



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase						    =  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase						    =  ["""XLNetTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase						    =  ["""XLNetTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase						    =  [
          """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """XLNetForMultipleChoice""",
          """XLNetForQuestionAnswering""",
          """XLNetForQuestionAnsweringSimple""",
          """XLNetForSequenceClassification""",
          """XLNetForTokenClassification""",
          """XLNetLMHeadModel""",
          """XLNetModel""",
          """XLNetPreTrainedModel""",
          """load_tf_weights_in_xlnet""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase						    =  [
          """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFXLNetForMultipleChoice""",
          """TFXLNetForQuestionAnsweringSimple""",
          """TFXLNetForSequenceClassification""",
          """TFXLNetForTokenClassification""",
          """TFXLNetLMHeadModel""",
          """TFXLNetMainLayer""",
          """TFXLNetModel""",
          """TFXLNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlnet import XLNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlnet_fast import XLNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlnet import (
                XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLNetForMultipleChoice,
                XLNetForQuestionAnswering,
                XLNetForQuestionAnsweringSimple,
                XLNetForSequenceClassification,
                XLNetForTokenClassification,
                XLNetLMHeadModel,
                XLNetModel,
                XLNetPreTrainedModel,
                load_tf_weights_in_xlnet,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xlnet import (
                TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXLNetForMultipleChoice,
                TFXLNetForQuestionAnsweringSimple,
                TFXLNetForSequenceClassification,
                TFXLNetForTokenClassification,
                TFXLNetLMHeadModel,
                TFXLNetMainLayer,
                TFXLNetModel,
                TFXLNetPreTrainedModel,
            )

else:
      import sys

      _lowerCamelCase						    =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	analyze_text(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(" " + ascii_lowercase     )
 # what is our total sum of probabilities.
 __SCREAMING_SNAKE_CASE       :    int       =	sum(single_char_strings.values()     )

 # one length string
 __SCREAMING_SNAKE_CASE       :    str       =	0
 # for each alpha we go in our dict and if it is in it we calculate entropy
 for ch in my_alphas:
  if ch in single_char_strings:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	single_char_strings[ch]
   __SCREAMING_SNAKE_CASE       :    str       =	my_str / all_sum
   my_fir_sum += prob * math.loga(__lowerCamelCase     )  # entropy formula.

    # print entropy
 print(F"""{round(-1 * my_fir_sum     ):.1f}"""     )

 # two len string
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum(two_char_strings.values()     )
 __SCREAMING_SNAKE_CASE       :    int       =	0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   __SCREAMING_SNAKE_CASE       :    int       =	cha + cha
   if sequence in two_char_strings:
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	two_char_strings[sequence]
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(__lowerCamelCase     ) / all_sum
    my_sec_sum += prob * math.loga(__lowerCamelCase     )

    # print second entropy
 print(F"""{round(-1 * my_sec_sum     ):.1f}"""     )

 # print the difference between them
 print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	Counter()  # type: ignore
 __SCREAMING_SNAKE_CASE       :    int       =	Counter()  # type: ignore
 single_char_strings[text[-1]] += 1

 # first case when we have space at start.
 two_char_strings[" " + text[0]] += 1
 for i in range(0		,	len(__lowerCamelCase     ) - 1     ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 import doctest

 doctest.testmod()
 # text = (
 #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
 #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
 #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
 #     "beloved evident through no service elderly is. Blind there if every no so "
 #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
 #     "of message cottage windows do besides against uncivil.  Delightful "
 #     "unreserved impossible few estimating men favourable see entreaties. She "
 #     "propriety immediate was improving. He or entrance humoured likewise "
 #     "moderate. Much nor game son say feel. Fat make met can must form into "
 #     "gate. Me we offending prevailed discovery. "
 # )

 # calculate_prob(text)


if __name__ == "__main__":
      main()






