
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  BigBirdConfig
 __lowerCAmelCase	:  jnp.dtype	   =      jnp.floataa
 __lowerCAmelCase	:  bool	   =      True


 def   lowercase__  (       self):


   '''simple docstring'''




   super().setup()
   lowercase__	: Dict     		=   nn.Dense(5       ,	dtype=self.dtype)



 def __call__(       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[str]     		=   super().__call__(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   self.cls(outputs[2])
   return outputs[:2] + (cls_out,)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      FlaxBigBirdForNaturalQuestionsModule

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  def cross_entropy(lowercase_			,			lowercase_			,			lowercase_=None ):
    lowercase__	: int     		=   logits.shape[-1]
    lowercase__	: List[str]     		=   (labels[..., None] == jnp.arange(lowercase_ )[None]).astype("""f4""" )
    lowercase__	: int     		=   jax.nn.log_softmax(lowercase_			,			axis=-1 )
    lowercase__	: Any     		=   -jnp.sum(labels * logits			,			axis=-1 )
    if reduction is not None:
      lowercase__	: Optional[int]     		=   reduction(lowercase_ )
    return loss

  lowercase__	: int     		=   partial(lowercase_			,			reduction=jnp.mean )
  lowercase__	: Tuple     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: List[Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: Union[str, Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  str	   =      "google/bigbird-roberta-base"
 __lowerCAmelCase	:  int	   =      3_000
 __lowerCAmelCase	:  int	   =      10_500

 __lowerCAmelCase	:  int	   =      128
 __lowerCAmelCase	:  int	   =      3

 __lowerCAmelCase	:  int	   =      1
 __lowerCAmelCase	:  int	   =      5

 # tx_args
 __lowerCAmelCase	:  float	   =      3e-5
 __lowerCAmelCase	:  float	   =      0.0
 __lowerCAmelCase	:  int	   =      20_000
 __lowerCAmelCase	:  float	   =      0.0_095

 __lowerCAmelCase	:  str	   =      "bigbird-roberta-natural-questions"
 __lowerCAmelCase	:  str	   =      "training-expt"
 __lowerCAmelCase	:  str	   =      "data/nq-training.jsonl"
 __lowerCAmelCase	:  str	   =      "data/nq-validation.jsonl"


 def   lowercase__  (       self):


   '''simple docstring'''




   os.makedirs(self.base_dir       ,	exist_ok=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   os.path.join(self.base_dir       ,	self.save_dir)
   lowercase__	: str     		=   self.batch_size_per_device * jax.device_count()



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  int
 __lowerCAmelCase	:  int	   =      4_096  # no dynamic padding on TPUs


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.collate_fn(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   jax.tree_util.tree_map(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return batch


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.fetch_inputs(features["""input_ids"""])
   lowercase__	: str     		=   {
       """input_ids""": jnp.array(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.intaa),
       """attention_mask""": jnp.array(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.intaa),
       """start_labels""": jnp.array(features["""start_token"""]       ,	dtype=jnp.intaa),
       """end_labels""": jnp.array(features["""end_token"""]       ,	dtype=jnp.intaa),
       """pooled_labels""": jnp.array(features["""category"""]       ,	dtype=jnp.intaa),
   }
   return batch


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   [self._fetch_inputs(SCREAMING_SNAKE_CASE_) for ids in input_ids]
   return zip(*SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   [1 for _ in range(len(SCREAMING_SNAKE_CASE_))]
   while len(SCREAMING_SNAKE_CASE_) < self.max_length:
     input_ids.append(self.pad_id)
     attention_mask.append(0)
   return input_ids, attention_mask

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






  if seed is not None:
    lowercase__	: Any     		=   dataset.shuffle(seed=lowercase_ )
  for i in range(len(lowercase_ ) // batch_size ):
    lowercase__	: List[str]     		=   dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(lowercase_ )

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			**lowercase_ )		->						int:


  '''simple docstring'''






  def loss_fn(lowercase_ ):
    lowercase__	: Dict     		=   model_inputs.pop("""start_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""end_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""pooled_labels""" )

    lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=lowercase_			,			dropout_rng=lowercase_			,			train=lowercase_ )
    lowercase__		,     lowercase__		,     lowercase__	: Any     		=   outputs

    return state.loss_fn(
        lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)

  lowercase__		,     lowercase__	: Optional[int]     		=   jax.random.split(lowercase_ )
  lowercase__	: Tuple     		=   jax.value_and_grad(lowercase_ )
  lowercase__		,     lowercase__	: Optional[int]     		=   grad_fn(state.params )
  lowercase__	: Tuple     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  lowercase__	: Any     		=   jax.lax.pmean(lowercase_			,			"""batch""" )

  lowercase__	: str     		=   state.apply_gradients(grads=lowercase_ )
  return state, metrics, new_drp_rng

