

import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
						from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
						lowerCAmelCase							:  List[Any]   	=						True
						from torch.cuda.amp import autocast


lowerCAmelCase							:  Union[str, Any]   	=						logging.getLogger(__name__)



@dataclass
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
			_UpperCAmelCase   :							Optional[str]										=     field(
			    default=UpperCAmelCase_		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,       )
			_UpperCAmelCase   :							Optional[bool]										=     field(
			    default=UpperCAmelCase_		,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}	)
			_UpperCAmelCase   :							Optional[bool]										=     field(
			    default=UpperCAmelCase_		,       metadata={'''help''': '''Whether to log verbose messages or not.'''}		,       )
			_UpperCAmelCase   :							Optional[float]										=     field(
			    default=2.0		,       metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}	)
			_UpperCAmelCase   :							Optional[float]										=     field(
			    default=0.5		,       metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}	)
			_UpperCAmelCase   :							Optional[float]										=     field(
			    default=0.999_995		,       metadata={'''help''': '''Decay of gumbel temperature during training.'''}	)







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,	datefmt="%m/%d/%Y %H:%M:%S"				,	handlers=[logging.StreamHandler(sys.stdout    )]				,	)
						SCREAMING_SNAKE_CASE_: int  		=       logging.WARNING
						if model_args.verbose_logging:
												SCREAMING_SNAKE_CASE_: List[str]  		=       logging.DEBUG
						elif trainer_utils.is_main_process(training_args.local_rank    ):
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       logging.INFO
						logger.setLevel(lowercase_    )



@dataclass
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     field(
			    default=UpperCAmelCase_		,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}	)
			_UpperCAmelCase   :							Optional[str]										=     field(
			    default=UpperCAmelCase_		,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
			_UpperCAmelCase   :							Optional[str]										=     field(
			    default='''train'''		,       metadata={
			        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
			    }		,       )
			_UpperCAmelCase   :							Optional[str]										=     field(
			    default='''validation'''		,       metadata={
			        '''help''': (
			            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
			        )
			    }		,       )
			_UpperCAmelCase   :							Optional[str]										=     field(
			    default='''file'''		,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}		,       )
			_UpperCAmelCase   :							bool										=     field(
			    default=UpperCAmelCase_		,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}	)
			_UpperCAmelCase   :							Optional[int]										=     field(
			    default=1		,       metadata={
			        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
			    }		,       )
			_UpperCAmelCase   :							Optional[int]										=     field(
			    default=UpperCAmelCase_		,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}		,       )
			_UpperCAmelCase   :							Optional[float]										=     field(
			    default=20.0		,       metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}	)







@dataclass
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							WavaVecaForPreTraining
			_UpperCAmelCase   :							WavaVecaFeatureExtractor
			_UpperCAmelCase   :							Union[bool, str]										=     "longest"
			_UpperCAmelCase   :							Optional[int]										=     None
			_UpperCAmelCase   :							Optional[int]										=     None



			def __call__( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]):
									# reformat list to dict and set to pytorch format
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.feature_extractor.pad(
									    A__   ,						max_length=self.max_length   ,						padding=self.padding   ,						pad_to_multiple_of=self.pad_to_multiple_of   ,						return_tensors="pt"   ,						)
									SCREAMING_SNAKE_CASE_: Dict  		=       self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1])

									SCREAMING_SNAKE_CASE_: List[Any]  		=       batch["""input_values"""].shape[0]

									# make sure that no loss is computed on padded inputs
									if batch["attention_mask"] is not None:
															# compute real output lengths according to convolution formula
															SCREAMING_SNAKE_CASE_: Any  		=       self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1)).to(
															    torch.long)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.zeros(
															    (batch_size, mask_indices_seq_length)   ,						dtype=torch.long   ,						device=batch["input_values"].device)

															# these two operations makes sure that all values
															# before the output lengths indices are attended to
															SCREAMING_SNAKE_CASE_: int  		=       1
															SCREAMING_SNAKE_CASE_: int  		=       attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()

