






from typing import Any






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validation(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   # Creates data structures and fill initial step
   UpperCamelCase_									=     {}
   UpperCamelCase_									=     {}
   for state in states_space:
      UpperCamelCase_									=     observations_space[0]
      UpperCamelCase_									=     (
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      UpperCamelCase_									=     None

   # Fills the data structure with the probabilities of
   # different transitions and pointers to previous states
   for o in range(1    ,      len(__lowercase)):
      UpperCamelCase_									=     observations_space[o]
      UpperCamelCase_									=     observations_space[o - 1]
      for state in states_space:
         # Calculates the argmax for probability function
         UpperCamelCase_									=     ''
         UpperCamelCase_									=     -1
         for k_state in states_space:
            UpperCamelCase_									=     (
                probabilities[(k_state, prior_observation)]
                * transition_probabilities[k_state][state]
                * emission_probabilities[state][observation]
            )
            if probability > max_probability:
               UpperCamelCase_									=     probability
               UpperCamelCase_									=     k_state

            # Update probabilities and pointers dicts
         UpperCamelCase_									=     (
             probabilities[(arg_max, prior_observation)]
             * transition_probabilities[arg_max][state]
             * emission_probabilities[state][observation]
         )

         UpperCamelCase_									=     arg_max

    # The final observation
   UpperCamelCase_									=     observations_space[len(__lowercase) - 1]

   # argmax for given final observation
   UpperCamelCase_									=     ''
   UpperCamelCase_									=     -1
   for k_state in states_space:
      UpperCamelCase_									=     probabilities[(k_state, final_observation)]
      if probability > max_probability:
         UpperCamelCase_									=     probability
         UpperCamelCase_									=     k_state
   UpperCamelCase_									=     arg_max

   # Process pointers backwards
   UpperCamelCase_									=     last_state
   UpperCamelCase_									=     []
   for o in range(len(__lowercase) - 1    ,      -1    ,      -1):
      result.append(__lowercase)
      UpperCamelCase_									=     pointers[previous, observations_space[o]]
   result.reverse()

   return result






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_not_empty(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   _validate_lists(__lowercase    ,      __lowercase)
   _validate_dicts(
       __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]):
      raise ValueError('There\'s an empty parameter')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_list(__lowercase    ,      'observations_space')
   _validate_list(__lowercase    ,      'states_space')






def     _snake_case    (__lowercase    ,      __lowercase):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a list"""
      raise ValueError(__lowercase)
   else:
      for x in _object:
         if not isinstance(__lowercase    ,      __lowercase):
            UpperCamelCase_									=     f"""{var_name} must be a list of strings"""
            raise ValueError(__lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_dict(__lowercase    ,      'initial_probabilities'    ,      __lowercase)
   _validate_nested_dict(__lowercase    ,      'transition_probabilities')
   _validate_nested_dict(__lowercase    ,      'emission_probabilities')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_dict(_object    ,      __lowercase    ,      __lowercase)
   for x in _object.values():
      _validate_dict(__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase = False):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a dict"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object):
      UpperCamelCase_									=     f"""{var_name} all keys must be strings"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object.values()):
      UpperCamelCase_									=     'nested dictionary ' if nested else ''
      UpperCamelCase_									=     f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
      raise ValueError(__lowercase)


if __name__ == "__main__":
 from doctest import testmod

 testmod()













import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__      :	Any       	=      logging.get_logger(__name__)


@dataclass
class 						_a       :






  """simple docstring"""





  A_						=   field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()    )}    )
  A_						=   field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}    )
  A_						=   field(
      default=128						,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.task_name.lower()




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """train"""
  A_						=   """dev"""
  A_						=   """test"""




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42
  A_						=   42
  A_						=   42




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = Split.train		,		_UpperCAmelCase = None		,		)			-> Tuple:
     warnings.warn(
         'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
         'library. You can have a look at this example script for pointers: '
         'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'		,		_UpperCAmelCase		,		)
     UpperCamelCase_									=     args
     UpperCamelCase_									=     glue_processors[args.task_name]()
     UpperCamelCase_									=     glue_output_modes[args.task_name]
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        try:
           UpperCamelCase_									=     Split[mode]
        except KeyError:
           raise KeyError('mode is not a valid split name' )
        # Load data features from cache or dataset file
     UpperCamelCase_									=     os.path.join(
         cache_dir if cache_dir is not None else args.data_dir		,		f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""		,		)
     UpperCamelCase_									=     self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
     UpperCamelCase_									=     label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     UpperCamelCase_									=     cached_features_file + '.lock'
     with FileLock(_UpperCAmelCase ):
        if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
           UpperCamelCase_									=     time.time()
           UpperCamelCase_									=     torch.load(_UpperCAmelCase )
           logger.info(
               f"""Loading features from cached file {cached_features_file} [took %.3f s]"""		,		time.time() - start )
        else:
           logger.info(f"""Creating features from dataset file at {args.data_dir}""" )

           if mode == Split.dev:
              UpperCamelCase_									=     self.processor.get_dev_examples(args.data_dir )
           elif mode == Split.test:
              UpperCamelCase_									=     self.processor.get_test_examples(args.data_dir )
           else:
              UpperCamelCase_									=     self.processor.get_train_examples(args.data_dir )
           if limit_length is not None:
              UpperCamelCase_									=     examples[:limit_length]
           UpperCamelCase_									=     glue_convert_examples_to_features(
               _UpperCAmelCase		,		_UpperCAmelCase		,		max_length=args.max_seq_length		,		label_list=_UpperCAmelCase		,		output_mode=self.output_mode		,		)
           UpperCamelCase_									=     time.time()
           torch.save(self.features		,		_UpperCAmelCase )
           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
           logger.info(
               f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )




  def __len__(     self )			-> List[str]:
     return len(self.features )




  def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
     return self.features[i]




  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.label_list