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			**lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Tuple     		=   model_inputs.pop("""start_labels""" )
  lowercase__	: List[str]     		=   model_inputs.pop("""end_labels""" )
  lowercase__	: int     		=   model_inputs.pop("""pooled_labels""" )

  lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=state.params			,			train=lowercase_ )
  lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   outputs

  lowercase__	: Optional[Any]     		=   state.loss_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: List[str]     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  return metrics



class    _snake_case (						train_state.TrainState				):
 __lowerCAmelCase	:  Callable	   =      struct.field(pytree_node=UpperCAmelCase_				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Args
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  wandb
 __lowerCAmelCase	:  Callable	   =      None


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: List[str]     		=   model.params
   lowercase__	: Dict     		=   TrainState.create(
       apply_fn=model.__call__       ,	params=SCREAMING_SNAKE_CASE_       ,	tx=SCREAMING_SNAKE_CASE_       ,	loss_fn=SCREAMING_SNAKE_CASE_       ,	)
   if ckpt_dir is not None:
     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: str     		=   restore_checkpoint(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
     lowercase__	: str     		=   {
         """lr""": args.lr,
         """init_lr""": args.init_lr,
         """warmup_steps""": args.warmup_steps,
         """num_train_steps""": num_train_steps,
         """weight_decay""": args.weight_decay,
     }
     lowercase__		,     lowercase__	: Any     		=   build_tx(**SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   train_state.TrainState(
         step=SCREAMING_SNAKE_CASE_       ,	apply_fn=model.__call__       ,	params=SCREAMING_SNAKE_CASE_       ,	tx=SCREAMING_SNAKE_CASE_       ,	opt_state=SCREAMING_SNAKE_CASE_       ,	)
     lowercase__	: Optional[Any]     		=   args
     lowercase__	: Union[str, Any]     		=   data_collator
     lowercase__	: str     		=   lr
     lowercase__	: Union[str, Any]     		=   params
   lowercase__	: Dict     		=   jax_utils.replicate(SCREAMING_SNAKE_CASE_)
   return state


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.args
   lowercase__	: List[str]     		=   len(SCREAMING_SNAKE_CASE_) // args.batch_size

   lowercase__	: int     		=   jax.random.PRNGKey(0)
   lowercase__	: Union[str, Any]     		=   jax.random.split(SCREAMING_SNAKE_CASE_       ,	jax.device_count())
   for epoch in range(args.max_epochs):
     lowercase__	: Tuple     		=   jnp.array(0       ,	dtype=jnp.floataa)
     lowercase__	: List[str]     		=   get_batched_dataset(SCREAMING_SNAKE_CASE_       ,	args.batch_size       ,	seed=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   0
     for batch in tqdm(SCREAMING_SNAKE_CASE_       ,	total=SCREAMING_SNAKE_CASE_       ,	desc=f'Running EPOCH-{epoch}'):
       lowercase__	: Tuple     		=   self.data_collator(SCREAMING_SNAKE_CASE_)
       lowercase__		,     lowercase__		,     lowercase__	: List[Any]     		=   self.train_step_fn(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
       running_loss += jax_utils.unreplicate(metrics["""loss"""])
       i += 1
       if i % args.logging_steps == 0:
         lowercase__	: List[str]     		=   jax_utils.unreplicate(state.step)
         lowercase__	: str     		=   running_loss.item() / i
         lowercase__	: Tuple     		=   self.scheduler_fn(state_step - 1)

         lowercase__	: Tuple     		=   self.evaluate(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
         lowercase__	: List[Any]     		=   {
             """step""": state_step.item(),
             """eval_loss""": eval_loss.item(),
             """tr_loss""": tr_loss,
             """lr""": lr.item(),
         }
         tqdm.write(str(SCREAMING_SNAKE_CASE_))
         self.logger.log(SCREAMING_SNAKE_CASE_       ,	commit=SCREAMING_SNAKE_CASE_)