									# sample randomly masked indices
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       _compute_mask_indices(
									    (batch_size, mask_indices_seq_length)   ,						self.model.config.mask_time_prob   ,						self.model.config.mask_time_length   ,						attention_mask=A__   ,						min_masks=2   ,						)

									return batch







class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     List[str]   ,						*lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Optional[Any]=1   ,						lowerCAmelCase__  :     Any=0   ,						lowerCAmelCase__  :     str=1.0   ,						**lowerCAmelCase__  :     Any):
									super().__init__(*A__   ,						**A__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       0
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       max_gumbel_temp
									SCREAMING_SNAKE_CASE_: List[Any]  		=       min_gumbel_temp
									SCREAMING_SNAKE_CASE_: List[Any]  		=       gumbel_temp_decay




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     str):
									model.train()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._prepare_inputs(A__)

									if self.use_amp:
															with autocast():
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.compute_loss(A__   ,						A__)
									else:
															SCREAMING_SNAKE_CASE_: int  		=       self.compute_loss(A__   ,						A__)

									if self.args.n_gpu > 1 or self.deepspeed:
															if model.module.config.ctc_loss_reduction == "mean":
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       loss.mean()
															elif model.module.config.ctc_loss_reduction == "sum":
																					SCREAMING_SNAKE_CASE_: List[str]  		=       loss.sum() / (inputs["""mask_time_indices"""]).sum()
															else:
																					raise ValueError(F"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']")

									if self.args.gradient_accumulation_steps > 1:
															SCREAMING_SNAKE_CASE_: Dict  		=       loss / self.args.gradient_accumulation_steps

									if self.use_amp:
															self.scaler.scale(A__).backward()
									elif self.use_apex:
															with amp.scale_loss(A__   ,						self.optimizer) as scaled_loss:
																					scaled_loss.backward()
									elif self.deepspeed:
															self.deepspeed.backward(A__)
									else:
															loss.backward()

									self.num_update_step += 1
									# make sure gumbel softmax temperature is decayed
									if self.args.n_gpu > 1 or self.deepspeed:
															model.module.set_gumbel_temperature(
															    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step   ,						self.min_gumbel_temp))
									else:
															model.set_gumbel_temperature(
															    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step   ,						self.min_gumbel_temp))

									return loss.detach()







def 			A_      (    ):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						SCREAMING_SNAKE_CASE_: Tuple  		=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

						SCREAMING_SNAKE_CASE_: List[Any]  		=       parser.parse_args_into_dataclasses()
						configure_logger(lowercase_				,	lowercase_    )

						# Downloading and loading a dataset from the hub.
						SCREAMING_SNAKE_CASE_: List[str]  		=       load_dataset(data_args.dataset_name				,	data_args.dataset_config_name				,	cache_dir=model_args.cache_dir    )

						if "validation" not in datasets.keys():
												# make sure only "validation" and "train" keys remain"
												SCREAMING_SNAKE_CASE_: Dict  		=       DatasetDict()
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       load_dataset(
												    data_args.dataset_name				,	data_args.dataset_config_name				,	split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"				,	cache_dir=model_args.cache_dir				,	)
												SCREAMING_SNAKE_CASE_: List[Any]  		=       load_dataset(
												    data_args.dataset_name				,	data_args.dataset_config_name				,	split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"				,	cache_dir=model_args.cache_dir				,	)
						else:
												# make sure only "validation" and "train" keys remain"
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       DatasetDict()
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       load_dataset(
												    data_args.dataset_name				,	data_args.dataset_config_name				,	split="validation"				,	cache_dir=model_args.cache_dir				,	)
												SCREAMING_SNAKE_CASE_: List[str]  		=       load_dataset(
												    data_args.dataset_name				,	data_args.dataset_config_name				,	split=f"{data_args.train_split_name}"				,	cache_dir=model_args.cache_dir				,	)

						# only normalized-inputs-training is supported
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       WavaVecaFeatureExtractor.from_pretrained(
						    model_args.model_name_or_path				,	cache_dir=model_args.cache_dir				,	do_normalize=lowercase_    )

						def prepare_dataset(_UpperCAmelCase    ):
												# check that all files have the correct sampling rate
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       librosa.load(batch[data_args.speech_file_column]				,	sr=feature_extractor.sampling_rate    )
												return batch

						# load audio files into numpy arrays
						SCREAMING_SNAKE_CASE_: List[Any]  		=       datasets.map(
						    lowercase_				,	num_proc=data_args.preprocessing_num_workers				,	remove_columns=datasets["train"].column_names    )

