



'''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  (__A):



     """simple docstring"""



     _a      =  int(__A)
     _a  ,    _a  ,    _a      =  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  ,	__A  ,	__A  ,	__A  ,	__A=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  (__A):



     """simple docstring"""



     _a      =  '''<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      =  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'''







 __lowerCamelCase    :       Tuple          =       5
 __lowerCamelCase    :       Union[str, Any]          =       0.2



 def __init__(self ,   A ,   A = None ,   A = True ,   A = None ,   A = 300 ,   )    ->     Dict:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A = False ,   A = None       )    ->     Dict:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A=None       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  ''' ''' * (len(str(self.total       )       ) - len(str(A       )       )) + str(A       )
      if self.elapsed_time is None:
           _a      =  f'''[{spaced_value}/{self.total} : < :'''
      elif self.predicted_remaining is None:
           _a      =  f'''[{spaced_value}/{self.total} {format_time(self.elapsed_time       )}'''
      else:
           _a      =  (
               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       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  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      =  disp.display(disp.HTML(self.html_code       ) ,   display_id=A       )
      else:
           self.output.update(disp.HTML(self.html_code       )       )



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


      """simple docstring"""



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






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A=None       )    ->     Optional[int]:


      """simple docstring"""



      super().__init__(A       )
      _a      =  None if column_names is None else [column_names]
      _a      =  None



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  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      =  disp.display(disp.HTML(self.html_code       ) ,   display_id=A       )
      else:
           self.output.update(disp.HTML(self.html_code       )       )



 def 					a__  (self ,   A       )    ->     List[Any]:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A=None ,   A=300       )    ->     Tuple:


      """simple docstring"""



      _a      =  NotebookProgressBar(A ,   prefix=A ,   parent=self ,   width=A       )
      return self.child_bar



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  None
      self.display()






class __A			(  A						):



 '''simple docstring'''







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


      """simple docstring"""



      _a      =  None
      _a      =  None
      _a      =  False



 def 					a__  (self ,   A ,   A ,   A ,   **A       )    ->     Any:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A ,   A ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  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      =  False



 def 					a__  (self ,   A ,   A ,   A ,   A=None ,   **A       )    ->     str:


      """simple docstring"""



      if not has_length(A       ):
           return
      if self.prediction_bar is None:
           if self.training_tracker is not None:
                _a      =  self.training_tracker.add_child(len(A       )       )
           else:
                _a      =  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       )    ->     int:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A ,   A ,   A=None ,   **A       )    ->     str:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A ,   A ,   A=None ,   **A       )    ->     List[str]:


      """simple docstring"""



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

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



 def 					a__  (self ,   A ,   A ,   A ,   **A       )    ->     Optional[Any]:


      """simple docstring"""



      self.training_tracker.update(
          state.global_step ,   comment=f'''Epoch {int(state.epoch       )}/{state.num_train_epochs}''' ,   force_update=A       )
      _a      =  None








'''simple docstring'''





import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf






class __A			(  enum.Enum						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       0
 __lowerCamelCase    :       Any          =       1
 __lowerCamelCase    :       List[Any]          =       2






@add_end_docstrings(A						)
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       '\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '



 def __init__(self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      super().__init__(*A ,   **A       )
      self.check_model_type(
          TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING       )
      if "prefix" not in self._preprocess_params:
           # This is very specific. The logic is quite complex and needs to be done
           # as a "default".
           # It also defines both some preprocess_kwargs and generate_kwargs
           # which is why we cannot put them in their respective methods.
           _a      =  None
           if self.model.config.prefix is not None:
                _a      =  self.model.config.prefix
           if prefix is None and self.model.__class__.__name__ in [
               "XLNetLMHeadModel",
               "TransfoXLLMHeadModel",
               "TFXLNetLMHeadModel",
               "TFTransfoXLLMHeadModel",
           ]:
                # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                _a      =  self.XL_PREFIX
           if prefix is not None:
                # Recalculate some generate_kwargs linked to prefix.
                _a  ,    _a  ,    _a      =  self._sanitize_parameters(prefix=A ,   **self._forward_params       )
                _a      =  {**self._preprocess_params, **preprocess_params}
                _a      =  {**self._forward_params, **forward_params}



 def 					a__  (self ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   **A ,   )    ->     str:


      """simple docstring"""



