
import os
from string import ascii_letters

snake_case__  							=							ascii_letters + """ \t\n"""

def        lowerCamelCase_      (			):
						lowercase       :    List[str]											=  os.path.split(os.path.realpath(UpperCAmelCase_       )       )
						lowercase       :    Optional[Any]											=  {}
						with open(path[0] + '''/dictionary.txt'''       ) as dictionary_file:
												for word in dictionary_file.read().split('''\n'''       ):
																		lowercase       :    Union[str, Any]											=  None
						return english_words


snake_case__  							=							load_dictionary()

def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict       ):
						lowercase       :    Tuple											=  message.upper()
						lowercase       :    Tuple											=  remove_non_letters(UpperCAmelCase_       )
						lowercase       :    Dict											=  message.split()
						lowercase       :    Any											=  len([word for word in possible_words if word in ENGLISH_WORDS]       )
						return float(UpperCAmelCase_       ) / len(UpperCAmelCase_       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict       ):
						return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				Any = 20							,			UpperCAmelCase_							:				Union[str, Any] = 85       ):
						lowercase       :    Union[str, Any]											=  get_english_count(UpperCAmelCase_       ) * 1_00 >= word_percentage
						lowercase       :    Any											=  len(remove_non_letters(UpperCAmelCase_       )       )
						lowercase       :    Dict											=  (float(UpperCAmelCase_       ) / len(UpperCAmelCase_       )) * 1_00
						lowercase       :    Any											=  message_letters_percentage >= letter_percentage
						return words_match and letters_match


if __name__ == "__main__":
						import doctest

						doctest.testmod()




from typing import TYPE_CHECKING

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


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

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


