




'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							:	Optional[Any]	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	str	     =		{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}







def       _UpperCamelCase			(_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  List[Any]						)-> str:




     '''simple docstring'''





     for attribute in key.split('''.'''						):
          __snake_case            =	getattr(_lowerCamelCase ,						_lowerCamelCase						)

     if weight_type is not None:
          __snake_case            =	getattr(_lowerCamelCase ,						_lowerCamelCase						).shape
     else:
          __snake_case            =	hf_pointer.shape

     assert hf_shape == value.shape, (
         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":
          __snake_case            =	value
     elif weight_type == "weight_g":
          __snake_case            =	value
     elif weight_type == "weight_v":
          __snake_case            =	value
     elif weight_type == "bias":
          __snake_case            =	value
     else:
          __snake_case            =	value

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







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  Optional[int]						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	fairseq_model.state_dict()

     __snake_case            =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __snake_case            =	False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase ,						hf_model.config.feat_extract_norm == '''group''' ,						)
               __snake_case            =	True
          else:
               for key, mapped_key in MAPPING.items():
                    __snake_case            =	'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                    if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                         __snake_case            =	True
                         if "*" in mapped_key:
                              __snake_case            =	name.split(_lowerCamelCase						)[0].split('''.'''						)[-2]
                              __snake_case            =	mapped_key.replace('''*''' ,						_lowerCamelCase						)
                         if "weight_g" in name:
                              __snake_case            =	'''weight_g'''
                         elif "weight_v" in name:
                              __snake_case            =	'''weight_v'''
                         elif "weight" in name:
                              __snake_case            =	'''weight'''
                         elif "bias" in name:
                              __snake_case            =	'''bias'''
                         else:
                              __snake_case            =	None
                         set_recursively(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    continue
          if not is_used:
               unused_weights.append(_lowerCamelCase						)

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







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Optional[int]						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	full_name.split('''conv_layers.'''						)[-1]
     __snake_case            =	name.split('''.'''						)
     __snake_case            =	int(items[0]						)
     __snake_case            =	int(items[1]						)

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   f'''{full_name} has size {value.shape}, but'''
                   f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
               )
               __snake_case            =	value
               logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   f'''{full_name} has size {value.shape}, but'''
                   f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
               )
               __snake_case            =	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:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                   " found."
               )
               __snake_case            =	value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f'''{full_name} has size {value.shape}, but'''
                   f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
               )
               __snake_case            =	value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
     else:
          unused_weights.append(_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  List[Any]						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	SEWConfig()
     if is_finetuned:
          __snake_case            =	model.wav_encoder.wav_model.cfg
     else:
          __snake_case            =	model.cfg

     __snake_case            =	fs_config.conv_bias
     __snake_case            =	eval(fs_config.conv_feature_layers						)
     __snake_case            =	[x[0] for x in conv_layers]
     __snake_case            =	[x[1] for x in conv_layers]
     __snake_case            =	[x[2] for x in conv_layers]
     __snake_case            =	'''gelu'''
     __snake_case            =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
     __snake_case            =	0.0
     __snake_case            =	fs_config.activation_fn.name
     __snake_case            =	fs_config.encoder_embed_dim
     __snake_case            =	0.02
     __snake_case            =	fs_config.encoder_ffn_embed_dim
     __snake_case            =	1E-5
     __snake_case            =	fs_config.encoder_layerdrop
     __snake_case            =	fs_config.encoder_attention_heads
     __snake_case            =	fs_config.conv_pos_groups
     __snake_case            =	fs_config.conv_pos
     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	fs_config.encoder_layers
     __snake_case            =	fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          __snake_case            =	model.cfg
          __snake_case            =	fs_config.final_dropout
          __snake_case            =	fs_config.layerdrop
     __snake_case            =	fs_config.activation_dropout
     __snake_case            =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     __snake_case            =	fs_config.attention_dropout
     __snake_case            =	fs_config.dropout_input
     __snake_case            =	fs_config.dropout
     __snake_case            =	fs_config.mask_channel_length
     __snake_case            =	fs_config.mask_channel_prob
     __snake_case            =	fs_config.mask_length
     __snake_case            =	fs_config.mask_prob

     __snake_case            =	'''Wav2Vec2FeatureExtractor'''
     __snake_case            =	'''Wav2Vec2CTCTokenizer'''

     return config







@torch.no_grad()
def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  Tuple=None ,						_lowerCamelCase				:  Optional[int]=None ,						_lowerCamelCase				:  Optional[Any]=True						)-> int:




     '''simple docstring'''





     if is_finetuned:
          __snake_case       ,     __snake_case       ,     __snake_case            =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path] ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
     else:
          __snake_case       ,     __snake_case       ,     __snake_case            =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

     if config_path is not None:
          __snake_case            =	SEWConfig.from_pretrained(_lowerCamelCase						)
     else:
          __snake_case            =	convert_config(model[0] ,						_lowerCamelCase						)
     __snake_case            =	model[0].eval()

     __snake_case            =	True if config.feat_extract_norm == '''layer''' else False
     __snake_case            =	WavaVecaFeatureExtractor(
         feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=_lowerCamelCase ,						return_attention_mask=_lowerCamelCase ,						)

     if is_finetuned:
          if dict_path:
               __snake_case            =	Dictionary.load(_lowerCamelCase						)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __snake_case            =	target_dict.pad_index
               __snake_case            =	target_dict.bos_index
               __snake_case            =	target_dict.pad_index
               __snake_case            =	target_dict.bos_index
               __snake_case            =	target_dict.eos_index
               __snake_case            =	len(target_dict.symbols						)
               __snake_case            =	os.path.join(_lowerCamelCase ,						'''vocab.json'''						)
               if not os.path.isdir(_lowerCamelCase						):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCamelCase						)						)
                    return
               os.makedirs(_lowerCamelCase ,						exist_ok=_lowerCamelCase						)
               with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as vocab_handle:
                    json.dump(target_dict.indices ,						_lowerCamelCase						)
               __snake_case            =	WavaVecaCTCTokenizer(
                   _lowerCamelCase ,						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=_lowerCamelCase ,						)
               __snake_case            =	WavaVecaProcessor(feature_extractor=_lowerCamelCase ,						tokenizer=_lowerCamelCase						)
               processor.save_pretrained(_lowerCamelCase						)