       if i % args.save_steps == 0:
         self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}'       ,	state=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   get_batched_dataset(SCREAMING_SNAKE_CASE_       ,	self.args.batch_size)
   lowercase__	: Tuple     		=   len(SCREAMING_SNAKE_CASE_) // self.args.batch_size
   lowercase__	: Union[str, Any]     		=   jnp.array(0       ,	dtype=jnp.floataa)
   lowercase__	: Optional[Any]     		=   0
   for batch in tqdm(SCREAMING_SNAKE_CASE_       ,	total=SCREAMING_SNAKE_CASE_       ,	desc="""Evaluating ... """):
     lowercase__	: Tuple     		=   self.data_collator(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   self.val_step_fn(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
     running_loss += jax_utils.unreplicate(metrics["""loss"""])
     i += 1
   return running_loss / i



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   jax_utils.unreplicate(SCREAMING_SNAKE_CASE_)
   print(f'SAVING CHECKPOINT IN {save_dir}'       ,	end=""" ... """)
   self.model_save_fn(SCREAMING_SNAKE_CASE_       ,	params=state.params)
   with open(os.path.join(SCREAMING_SNAKE_CASE_       ,	"""opt_state.msgpack""")       ,	"""wb""") as f:
     f.write(to_bytes(state.opt_state))
   joblib.dump(self.args       ,	os.path.join(SCREAMING_SNAKE_CASE_       ,	"""args.joblib"""))
   joblib.dump(self.data_collator       ,	os.path.join(SCREAMING_SNAKE_CASE_       ,	"""data_collator.joblib"""))
   with open(os.path.join(SCREAMING_SNAKE_CASE_       ,	"""training_state.json""")       ,	"""w""") as f:
     json.dump({"""step""": state.step.item()}       ,	SCREAMING_SNAKE_CASE_)
   print("""DONE""")

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  print(F'RESTORING CHECKPOINT FROM {save_dir}'			,			end=""" ... """ )
  with open(os.path.join(lowercase_			,			"""flax_model.msgpack""" )			,			"""rb""" ) as f:
    lowercase__	: Optional[Any]     		=   from_bytes(state.params			,			f.read() )

  with open(os.path.join(lowercase_			,			"""opt_state.msgpack""" )			,			"""rb""" ) as f:
    lowercase__	: Dict     		=   from_bytes(state.opt_state			,			f.read() )

  lowercase__	: Any     		=   joblib.load(os.path.join(lowercase_			,			"""args.joblib""" ) )
  lowercase__	: Optional[int]     		=   joblib.load(os.path.join(lowercase_			,			"""data_collator.joblib""" ) )

  with open(os.path.join(lowercase_			,			"""training_state.json""" )			,			"""r""" ) as f:
    lowercase__	: int     		=   json.load(lowercase_ )
  lowercase__	: Optional[Any]     		=   training_state["""step"""]

  print("""DONE""" )
  return params, opt_state, step, args, data_collator

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   num_train_steps - warmup_steps
  lowercase__	: int     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=lowercase_			,			transition_steps=lowercase_ )
  lowercase__	: Optional[int]     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=1E-7			,			transition_steps=lowercase_ )
  lowercase__	: Any     		=   optax.join_schedules(schedules=[warmup_fn, decay_fn]			,			boundaries=[warmup_steps] )
  return lr



def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  def weight_decay_mask(lowercase_ ):
    lowercase__	: Dict     		=   traverse_util.flatten_dict(lowercase_ )
    lowercase__	: int     		=   {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
    return traverse_util.unflatten_dict(lowercase_ )

  lowercase__	: Optional[int]     		=   scheduler_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  lowercase__	: int     		=   optax.adamw(learning_rate=lowercase_			,			weight_decay=lowercase_			,			mask=lowercase_ )
  return tx, lr













"""simple docstring"""





import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A											=						logging.getLogger(__name__)


__A											=						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__A											=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={
               """help""": (
                   """The model checkpoint for weights initialization. Leave None if you want to train a model from"""
                   """ scratch."""
               )
           }    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a			)}    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,  )






@dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """The input training data file (a text file)."""}			)
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={
               """help""": (
                   """The input training data files (multiple files in glob format). """
                   """Very often splitting large files to smaller files can prevent tokenizer going out of memory"""
               )
           }    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """An optional input train ref data file for whole word mask in Chinese."""}    ,  )
       __magic_name__	:Optional[str]						    =   field(
           default=a    ,  metadata={"""help""": """An optional input eval ref data file for whole word mask in Chinese."""}    ,  )
       __magic_name__	:bool						    =   field(
           default=a    ,  metadata={"""help""": """Whether distinct lines of text in the dataset are to be handled as distinct sequences."""}    ,  )

