"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE									=						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE									=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for attribute in key.split('.'			):
		_lowercase :    List[str]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			)

	if weight_type is not None:
		_lowercase :    List[str]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			).shape
	else:
		_lowercase :    Tuple							=							hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
		    F''' {value.shape} for {full_name}'''			)

	if weight_type == "weight":
		_lowercase :    Dict							=							value
	elif weight_type == "weight_g":
		_lowercase :    str							=							value
	elif weight_type == "weight_v":
		_lowercase :    Optional[int]							=							value
	elif weight_type == "bias":
		_lowercase :    Optional[int]							=							value
	elif weight_type == "running_mean":
		_lowercase :    Dict							=							value
	elif weight_type == "running_var":
		_lowercase :    Tuple							=							value
	elif weight_type == "num_batches_tracked":
		_lowercase :    Tuple							=							value
	elif weight_type == "inv_freq":
		_lowercase :    Optional[int]							=							value
	else:
		_lowercase :    Dict							=							value

	logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[Any]							=							[]
	_lowercase :    Dict							=							fairseq_model.state_dict()

	_lowercase :    Any							=							hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		_lowercase :    Any							=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,hf_model.config.feat_extract_norm == 'group'     ,)
			_lowercase :    Union[str, Any]							=							True
		else:
			for key, mapped_key in MAPPING.items():
				_lowercase :    Any							=							'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
					_lowercase :    Optional[Any]							=							True
					if "*" in mapped_key:
						_lowercase :    Union[str, Any]							=							name.split(__UpperCAmelCase			)[0].split('.'			)[-2]
						_lowercase :    List[Any]							=							mapped_key.replace('*'     ,__UpperCAmelCase			)
					if "pos_bias_u" in name:
						_lowercase :    int							=							None
					elif "pos_bias_v" in name:
						_lowercase :    Tuple							=							None
					elif "weight_g" in name:
						_lowercase :    Dict							=							'weight_g'
					elif "weight_v" in name:
						_lowercase :    Union[str, Any]							=							'weight_v'
					elif "bias" in name:
						_lowercase :    Dict							=							'bias'
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						_lowercase :    Any							=							'weight'
					elif "running_mean" in name:
						_lowercase :    int							=							'running_mean'
					elif "inv_freq" in name:
						_lowercase :    Tuple							=							'inv_freq'
					elif "running_var" in name:
						_lowercase :    Optional[int]							=							'running_var'
					elif "num_batches_tracked" in name:
						_lowercase :    Optional[int]							=							'num_batches_tracked'
					else:
						_lowercase :    Tuple							=							None
					set_recursively(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			)
				continue
		if not is_used:
			unused_weights.append(__UpperCAmelCase			)

	logger.warning(F'''Unused weights: {unused_weights}'''			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							full_name.split('conv_layers.'			)[-1]
	_lowercase :    List[str]							=							name.split('.'			)
	_lowercase :    int							=							int(items[0]			)
	_lowercase :    List[Any]							=							int(items[1]			)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''			)
			_lowercase :    Optional[int]							=							value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''			)
			_lowercase :    Tuple							=							value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''			)
			_lowercase :    List[Any]							=							value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''			)
			_lowercase :    Dict							=							value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
	else:
		unused_weights.append(__UpperCAmelCase			)


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=True			):






	"""simple docstring"""





	if config_path is not None:
		_lowercase :    Tuple							=							WavaVecaConformerConfig.from_pretrained(__UpperCAmelCase     ,hidden_act='swish'			)
	else:
		_lowercase :    Optional[int]							=							WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		_lowercase :    Optional[Any]							=							'rotary'

	if is_finetuned:
		if dict_path:
			_lowercase :    Tuple							=							Dictionary.load(__UpperCAmelCase			)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_lowercase :    Dict							=							target_dict.pad_index
			_lowercase :    Any							=							target_dict.bos_index
			_lowercase :    Tuple							=							target_dict.eos_index
			_lowercase :    List[Any]							=							len(target_dict.symbols			)
			_lowercase :    Dict							=							os.path.join(__UpperCAmelCase     ,'vocab.json'			)
			if not os.path.isdir(__UpperCAmelCase			):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__UpperCAmelCase			)			)
				return
			os.makedirs(__UpperCAmelCase     ,exist_ok=__UpperCAmelCase			)
			_lowercase :    Tuple							=							target_dict.indices

			# fairseq has the <pad> and <s> switched
			_lowercase :    Optional[Any]							=							0
			_lowercase :    Union[str, Any]							=							1
			with open(__UpperCAmelCase     ,'w'     ,encoding='utf-8'			) as vocab_handle:
				json.dump(__UpperCAmelCase     ,__UpperCAmelCase			)
			_lowercase :    Optional[int]							=							WavaVecaCTCTokenizer(
			    __UpperCAmelCase     ,unk_token=target_dict.unk_word     ,pad_token=target_dict.pad_word     ,bos_token=target_dict.bos_word     ,eos_token=target_dict.eos_word     ,word_delimiter_token='|'     ,do_lower_case=__UpperCAmelCase     ,)
			_lowercase :    Any							=							True if config.feat_extract_norm == 'layer' else False
			_lowercase :    Union[str, Any]							=							WavaVecaFeatureExtractor(
			    feature_size=1     ,sampling_rate=16_000     ,padding_value=0     ,do_normalize=__UpperCAmelCase     ,return_attention_mask=__UpperCAmelCase     ,)
			_lowercase :    Tuple							=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase     ,tokenizer=__UpperCAmelCase			)
			processor.save_pretrained(__UpperCAmelCase			)