          __snake_case            =	SEWForCTC(_lowerCamelCase						)
     else:
          __snake_case            =	SEWModel(_lowerCamelCase						)
          feature_extractor.save_pretrained(_lowerCamelCase						)

     recursively_load_weights(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCAmelCase_							:	Optional[int]	     =		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(
         '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     UpperCAmelCase_							:	str	     =		parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_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       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	int(_lowerCamelCase						)
     __snake_case       ,     __snake_case       ,     __snake_case            =	t // 36_00, (t // 60) % 60, t % 60
     return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''







def       _UpperCamelCase			(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  List[Any]=3_00						)-> int:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	'''<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:
               __snake_case            =	f'''{elt:.6f}''' if isinstance(_lowerCamelCase ,						_lowerCamelCase						) else str(_lowerCamelCase						)
               html_code += f'''      <td>{elt}</td>\n'''
          html_code += "    </tr>\n"
     html_code += "  </tbody>\n</table><p>"
     return html_code





class      lowerCAmelCase :
       __lowercase	:   str										=      5
       __lowercase	:   Optional[Any]										=      0.2


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 300     ,	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	total
            __snake_case            =	'''''' if prefix is None else prefix
            __snake_case            =	leave
            __snake_case            =	parent
            __snake_case            =	width
            __snake_case            =	None
            __snake_case            =	None
            __snake_case            =	None


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	value
            if comment is not None:
                 __snake_case            =	comment
            if self.last_value is None:
                 __snake_case            =	__snake_case            =	time.time()
                 __snake_case            =	__snake_case            =	value
                 __snake_case            =	__snake_case            =	None
                 __snake_case            =	self.warmup
                 __snake_case            =	1
                 self.update_bar(__SCREAMING_SNAKE_CASE	)
            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
                 __snake_case            =	time.time()
                 __snake_case            =	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:
                      __snake_case            =	self.elapsed_time / (value - self.start_value)
                 else:
                      __snake_case            =	None
                 if value >= self.total:
                      __snake_case            =	self.total
                      __snake_case            =	None
                      if not self.leave:
                           self.close()
                 elif self.average_time_per_item is not None:
                      __snake_case            =	self.average_time_per_item * (self.total - value)
                 self.update_bar(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	value
                 __snake_case            =	current_time
                 if self.average_time_per_item is None:
                      __snake_case            =	1
                 else:
                      __snake_case            =	max(int(self.update_every / self.average_time_per_item	)     ,	1	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	''' ''' * (len(str(self.total	)	) - len(str(__SCREAMING_SNAKE_CASE	)	)) + str(__SCREAMING_SNAKE_CASE	)
            if self.elapsed_time is None:
                 __snake_case            =	F'''[{spaced_value}/{self.total} : < :'''
            elif self.predicted_remaining is None:
                 __snake_case            =	F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time	)}'''
            else:
                 __snake_case            =	(
                     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    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	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:
                 __snake_case            =	disp.display(disp.HTML(self.html_code	)     ,	display_id=__SCREAMING_SNAKE_CASE	)
            else:
                 self.output.update(disp.HTML(self.html_code	)	)




       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




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





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	Any:



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	None if column_names is None else [column_names]
            __snake_case            =	None


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	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:
                 __snake_case            =	disp.display(disp.HTML(self.html_code	)     ,	display_id=__SCREAMING_SNAKE_CASE	)
            else:
                 self.output.update(disp.HTML(self.html_code	)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            if self.inner_table is None:
                 __snake_case            =	[list(values.keys()	), list(values.values()	)]
            else:
                 __snake_case            =	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(__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	columns
                 self.inner_table.append([values[c] for c in columns]	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=300	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	NotebookProgressBar(__SCREAMING_SNAKE_CASE     ,	prefix=__SCREAMING_SNAKE_CASE     ,	parent=self     ,	width=__SCREAMING_SNAKE_CASE	)
            return self.child_bar




       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	None
            self.display()





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	None
            __snake_case            =	None
            __snake_case            =	False


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	'''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
            __snake_case            =	0
            __snake_case            =	0
            __snake_case            =	[self.first_column] + ['''Training Loss''']
            if args.evaluation_strategy != IntervalStrategy.NO:
                 column_names.append('''Validation Loss'''	)
            __snake_case            =	NotebookTrainingTracker(state.max_steps     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	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     ,	)
            __snake_case            =	False


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            if not has_length(__SCREAMING_SNAKE_CASE	):
                 return
            if self.prediction_bar is None:
                 if self.training_tracker is not None:
                      __snake_case            =	self.training_tracker.add_child(len(__SCREAMING_SNAKE_CASE	)	)
                 else:
                      __snake_case            =	NotebookProgressBar(len(__SCREAMING_SNAKE_CASE	)	)
                 self.prediction_bar.update(1	)
            else:
                 self.prediction_bar.update(self.prediction_bar.value + 1	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            if self.prediction_bar is not None:
                 self.prediction_bar.close()
            __snake_case            =	None


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




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

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




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            self.training_tracker.update(
                state.global_step     ,	comment=F'''Epoch {int(state.epoch	)}/{state.num_train_epochs}'''     ,	force_update=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	None






