






'''simple docstring'''





import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


SCREAMING_SNAKE_CASE 							=						logging.getLogger(__name__)

SCREAMING_SNAKE_CASE 							=						tf.data.AUTOTUNE

def      _lowerCamelCase	(						)      ->					List[Any]:
    _UpperCAmelCase				:      Optional[int]			  =				argparse.ArgumentParser(description='''Train a masked language model on TPU.''' )
    parser.add_argument(
        '''--pretrained_model_config'''		,		type=_UpperCamelCase		,		default='''roberta-base'''		,		help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!'''		,		)
    parser.add_argument(
        '''--tokenizer'''		,		type=_UpperCamelCase		,		default='''unigram-tokenizer-wikitext'''		,		help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'''		,		)

    parser.add_argument(
        '''--per_replica_batch_size'''		,		type=_UpperCamelCase		,		default=8		,		help='''Batch size per TPU core.'''		,		)

    parser.add_argument(
        '''--no_tpu'''		,		action='''store_true'''		,		help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'''		,		)

    parser.add_argument(
        '''--tpu_name'''		,		type=_UpperCamelCase		,		help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'''		,		default='''local'''		,		)

    parser.add_argument(
        '''--tpu_zone'''		,		type=_UpperCamelCase		,		help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''		,		)

    parser.add_argument(
        '''--gcp_project'''		,		type=_UpperCamelCase		,		help='''Google cloud project name. Only used for non-Colab TPU nodes.''' )

    parser.add_argument(
        '''--bfloat16'''		,		action='''store_true'''		,		help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'''		,		)

    parser.add_argument(
        '''--train_dataset'''		,		type=_UpperCamelCase		,		help='''Path to training dataset to load. If the path begins with `gs://`'''
        ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''		,		)

    parser.add_argument(
        '''--shuffle_buffer_size'''		,		type=_UpperCamelCase		,		default=2**18		,		help='''Size of the shuffle buffer (in samples)'''		,		)

    parser.add_argument(
        '''--eval_dataset'''		,		type=_UpperCamelCase		,		help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
        ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''		,		)

    parser.add_argument(
        '''--num_epochs'''		,		type=_UpperCamelCase		,		default=1		,		help='''Number of epochs to train for.'''		,		)

    parser.add_argument(
        '''--learning_rate'''		,		type=_UpperCamelCase		,		default=1e-4		,		help='''Learning rate to use for training.'''		,		)

    parser.add_argument(
        '''--weight_decay_rate'''		,		type=_UpperCamelCase		,		default=1e-3		,		help='''Weight decay rate to use for training.'''		,		)

    parser.add_argument(
        '''--max_length'''		,		type=_UpperCamelCase		,		default=512		,		help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''		,		)

    parser.add_argument(
        '''--mlm_probability'''		,		type=_UpperCamelCase		,		default=0.15		,		help='''Fraction of tokens to mask during training.'''		,		)

    parser.add_argument('''--output_dir'''		,		type=_UpperCamelCase		,		required=_UpperCamelCase		,		help='''Path to save model checkpoints to.''' )
    parser.add_argument('''--hub_model_id'''		,		type=_UpperCamelCase		,		help='''Model ID to upload to on the Hugging Face Hub.''' )

    _UpperCAmelCase				:      Optional[Any]			  =				parser.parse_args()
    return args

def      _lowerCamelCase	(						__A    :							Optional[Any] )      ->					Any:


    try:
        if args.tpu_name:
            _UpperCAmelCase				:      Tuple			  =				tf.distribute.cluster_resolver.TPUClusterResolver(
                args.tpu_name		,		zone=args.tpu_zone		,		project=args.gcp_project )
        else:
            _UpperCAmelCase				:      Optional[Any]			  =				tf.distribute.cluster_resolver.TPUClusterResolver()
    except ValueError:
        raise RuntimeError(
            '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
            '''--gcp_project. When running on a TPU VM, use --tpu_name local.''' )

    tf.config.experimental_connect_to_cluster(_UpperCamelCase )
    tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase )

    return tpu

def      _lowerCamelCase	(						__A    :							str )      ->					Dict:
    _UpperCAmelCase				:      Tuple			  =				0
    for file in file_list:
        _UpperCAmelCase				:      Any			  =				file.split('''/''' )[-1]
        _UpperCAmelCase				:      Any			  =				re.search(r'''-\d+-(\d+)\.tfrecord'''		,		_UpperCamelCase ).group(1 )
        _UpperCAmelCase				:      Union[str, Any]			  =				int(_UpperCamelCase )
        num_samples += sample_count

