





from typing import TYPE_CHECKING

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


UpperCAmelCase_       : Optional[Any]							=    {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    ["""GPT2TokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]

try:
      if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Union[str, Any]							=    ["""TFGPT2Tokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Any							=    ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_fast import GPTaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpta import (
                GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTaDoubleHeadsModel,
                GPTaForQuestionAnswering,
                GPTaForSequenceClassification,
                GPTaForTokenClassification,
                GPTaLMHeadModel,
                GPTaModel,
                GPTaPreTrainedModel,
                load_tf_weights_in_gpta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_gpta import (
                TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGPTaDoubleHeadsModel,
                TFGPTaForSequenceClassification,
                TFGPTaLMHeadModel,
                TFGPTaMainLayer,
                TFGPTaModel,
                TFGPTaPreTrainedModel,
            )

      try:
            if not is_keras_nlp_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_tf import TFGPTaTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
      import sys

      UpperCAmelCase_       : Optional[Any]							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""
import os
from string import ascii_letters

SCREAMING_SNAKE_CASE__       =	ascii_letters + " \t\n"





def  lowerCAmelCase__     (      )       ->					dict[str, None]:





						"""simple docstring"""






						snake_case             =      os.path.split(os.path.realpath(_UpperCamelCase       )       )
						snake_case             =      {}
						with open(path[0] + '/dictionary.txt'       ) as dictionary_file:
												for word in dictionary_file.read().split('\n'       ):
																		snake_case             =      None
						return english_words


SCREAMING_SNAKE_CASE__       =	load_dictionary()





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					float:





						"""simple docstring"""






						snake_case             =      message.upper()
						snake_case             =      remove_non_letters(_UpperCamelCase       )
						snake_case             =      message.split()
						snake_case             =      len([word for word in possible_words if word in ENGLISH_WORDS]       )
						return float(_UpperCamelCase       ) / len(_UpperCamelCase       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					str:





						"""simple docstring"""






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





def  lowerCAmelCase__     (      _UpperCamelCase							:					str			,      _UpperCamelCase							:					int = 2_0			,      _UpperCamelCase							:					int = 8_5       )       ->					bool:





						"""simple docstring"""






						snake_case             =      get_english_count(_UpperCamelCase       ) * 1_0_0 >= word_percentage
						snake_case             =      len(remove_non_letters(_UpperCamelCase       )       )
						snake_case             =      (float(_UpperCamelCase       ) / len(_UpperCamelCase       )) * 1_0_0
						snake_case             =      message_letters_percentage >= letter_percentage
						return words_match and letters_match


if __name__ == "__main__":
					import doctest

					doctest.testmod()


