int]       =     use_auth_token

        if token is not None:
               # change to `token` in a follow-up PR
               _UpperCamelCase   :				List[Any]       =     token

        _UpperCamelCase   :				List[Any]       =     cls._get_arguments_from_pretrained(lowerCamelCase				,	**lowerCamelCase     )
        return cls(*lowerCamelCase     )

 @classmethod
 def     __snake_case    (	cls				:						Dict				,	lowerCamelCase				:						Optional[Any]="AutoProcessor"     )       ->							Optional[int]:
        if not isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				Tuple       =     auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(lowerCamelCase				,	lowerCamelCase     ):
               raise ValueError(f"{auto_class} is not a valid auto class."     )

        _UpperCamelCase   :				Union[str, Any]       =     auto_class

 @classmethod
 def     __snake_case    (	cls				:						int				,	lowerCamelCase				:						Any				,	**lowerCamelCase				:						List[Any]     )       ->							Optional[Any]:
        _UpperCamelCase   :				Optional[int]       =     []
        for attribute_name in cls.attributes:
               _UpperCamelCase   :				Dict       =     getattr(cls				,	f"{attribute_name}_class"     )
               if isinstance(lowerCamelCase				,	lowerCamelCase     ):
                      _UpperCamelCase   :				Any       =     tuple(getattr(lowerCamelCase				,	lowerCamelCase     ) if n is not None else None for n in class_name     )
                      _UpperCamelCase   :				List[Any]       =     kwargs.get('use_fast'				,	lowerCamelCase     )
                      if use_fast and classes[1] is not None:
                             _UpperCamelCase   :				Optional[int]       =     classes[1]
                      else:
                             _UpperCamelCase   :				Any       =     classes[0]
               else:
                      _UpperCamelCase   :				Any       =     getattr(lowerCamelCase				,	lowerCamelCase     )

               args.append(attribute_class.from_pretrained(lowerCamelCase				,	**lowerCamelCase     )     )
        return args

 @property
 def     __snake_case    (	self				:						List[Any]     )       ->							Optional[Any]:
        _UpperCamelCase   :				List[str]       =     getattr(self				,	self.attributes[0]     )
        return getattr(lowerCamelCase				,	'model_input_names'				,	lowerCamelCase     )







UpperCAmelCase							= copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
 UpperCAmelCase							= ProcessorMixin.push_to_hub.__doc__.format(
     object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
 )