    return num_samples

def      _lowerCamelCase	(						__A    :							str		,		__A    :							int		,		__A    :							Dict		,		__A    :							Any		,		__A    :							Tuple		,		__A    :							int=None )      ->					Optional[int]:
    _UpperCAmelCase				:      Dict			  =				count_samples(_UpperCamelCase )
    _UpperCAmelCase				:      int			  =				tf.data.Dataset.from_tensor_slices(_UpperCamelCase )
    if shuffle:
        _UpperCAmelCase				:      List[str]			  =				dataset.shuffle(len(_UpperCamelCase ) )
    _UpperCAmelCase				:      List[str]			  =				tf.data.TFRecordDataset(_UpperCamelCase		,		num_parallel_reads=_UpperCamelCase )
    # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
    _UpperCAmelCase				:      int			  =				dataset.apply(tf.data.experimental.assert_cardinality(_UpperCamelCase ) )
    _UpperCAmelCase				:      Tuple			  =				dataset.map(_UpperCamelCase		,		num_parallel_calls=_UpperCamelCase )
    if shuffle:
        assert shuffle_buffer_size is not None
        _UpperCAmelCase				:      Optional[int]			  =				dataset.shuffle(args.shuffle_buffer_size )
    _UpperCAmelCase				:      Optional[int]			  =				dataset.batch(_UpperCamelCase		,		drop_remainder=_UpperCamelCase )
    _UpperCAmelCase				:      Union[str, Any]			  =				dataset.map(_UpperCamelCase		,		num_parallel_calls=_UpperCamelCase )
    _UpperCAmelCase				:      Any			  =				dataset.prefetch(_UpperCamelCase )
    return dataset

def      _lowerCamelCase	(						__A    :							str )      ->					Dict:


    if not args.no_tpu:
        _UpperCAmelCase				:      int			  =				initialize_tpu(_UpperCamelCase )
        _UpperCAmelCase				:      Dict			  =				tf.distribute.TPUStrategy(_UpperCamelCase )
    else:
        _UpperCAmelCase				:      Union[str, Any]			  =				tf.distribute.OneDeviceStrategy(device='''/gpu:0''' )

    if args.bfloataa:
        tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16''' )

    _UpperCAmelCase				:      Tuple			  =				AutoTokenizer.from_pretrained(args.tokenizer )
    _UpperCAmelCase				:      Union[str, Any]			  =				AutoConfig.from_pretrained(args.pretrained_model_config )
    _UpperCAmelCase				:      Optional[int]			  =				tokenizer.vocab_size

    _UpperCAmelCase				:      Optional[int]			  =				tf.io.gfile.glob(os.path.join(args.train_dataset		,		'''*.tfrecord''' ) )
    if not training_records:
        raise ValueError(f'''No .tfrecord files found in {args.train_dataset}.''' )
    _UpperCAmelCase				:      Any			  =				tf.io.gfile.glob(os.path.join(args.eval_dataset		,		'''*.tfrecord''' ) )
    if not eval_records:
        raise ValueError(f'''No .tfrecord files found in {args.eval_dataset}.''' )

    _UpperCAmelCase				:      Dict			  =				count_samples(_UpperCamelCase )

    _UpperCAmelCase				:      Union[str, Any]			  =				num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
    _UpperCAmelCase				:      Any			  =				steps_per_epoch * args.num_epochs

    with strategy.scope():
        _UpperCAmelCase				:      List[str]			  =				TFAutoModelForMaskedLM.from_config(_UpperCamelCase )
        model(model.dummy_inputs )  # Pass some dummy inputs through the model to ensure all the weights are built
        _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				create_optimizer(
            num_train_steps=_UpperCamelCase		,		num_warmup_steps=total_train_steps // 20		,		init_lr=args.learning_rate		,		weight_decay_rate=args.weight_decay_rate		,		)

        # Transformers models compute the right loss for their task by default when labels are passed, and will
        # use this for training unless you specify your own loss function in compile().
        model.compile(optimizer=_UpperCamelCase		,		metrics=['''accuracy'''] )

    def decode_fn(__A    :							List[str] ):
        _UpperCAmelCase				:      str			  =				{
            '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa		,		shape=(args.max_length,) ),
            '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa		,		shape=(args.max_length,) ),
        }
        return tf.io.parse_single_example(_UpperCamelCase		,		_UpperCamelCase )

    # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
    # use their methods in our data pipeline.
    _UpperCAmelCase				:      Union[str, Any]			  =				DataCollatorForLanguageModeling(
        tokenizer=_UpperCamelCase		,		mlm_probability=args.mlm_probability		,		mlm=_UpperCamelCase		,		return_tensors='''tf''' )

    def mask_with_collator(__A    :							Optional[Any] ):
        # TF really needs an isin() function
        _UpperCAmelCase				:      Dict			  =				(
            ~tf.cast(batch['''attention_mask''']		,		tf.bool )
            | (batch['''input_ids'''] == tokenizer.cls_token_id)
            | (batch['''input_ids'''] == tokenizer.sep_token_id)
        )
        _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				data_collator.tf_mask_tokens(
            batch['''input_ids''']		,		vocab_size=len(_UpperCamelCase )		,		mask_token_id=tokenizer.mask_token_id		,		special_tokens_mask=_UpperCamelCase		,		)
        return batch

    _UpperCAmelCase				:      Tuple			  =				args.per_replica_batch_size * strategy.num_replicas_in_sync

    _UpperCAmelCase				:      int			  =				prepare_dataset(
        _UpperCamelCase		,		decode_fn=_UpperCamelCase		,		mask_fn=_UpperCamelCase		,		batch_size=_UpperCamelCase		,		shuffle=_UpperCamelCase		,		shuffle_buffer_size=args.shuffle_buffer_size		,		)

    _UpperCAmelCase				:      Optional[Any]			  =				prepare_dataset(
        _UpperCamelCase		,		decode_fn=_UpperCamelCase		,		mask_fn=_UpperCamelCase		,		batch_size=_UpperCamelCase		,		shuffle=_UpperCamelCase		,		)

    _UpperCAmelCase				:      Tuple			  =				[]
    if args.hub_model_id:
        callbacks.append(
            PushToHubCallback(output_dir=args.output_dir		,		hub_model_id=args.hub_model_id		,		tokenizer=_UpperCamelCase ) )

    model.fit(
        _UpperCamelCase		,		validation_data=_UpperCamelCase		,		epochs=args.num_epochs		,		callbacks=_UpperCamelCase		,		)

    model.save_pretrained(args.output_dir )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						parse_args()
  main(args)












import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length

def      _lowerCamelCase	(						__A    :							List[str] )      ->					Union[str, Any]:
    _UpperCAmelCase				:      Dict			  =				int(__A )
    _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				t // 3_600, (t // 60) % 60, t % 60
    return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''

def      _lowerCamelCase	(						__A    :							List[str]		,		__A    :							List[Any]		,		__A    :							str		,		__A    :							int		,		__A    :							Dict=300 )      ->					int:
    # docstyle-ignore
    return f'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''

def      _lowerCamelCase	(						__A    :							List[str] )      ->					Optional[int]:
    _UpperCAmelCase				:      Optional[Any]			  =				'''<table border="1" class="dataframe">\n'''
    html_code += """  <thead>\n <tr style="text-align: left;">\n"""
    for i in items[0]:
        html_code += f'''      <th>{i}</th>\n'''
    html_code += "    </tr>\n  </thead>\n  <tbody>\n"
    for line in items[1:]:
        html_code += "    <tr>\n"
        for elt in line:
            _UpperCAmelCase				:      Tuple			  =				f'''{elt:.6f}''' if isinstance(__A		,		__A ) else str(__A )
            html_code += f'''      <td>{elt}</td>\n'''
        html_code += "    </tr>\n"
    html_code += "  </tbody>\n</table><p>"
    return html_code
class 							A_    :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					5
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					0.2


 def __init__(	self							,  _A							,  _A = None							,  _A = True							,  _A = None							,  _A = 300							,  ) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				total
     _UpperCAmelCase				:      Optional[int]			  =				'''''' if prefix is None else prefix
     _UpperCAmelCase				:      Any			  =				leave
     _UpperCAmelCase				:      List[Any]			  =				parent
     _UpperCAmelCase				:      Optional[int]			  =				width
     _UpperCAmelCase				:      Optional[int]			  =				None
     _UpperCAmelCase				:      str			  =				None
     _UpperCAmelCase				:      int			  =				None