      _a      =  {}
      if prefix is not None:
           _a      =  prefix
      if prefix:
           _a      =  self.tokenizer(
               A ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
           _a      =  prefix_inputs['''input_ids'''].shape[-1]

      if handle_long_generation is not None:
           if handle_long_generation not in {"hole"}:
                raise ValueError(
                    f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
                    ''' [None, \'hole\']'''       )
           _a      =  handle_long_generation

      preprocess_params.update(A       )
      _a      =  generate_kwargs

      _a      =  {}
      if return_full_text is not None and return_type is None:
           if return_text is not None:
                raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''       )
           if return_tensors is not None:
                raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''       )
           _a      =  ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
      if return_tensors is not None and return_type is None:
           if return_text is not None:
                raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''       )
           _a      =  ReturnType.TENSORS
      if return_type is not None:
           _a      =  return_type
      if clean_up_tokenization_spaces is not None:
           _a      =  clean_up_tokenization_spaces

      if stop_sequence is not None:
           _a      =  self.tokenizer.encode(A ,   add_special_tokens=A       )
           if len(A       ) > 1:
                warnings.warn(
                    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
                    ''' the stop sequence will be used as the stop sequence string in the interim.'''       )
           _a      =  stop_sequence_ids[0]

      return preprocess_params, forward_params, postprocess_params



 def 					a__  (self ,   *A ,   **A       )    ->     List[Any]:


      """simple docstring"""



      if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
           kwargs.update({'''add_space_before_punct_symbol''': True}       )

      return super()._parse_and_tokenize(*A ,   **A       )



 def __call__(self ,   A ,   **A       )    ->     int:


      """simple docstring"""



      return super().__call__(A ,   **A       )



 def 					a__  (self ,   A ,   A="" ,   A=None ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  self.tokenizer(
          prefix + prompt_text ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
      _a      =  prompt_text

      if handle_long_generation == "hole":
           _a      =  inputs['''input_ids'''].shape[-1]
           if "max_new_tokens" in generate_kwargs:
                _a      =  generate_kwargs['''max_new_tokens''']
           else:
                _a      =  generate_kwargs.get('''max_length''' ,   self.model.config.max_length       ) - cur_len
                if new_tokens < 0:
                     raise ValueError('''We cannot infer how many new tokens are expected'''       )
           if cur_len + new_tokens > self.tokenizer.model_max_length:
                _a      =  self.tokenizer.model_max_length - new_tokens
                if keep_length <= 0:
                     raise ValueError(
                         '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
                         ''' models max length'''       )

                _a      =  inputs['''input_ids'''][:, -keep_length:]
                if "attention_mask" in inputs:
                     _a      =  inputs['''attention_mask'''][:, -keep_length:]

      return inputs



 def 					a__  (self ,   A ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  model_inputs['''input_ids''']
      _a      =  model_inputs.get('''attention_mask''' ,   A       )
      # Allow empty prompts
      if input_ids.shape[1] == 0:
           _a      =  None
           _a      =  None
           _a      =  1
      else:
           _a      =  input_ids.shape[0]
      _a      =  model_inputs.pop('''prompt_text'''       )

      # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
      # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
      _a      =  generate_kwargs.pop('''prefix_length''' ,   0       )
      if prefix_length > 0:
           _a      =  '''max_new_tokens''' in generate_kwargs or (
               '''generation_config''' in generate_kwargs
               and generate_kwargs['''generation_config'''].max_new_tokens is not None
           )
           if not has_max_new_tokens:
                _a      =  generate_kwargs.get('''max_length'''       ) or self.model.config.max_length
                generate_kwargs["max_length"] += prefix_length
           _a      =  '''min_new_tokens''' in generate_kwargs or (
               '''generation_config''' in generate_kwargs
               and generate_kwargs['''generation_config'''].min_new_tokens is not None
           )
           if not has_min_new_tokens and "min_length" in generate_kwargs:
                generate_kwargs["min_length"] += prefix_length

        # BS x SL
      _a      =  self.model.generate(input_ids=A ,   attention_mask=A ,   **A       )
      _a      =  generated_sequence.shape[0]
      if self.framework == "pt":
           _a      =  generated_sequence.reshape(A ,   out_b // in_b ,   *generated_sequence.shape[1:]       )
      elif self.framework == "tf":
           _a      =  tf.reshape(A ,   (in_b, out_b // in_b, *generated_sequence.shape[1:])       )
      return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}



 def 					a__  (self ,   A ,   A=ReturnType.FULL_TEXT ,   A=True       )    ->     str:


      """simple docstring"""



      _a      =  model_outputs['''generated_sequence'''][0]
      _a      =  model_outputs['''input_ids''']
      _a      =  model_outputs['''prompt_text''']
      _a      =  generated_sequence.numpy().tolist()
      _a      =  []
      for sequence in generated_sequence:
           if return_type == ReturnType.TENSORS:
                _a      =  {'''generated_token_ids''': sequence}
           elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                # Decode text
                _a      =  self.tokenizer.decode(
                    A ,   skip_special_tokens=A ,   clean_up_tokenization_spaces=A ,   )

                # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                if input_ids is None:
                     _a      =  0
                else:
                     _a      =  len(
                         self.tokenizer.decode(
                             input_ids[0] ,   skip_special_tokens=A ,   clean_up_tokenization_spaces=A ,   )       )

                if return_type == ReturnType.FULL_TEXT:
                     _a      =  prompt_text + text[prompt_length:]
                else:
                     _a      =  text[prompt_length:]

                _a      =  {'''generated_text''': all_text}
           records.append(A       )

      return records