		_lowercase :    Union[str, Any]							=							WavaVecaConformerForCTC(__UpperCAmelCase			)
	else:
		_lowercase :    Tuple							=							WavaVecaConformerForPreTraining(__UpperCAmelCase			)

	if is_finetuned:
		_lowercase				,      _lowercase				,      _lowercase :    int							=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]     ,arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
	else:
		_lowercase :    List[str]							=							argparse.Namespace(task='audio_pretraining'			)
		_lowercase :    List[Any]							=							fairseq.tasks.setup_task(__UpperCAmelCase			)

		_lowercase				,      _lowercase				,      _lowercase :    Union[str, Any]							=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,task=__UpperCAmelCase			)

	_lowercase :    str							=							model[0].eval()

	recursively_load_weights(__UpperCAmelCase     ,__UpperCAmelCase     ,not is_finetuned			)

	hf_wavavec.save_pretrained(__UpperCAmelCase			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
						parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						parser.add_argument(
						    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						convert_wavaveca_conformer_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)




"""simple docstring"""







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(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Optional[int]							=							int(__UpperCAmelCase			)
	_lowercase				,      _lowercase				,      _lowercase :    Union[str, Any]							=							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(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=300			):






	"""simple docstring"""





	return F'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[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:
			_lowercase :    Optional[Any]							=							F'''{elt:.6f}''' if isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) else str(__UpperCAmelCase			)
			html_code += F'''      <td>{elt}</td>\n'''
		html_code += "    </tr>\n"
	html_code += "  </tbody>\n</table><p>"
	return html_code



class  _lowerCamelCase			:
				_snake_case     =  5
				_snake_case     =  0.2







				def __init__( self    :       List[str]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Optional[str] = None			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Optional["NotebookTrainingTracker"] = None			,					lowerCamelCase_    :       int = 3_0_0			,					):

					"""simple docstring"""






					_lowercase :    int							=							total
					_lowercase :    Dict							=							'' if prefix is None else prefix
					_lowercase :    List[Any]							=							leave
					_lowercase :    Dict							=							parent
					_lowercase :    str							=							width
					_lowercase :    Any							=							None
					_lowercase :    Optional[int]							=							None
					_lowercase :    Union[str, Any]							=							None







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       str = None						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Optional[Any]=None						):

					"""simple docstring"""






					_lowercase :    str							=							' ' * (len(str(self.total						)						) - len(str(lowerCamelCase_						)						)) + str(lowerCamelCase_						)
					if self.elapsed_time is None:
						_lowercase :    Dict							=							F'''[{spaced_value}/{self.total} : < :'''
					elif self.predicted_remaining is None:
						_lowercase :    Optional[int]							=							F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time						)}'''
					else:
						_lowercase :    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 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    int							=							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:
						_lowercase :    List[str]							=							disp.display(disp.HTML(self.html_code						)			,					display_id=lowerCamelCase_						)
					else:
						self.output.update(disp.HTML(self.html_code						)						)







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






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



class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Optional[int]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Tuple=None						):

					"""simple docstring"""






					super().__init__(lowerCamelCase_						)
					_lowercase :    Tuple							=							None if column_names is None else [column_names]
					_lowercase :    Optional[int]							=							None







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							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:
						_lowercase :    Dict							=							disp.display(disp.HTML(self.html_code						)			,					display_id=lowerCamelCase_						)
					else:
						self.output.update(disp.HTML(self.html_code						)						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					if self.inner_table is None:
						_lowercase :    Optional[int]							=							[list(values.keys()						), list(values.values()						)]
					else:
						_lowercase :    str							=							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(lowerCamelCase_						)
							_lowercase :    List[Any]							=							columns
						self.inner_table.append([values[c] for c in columns]						)







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Union[str, Any]=None			,					lowerCamelCase_    :       int=3_0_0						):

					"""simple docstring"""






					_lowercase :    List[str]							=							NotebookProgressBar(lowerCamelCase_			,					prefix=lowerCamelCase_			,					parent=self			,					width=lowerCamelCase_						)
					return self.child_bar







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Any							=							None
					self.display()



class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    int							=							None
					_lowercase :    Any							=							None
					_lowercase :    Optional[int]							=							False







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    Any							=							'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
					_lowercase :    Dict							=							0
					_lowercase :    Optional[Any]							=							0
					_lowercase :    Union[str, Any]							=							[self.first_column] + ['Training Loss']
					if args.evaluation_strategy != IntervalStrategy.NO:
						column_names.append('Validation Loss'						)
					_lowercase :    Tuple							=							NotebookTrainingTracker(state.max_steps			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Union[str, Any]			,					**lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase :    Optional[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			,					)
					_lowercase :    Tuple							=							False







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Union[str, Any]=None			,					**lowerCamelCase_    :       str						):

					"""simple docstring"""






					if not has_length(lowerCamelCase_						):
						return
					if self.prediction_bar is None:
						if self.training_tracker is not None:
							_lowercase :    Tuple							=							self.training_tracker.add_child(len(lowerCamelCase_						)						)
						else:
							_lowercase :    Optional[Any]							=							NotebookProgressBar(len(lowerCamelCase_						)						)
						self.prediction_bar.update(1						)
					else:
						self.prediction_bar.update(self.prediction_bar.value + 1						)







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Dict			,					**lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					if self.prediction_bar is not None:
						self.prediction_bar.close()
					_lowercase :    Union[str, Any]							=							None







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       int=None			,					**lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       str=None			,					**lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






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

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







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Union[str, Any]			,					**lowerCamelCase_    :       Any						):

					"""simple docstring"""






					self.training_tracker.update(
					    state.global_step			,					comment=F'''Epoch {int(state.epoch						)}/{state.num_train_epochs}'''			,					force_update=lowerCamelCase_						)
					_lowercase :    List[Any]							=							None