 def  snake_case__				(	self							,  _A							,  _A = False							,  _A = None) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				value
     if comment is not None:
         _UpperCAmelCase				:      str			  =				comment
     if self.last_value is None:
         _UpperCAmelCase				:      Tuple			  =				time.time()
         _UpperCAmelCase				:      Optional[Any]			  =				value
         _UpperCAmelCase				:      Tuple			  =				None
         _UpperCAmelCase				:      List[str]			  =				self.warmup
         _UpperCAmelCase				:      Optional[Any]			  =				1
         self.update_bar(_A)
     elif value <= self.last_value and not force_update:
         return
     elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for							,  self.total):
         if self.first_calls > 0:
             self.first_calls -= 1
         _UpperCAmelCase				:      Union[str, Any]			  =				time.time()
         _UpperCAmelCase				:      int			  =				current_time - self.start_time
         # We could have value = self.start_value if the update is called twixe with the same start value.
         if value > self.start_value:
             _UpperCAmelCase				:      str			  =				self.elapsed_time / (value - self.start_value)
         else:
             _UpperCAmelCase				:      List[str]			  =				None
         if value >= self.total:
             _UpperCAmelCase				:      str			  =				self.total
             _UpperCAmelCase				:      Optional[Any]			  =				None
             if not self.leave:
                 self.close()
         elif self.average_time_per_item is not None:
             _UpperCAmelCase				:      List[str]			  =				self.average_time_per_item * (self.total - value)
         self.update_bar(_A)
         _UpperCAmelCase				:      str			  =				value
         _UpperCAmelCase				:      List[Any]			  =				current_time
         if self.average_time_per_item is None:
             _UpperCAmelCase				:      str			  =				1
         else:
             _UpperCAmelCase				:      Optional[Any]			  =				max(int(self.update_every / self.average_time_per_item)							,  1)


 def  snake_case__				(	self							,  _A							,  _A=None) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				''' ''' * (len(str(self.total)) - len(str(_A))) + str(_A)
     if self.elapsed_time is None:
         _UpperCAmelCase				:      List[Any]			  =				f'''[{spaced_value}/{self.total} : < :'''
     elif self.predicted_remaining is None:
         _UpperCAmelCase				:      Any			  =				f'''[{spaced_value}/{self.total} {format_time(self.elapsed_time)}'''
     else:
         _UpperCAmelCase				:      int			  =				(
             f'''[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <'''
             f''' {format_time(self.predicted_remaining)}'''
         )
         self.label += f''', {1/self.average_time_per_item:.2f} it/s'''
     self.label += "]" if self.comment is None or len(self.comment) == 0 else f''', {self.comment}]'''
     self.display()


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				html_progress_bar(self.value							,  self.total							,  self.prefix							,  self.label							,  self.width)
     if self.parent is not None:
         # If this is a child bar, the parent will take care of the display.
         self.parent.display()
         return
     if self.output is None:
         _UpperCAmelCase				:      Tuple			  =				disp.display(disp.HTML(self.html_code)							,  display_id=_A)
     else:
         self.output.update(disp.HTML(self.html_code))


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     if self.parent is None and self.output is not None:
         self.output.update(disp.HTML(''''''))



class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A=None) ->		Optional[Any]:

     """simple docstring"""



     super().__init__(_A)
     _UpperCAmelCase				:      List[str]			  =				None if column_names is None else [column_names]
     _UpperCAmelCase				:      Any			  =				None


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				html_progress_bar(self.value							,  self.total							,  self.prefix							,  self.label							,  self.width)
     if self.inner_table is not None:
         self.html_code += text_to_html_table(self.inner_table)
     if self.child_bar is not None:
         self.html_code += self.child_bar.html_code
     if self.output is None:
         _UpperCAmelCase				:      Optional[int]			  =				disp.display(disp.HTML(self.html_code)							,  display_id=_A)
     else:
         self.output.update(disp.HTML(self.html_code))


 def  snake_case__				(	self							,  _A) ->		str:

     """simple docstring"""



     if self.inner_table is None:
         _UpperCAmelCase				:      Union[str, Any]			  =				[list(values.keys()), list(values.values())]
     else:
         _UpperCAmelCase				:      Tuple			  =				self.inner_table[0]
         if len(self.inner_table) == 1:
             # We give a chance to update the column names at the first iteration
             for key in values.keys():
                 if key not in columns:
                     columns.append(_A)
             _UpperCAmelCase				:      str			  =				columns
         self.inner_table.append([values[c] for c in columns])


 def  snake_case__				(	self							,  _A							,  _A=None							,  _A=300) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				NotebookProgressBar(_A							,  prefix=_A							,  parent=self							,  width=_A)
     return self.child_bar


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				None
     self.display()



class 							A_    (						__lowercase			):






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				None
     _UpperCAmelCase				:      int			  =				None
     _UpperCAmelCase				:      List[Any]			  =				False