						# filter audio files that are too long
						SCREAMING_SNAKE_CASE_: int  		=       vectorized_datasets.filter(
						    lambda _UpperCAmelCase    : len(data["speech"]    ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate    )    )

						def normalize(_UpperCAmelCase    ):
												return feature_extractor(batch["speech"]				,	sampling_rate=feature_extractor.sampling_rate    )

						# normalize and transform to `BatchFeatures`
						SCREAMING_SNAKE_CASE_: List[str]  		=       vectorized_datasets.map(
						    lowercase_				,	batched=lowercase_				,	num_proc=data_args.preprocessing_num_workers				,	load_from_cache_file=not data_args.overwrite_cache				,	remove_columns=vectorized_datasets["train"].column_names				,	)

						# pretraining is only supported for "newer" stable layer norm architecture
						# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
						SCREAMING_SNAKE_CASE_: int  		=       WavaVecaConfig.from_pretrained(
						    model_args.model_name_or_path				,	cache_dir=model_args.cache_dir				,	gradient_checkpointing=training_args.gradient_checkpointing				,	)

						if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
												raise ValueError(
												    "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
												    " ``config.feat_extract_norm='layer'"    )

						SCREAMING_SNAKE_CASE_: int  		=       WavaVecaForPreTraining(lowercase_    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       DataCollatorForWavaVecaPretraining(model=lowercase_				,	feature_extractor=lowercase_    )

						SCREAMING_SNAKE_CASE_: Optional[int]  		=       WavaVecaPreTrainer(
						    model=lowercase_				,	data_collator=lowercase_				,	args=lowercase_				,	train_dataset=vectorized_datasets["train"]				,	eval_dataset=vectorized_datasets["validation"]				,	tokenizer=lowercase_				,	max_gumbel_temp=model_args.max_gumbel_temperature				,	min_gumbel_temp=model_args.min_gumbel_temperature				,	gumbel_temp_decay=model_args.gumbel_temperature_decay				,	)
						trainer.train()


if __name__ == "__main__":
						main()









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 						UpperCamelCase					(lowercase_: str					)     ->      Dict:
	A__							:    int        =		int(lowercase_					)
	A__   ,   A__   ,   A__							:    Tuple        =		t // 3600, (t // 60) % 60, t % 60
	return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: Optional[Any]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Tuple    ,      lowercase_: Any=300					)     ->      Optional[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 						UpperCamelCase					(lowercase_: Tuple					)     ->      Optional[int]:
	A__							:    Tuple        =		"""<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:
			A__							:    str        =		f"""{elt:.6f}""" if isinstance(lowercase_    ,      lowercase_					) else str(lowercase_					)
			html_code += f"""      <td>{elt}</td>\n"""
		html_code += "    </tr>\n"
	html_code += "  </tbody>\n</table><p>"
	return html_code



class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   str  							=       5
					UpperCAmelCase__:   int  							=       0.2






					def __init__( self    ,       A__    ,       A__ = None    ,       A__ = True    ,       A__ = None    ,       A__ = 300    ,       ):
						A__							:    Optional[int]        =		total
						A__							:    Tuple        =		"""""" if prefix is None else prefix
						A__							:    str        =		leave
						A__							:    str        =		parent
						A__							:    int        =		width
						A__							:    Dict        =		None
						A__							:    List[str]        =		None
						A__							:    Optional[int]        =		None