       __magic_name__	:bool						    =   field(
           default=a    ,  metadata={"""help""": """Train with masked-language modeling loss instead of language modeling."""}			)
       __magic_name__	:bool						    =   field(default=a    ,  metadata={"""help""": """Whether ot not to use whole word mask."""}			)
       __magic_name__	:float						    =   field(
           default=0.15    ,  metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}			)
       __magic_name__	:float						    =   field(
           default=1 / 6    ,  metadata={
               """help""": (
                   """Ratio of length of a span of masked tokens to surrounding context length for permutation language"""
                   """ modeling."""
               )
           }    ,  )
       __magic_name__	:int						    =   field(
           default=5    ,  metadata={"""help""": """Maximum length of a span of masked tokens for permutation language modeling."""}			)

       __magic_name__	:int						    =   field(
           default=-1    ,  metadata={
               """help""": (
                   """Optional input sequence length after tokenization."""
                   """The training dataset will be truncated in block of this size for training."""
                   """Default to the model max input length for single sentence inputs (take into account special tokens)."""
               )
           }    ,  )
       __magic_name__	:bool						    =   field(
           default=a    ,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)






def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = False	,  _SCREAMING_SNAKE_CASE = None	,  )   ->Optional[int]:





  """simple docstring"""


  def _dataset(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None     ):
    if args.line_by_line:
      if ref_path is not None:
        if not args.whole_word_mask or not args.mlm:
          raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask'     )
        return LineByLineWithRefDataset(
            tokenizer=_SCREAMING_SNAKE_CASE	,  file_path=_SCREAMING_SNAKE_CASE	,  block_size=args.block_size	,  ref_path=_SCREAMING_SNAKE_CASE	,  )

      return LineByLineTextDataset(tokenizer=_SCREAMING_SNAKE_CASE	,  file_path=_SCREAMING_SNAKE_CASE	,  block_size=args.block_size     )
    else:
      return TextDataset(
          tokenizer=_SCREAMING_SNAKE_CASE	,  file_path=_SCREAMING_SNAKE_CASE	,  block_size=args.block_size	,  overwrite_cache=args.overwrite_cache	,  cache_dir=_SCREAMING_SNAKE_CASE	,  )

  if evaluate:
    return _dataset(args.eval_data_file	,  args.eval_ref_file     )
  elif args.train_data_files:
    return ConcatDataset([_dataset(_SCREAMING_SNAKE_CASE     ) for f in glob(args.train_data_files     )]     )
  else:
    return _dataset(args.train_data_file	,  args.train_ref_file     )
def 					__A  ()   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
  lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				parser.parse_args_into_dataclasses()

  if data_args.eval_data_file is None and training_args.do_eval:
    raise ValueError(
        'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
        'or remove the --do_eval argument.'     )
  if (
      os.path.exists(training_args.output_dir     )
      and os.listdir(training_args.output_dir     )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
        ' --overwrite_output_dir to overcome.'     )

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,  datefmt='%m/%d/%Y %H:%M:%S'	,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,  )
  logger.warning(
      'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'	,  training_args.local_rank	,  training_args.device	,  training_args.n_gpu	,  bool(training_args.local_rank != -1     )	,  training_args.fpaa	,  )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank     ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('Training/evaluation parameters %s'	,  _SCREAMING_SNAKE_CASE     )

  # Set seed
  set_seed(training_args.seed     )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  if model_args.config_name:
    lowerCAmelCase__     :Optional[Any]											=				AutoConfig.from_pretrained(model_args.config_name	,  cache_dir=model_args.cache_dir     )
  elif model_args.model_name_or_path:
    lowerCAmelCase__     :List[str]											=				AutoConfig.from_pretrained(model_args.model_name_or_path	,  cache_dir=model_args.cache_dir     )
  else:
    lowerCAmelCase__     :List[Any]											=				CONFIG_MAPPING[model_args.model_type]()
    logger.warning('You are instantiating a new config instance from scratch.'     )

