


from typing import TYPE_CHECKING

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


_UpperCamelCase          =     {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase          =     [
          """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """OpenAIGPTDoubleHeadsModel""",
          """OpenAIGPTForSequenceClassification""",
          """OpenAIGPTLMHeadModel""",
          """OpenAIGPTModel""",
          """OpenAIGPTPreTrainedModel""",
          """load_tf_weights_in_openai_gpt""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase          =     [
          """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFOpenAIGPTDoubleHeadsModel""",
          """TFOpenAIGPTForSequenceClassification""",
          """TFOpenAIGPTLMHeadModel""",
          """TFOpenAIGPTMainLayer""",
          """TFOpenAIGPTModel""",
          """TFOpenAIGPTPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
      from .tokenization_openai import OpenAIGPTTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_openai_fast import OpenAIGPTTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_openai import (
                OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                OpenAIGPTDoubleHeadsModel,
                OpenAIGPTForSequenceClassification,
                OpenAIGPTLMHeadModel,
                OpenAIGPTModel,
                OpenAIGPTPreTrainedModel,
                load_tf_weights_in_openai_gpt,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_openai import (
                TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFOpenAIGPTDoubleHeadsModel,
                TFOpenAIGPTForSequenceClassification,
                TFOpenAIGPTLMHeadModel,
                TFOpenAIGPTMainLayer,
                TFOpenAIGPTModel,
                TFOpenAIGPTPreTrainedModel,
            )

else:
      import sys

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





'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Any						     =       os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE       )       )
     __A  :					dict[str, None]						     =       {}
     with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
          for word in dictionary_file.read().split("\n"       ):
               __A  :					int						     =       None
     return english_words


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       len([word for word in possible_words if word in ENGLISH_WORDS]       )
     return float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = 20					,				SCREAMING_SNAKE_CASE = 85       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()