					def   __A			( self    ,       A__    ,       A__ = False    ,       A__ = None			):
						A__							:    Tuple        =		value
						if comment is not None:
							A__							:    Any        =		comment
						if self.last_value is None:
							A__							:    int        =		time.time()
							A__							:    Dict        =		value
							A__							:    int        =		None
							A__							:    int        =		self.warmup
							A__							:    str        =		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
							A__							:    Any        =		time.time()
							A__							:    str        =		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:
								A__							:    Dict        =		self.elapsed_time / (value - self.start_value)
							else:
								A__							:    List[str]        =		None
							if value >= self.total:
								A__							:    Optional[Any]        =		self.total
								A__							:    List[Any]        =		None
								if not self.leave:
									self.close()
							elif self.average_time_per_item is not None:
								A__							:    List[Any]        =		self.average_time_per_item * (self.total - value)
							self.update_bar(A__			)
							A__							:    Any        =		value
							A__							:    List[str]        =		current_time
							if self.average_time_per_item is None:
								A__							:    str        =		1
							else:
								A__							:    Optional[Any]        =		max(int(self.update_every / self.average_time_per_item			)    ,       1			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Tuple        =		""" """ * (len(str(self.total			)			) - len(str(A__			)			)) + str(A__			)
						if self.elapsed_time is None:
							A__							:    Union[str, Any]        =		F"""[{spaced_value}/{self.total} : < :"""
						elif self.predicted_remaining is None:
							A__							:    Tuple        =		F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time			)}"""
						else:
							A__							:    Optional[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   __A			( self			):
						A__							:    str        =		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:
							A__							:    str        =		disp.display(disp.HTML(self.html_code			)    ,       display_id=A__			)
						else:
							self.output.update(disp.HTML(self.html_code			)			)






					def   __A			( self			):
						if self.parent is None and self.output is not None:
							self.output.update(disp.HTML(""""""			)			)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=None			):
						super().__init__(A__			)
						A__							:    Optional[Any]        =		None if column_names is None else [column_names]
						A__							:    Optional[Any]        =		None






					def   __A			( self			):
						A__							:    List[str]        =		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:
							A__							:    Optional[int]        =		disp.display(disp.HTML(self.html_code			)    ,       display_id=A__			)
						else:
							self.output.update(disp.HTML(self.html_code			)			)






					def   __A			( self    ,       A__			):
						if self.inner_table is None:
							A__							:    List[str]        =		[list(values.keys()			), list(values.values()			)]
						else:
							A__							:    Optional[Any]        =		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__			)
								A__							:    Any        =		columns
							self.inner_table.append([values[c] for c in columns]			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=300			):
						A__							:    Optional[Any]        =		NotebookProgressBar(A__    ,       prefix=A__    ,       parent=self    ,       width=A__			)
						return self.child_bar






					def   __A			( self			):
						A__							:    List[str]        =		None
						self.display()



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self			):
						A__							:    int        =		None
						A__							:    List[str]        =		None
						A__							:    Union[str, Any]        =		False






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						A__							:    List[str]        =		"""Epoch""" if args.evaluation_strategy == IntervalStrategy.EPOCH else """Step"""
						A__							:    Dict        =		0
						A__							:    Tuple        =		0
						A__							:    Optional[int]        =		[self.first_column] + ["""Training Loss"""]
						if args.evaluation_strategy != IntervalStrategy.NO:
							column_names.append("""Validation Loss"""			)
						A__							:    Union[str, Any]        =		NotebookTrainingTracker(state.max_steps    ,       A__			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						A__							:    Any        =		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    ,       )
						A__							:    str        =		False






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__=None    ,       **A__			):
						if not has_length(A__			):
							return
						if self.prediction_bar is None:
							if self.training_tracker is not None:
								A__							:    Union[str, Any]        =		self.training_tracker.add_child(len(A__			)			)
							else:
								A__							:    Tuple        =		NotebookProgressBar(len(A__			)			)
							self.prediction_bar.update(1			)
						else:
							self.prediction_bar.update(self.prediction_bar.value + 1			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						if self.prediction_bar is not None:
							self.prediction_bar.close()
						A__							:    List[str]        =		None






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__=None    ,       **A__			):
						# Only for when there is no evaluation
						if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
							A__							:    Dict        =		{"""Training Loss""": logs["""loss"""]}
							# First column is necessarily Step sine we're not in epoch eval strategy
							A__							:    List[Any]        =		state.global_step
							self.training_tracker.write_line(A__			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__=None    ,       **A__			):
						if self.training_tracker is not None:
							A__							:    Tuple        =		{"""Training Loss""": """No log""", """Validation Loss""": """No log"""}
							for log in reversed(state.log_history			):
								if "loss" in log:
									A__							:    Dict        =		log["""loss"""]
									break

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






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						self.training_tracker.update(
						    state.global_step    ,       comment=F"""Epoch {int(state.epoch			)}/{state.num_train_epochs}"""    ,       force_update=A__			)
						A__							:    Optional[int]        =		None