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  **_A) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				'''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
     _UpperCAmelCase				:      List[str]			  =				0
     _UpperCAmelCase				:      List[Any]			  =				0
     _UpperCAmelCase				:      List[Any]			  =				[self.first_column] + ['''Training Loss''']
     if args.evaluation_strategy != IntervalStrategy.NO:
         column_names.append('''Validation Loss''')
     _UpperCAmelCase				:      int			  =				NotebookTrainingTracker(state.max_steps							,  _A)


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  **_A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				int(state.epoch) if int(state.epoch) == state.epoch else f'''{state.epoch:.2f}'''
     self.training_tracker.update(
         state.global_step + 1							,  comment=f'''Epoch {epoch}/{state.num_train_epochs}'''							,  force_update=self._force_next_update							,  )
     _UpperCAmelCase				:      List[str]			  =				False


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		List[str]:

     """simple docstring"""



     if not has_length(_A):
         return
     if self.prediction_bar is None:
         if self.training_tracker is not None:
             _UpperCAmelCase				:      Union[str, Any]			  =				self.training_tracker.add_child(len(_A))
         else:
             _UpperCAmelCase				:      Dict			  =				NotebookProgressBar(len(_A))
         self.prediction_bar.update(1)
     else:
         self.prediction_bar.update(self.prediction_bar.value + 1)


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  **_A) ->		str:

     """simple docstring"""



     if self.prediction_bar is not None:
         self.prediction_bar.close()
     _UpperCAmelCase				:      List[Any]			  =				None


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		List[Any]:

     """simple docstring"""



     if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
         _UpperCAmelCase				:      Any			  =				{'''Training Loss''': logs['''loss''']}
         # First column is necessarily Step sine we're not in epoch eval strategy
         _UpperCAmelCase				:      Union[str, Any]			  =				state.global_step
         self.training_tracker.write_line(_A)


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		Dict:

     """simple docstring"""



     if self.training_tracker is not None:
         _UpperCAmelCase				:      List[str]			  =				{'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
         for log in reversed(state.log_history):
             if "loss" in log:
                 _UpperCAmelCase				:      Any			  =				log['''loss''']
                 break

         if self.first_column == "Epoch":
             _UpperCAmelCase				:      Optional[int]			  =				int(state.epoch)
         else:
             _UpperCAmelCase				:      Union[str, Any]			  =				state.global_step
         _UpperCAmelCase				:      List[str]			  =				'''eval'''
         for k in metrics:
             if k.endswith('''_loss'''):
                 _UpperCAmelCase				:      str			  =				re.sub(R'''\_loss$'''							,  ''''''							,  _A)
         _UpperCAmelCase				:      Optional[Any]			  =				metrics.pop('''total_flos'''							,  _A)
         _UpperCAmelCase				:      Optional[int]			  =				metrics.pop('''epoch'''							,  _A)
         _UpperCAmelCase				:      Union[str, Any]			  =				metrics.pop(f'''{metric_key_prefix}_runtime'''							,  _A)
         _UpperCAmelCase				:      List[Any]			  =				metrics.pop(f'''{metric_key_prefix}_samples_per_second'''							,  _A)
         _UpperCAmelCase				:      Optional[Any]			  =				metrics.pop(f'''{metric_key_prefix}_steps_per_second'''							,  _A)
         _UpperCAmelCase				:      List[str]			  =				metrics.pop(f'''{metric_key_prefix}_jit_compilation_time'''							,  _A)
         for k, v in metrics.items():
             if k == f'''{metric_key_prefix}_loss''':
                 _UpperCAmelCase				:      Optional[int]			  =				v
             else:
                 _UpperCAmelCase				:      Union[str, Any]			  =				k.split('''_''')
                 _UpperCAmelCase				:      List[str]			  =				''' '''.join([part.capitalize() for part in splits[1:]])
                 _UpperCAmelCase				:      List[str]			  =				v
         self.training_tracker.write_line(_A)
         self.training_tracker.remove_child()
         _UpperCAmelCase				:      List[str]			  =				None
         # Evaluation takes a long time so we should force the next update.
         _UpperCAmelCase				:      List[Any]			  =				True


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  **_A) ->		List[str]:

     """simple docstring"""



     self.training_tracker.update(
         state.global_step							,  comment=f'''Epoch {int(state.epoch)}/{state.num_train_epochs}'''							,  force_update=_A)
     _UpperCAmelCase				:      Optional[Any]			  =				None




