






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












import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