  if model_args.tokenizer_name:
    lowerCAmelCase__     :Union[str, Any]											=				AutoTokenizer.from_pretrained(model_args.tokenizer_name	,  cache_dir=model_args.cache_dir     )
  elif model_args.model_name_or_path:
    lowerCAmelCase__     :str											=				AutoTokenizer.from_pretrained(model_args.model_name_or_path	,  cache_dir=model_args.cache_dir     )
  else:
    raise ValueError(
        'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
        ' script, save it,and load it from here, using --tokenizer_name'     )

  if model_args.model_name_or_path:
    lowerCAmelCase__     :Optional[Any]											=				AutoModelWithLMHead.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	,  )
  else:
    logger.info('Training new model from scratch'     )
    lowerCAmelCase__     :int											=				AutoModelWithLMHead.from_config(_SCREAMING_SNAKE_CASE     )

  model.resize_token_embeddings(len(_SCREAMING_SNAKE_CASE     )     )

  if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
    raise ValueError(
        'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
        '--mlm flag (masked language modeling).'     )

  if data_args.block_size <= 0:
    lowerCAmelCase__     :Dict											=				tokenizer.max_len
    # Our input block size will be the max possible for the model
  else:
    lowerCAmelCase__     :Any											=				min(data_args.block_size	,  tokenizer.max_len     )

  # Get datasets

  lowerCAmelCase__     :List[str]											=				(
      get_dataset(_SCREAMING_SNAKE_CASE	,  tokenizer=_SCREAMING_SNAKE_CASE	,  cache_dir=model_args.cache_dir     ) if training_args.do_train else None
  )
  lowerCAmelCase__     :Optional[int]											=				(
      get_dataset(_SCREAMING_SNAKE_CASE	,  tokenizer=_SCREAMING_SNAKE_CASE	,  evaluate=_SCREAMING_SNAKE_CASE	,  cache_dir=model_args.cache_dir     )
      if training_args.do_eval
      else None
  )
  if config.model_type == "xlnet":
    lowerCAmelCase__     :str											=				DataCollatorForPermutationLanguageModeling(
        tokenizer=_SCREAMING_SNAKE_CASE	,  plm_probability=data_args.plm_probability	,  max_span_length=data_args.max_span_length	,  )
  else:
    if data_args.mlm and data_args.whole_word_mask:
      lowerCAmelCase__     :Optional[Any]											=				DataCollatorForWholeWordMask(
          tokenizer=_SCREAMING_SNAKE_CASE	,  mlm_probability=data_args.mlm_probability     )
    else:
      lowerCAmelCase__     :str											=				DataCollatorForLanguageModeling(
          tokenizer=_SCREAMING_SNAKE_CASE	,  mlm=data_args.mlm	,  mlm_probability=data_args.mlm_probability     )

    # Initialize our Trainer
  lowerCAmelCase__     :Tuple											=				Trainer(
      model=_SCREAMING_SNAKE_CASE	,  args=_SCREAMING_SNAKE_CASE	,  data_collator=_SCREAMING_SNAKE_CASE	,  train_dataset=_SCREAMING_SNAKE_CASE	,  eval_dataset=_SCREAMING_SNAKE_CASE	,  prediction_loss_only=_SCREAMING_SNAKE_CASE	,  )

  # Training
  if training_args.do_train:
    lowerCAmelCase__     :Tuple											=				(
        model_args.model_name_or_path
        if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     )
        else None
    )
    trainer.train(model_path=_SCREAMING_SNAKE_CASE     )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir     )

    # Evaluation
  lowerCAmelCase__     :Optional[Any]											=				{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'     )

    lowerCAmelCase__     :Any											=				trainer.evaluate()

    lowerCAmelCase__     :Optional[Any]											=				math.exp(eval_output['eval_loss']     )
    lowerCAmelCase__     :Dict											=				{'perplexity': perplexity}

    lowerCAmelCase__     :List[Any]											=				os.path.join(training_args.output_dir	,  'eval_results_lm.txt'     )
    if trainer.is_world_master():
      with open(_SCREAMING_SNAKE_CASE	,  'w'     ) as writer:
        logger.info('***** Eval results *****'     )
        for key in sorted(result.keys()     ):
          logger.info('  %s = %s'	,  _SCREAMING_SNAKE_CASE	,  str(result[key]     )     )
          writer.write('%s = %s\n' % (key, str(result[key]     ))     )

    results.update(_SCREAMING_SNAKE_CASE     )

  return results
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  main()


if __name__ == "__main__":
  main()
