
import os
from string import ascii_letters

lowerCamelCase__        =						ascii_letters + """ \t\n"""
def 					lowerCAmelCase__     (    )							->						dict[str, None]:
			lowerCAmelCase__ : Tuple   =						os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE_	)	)
			lowerCAmelCase__ : dict[str, None]   =						{}
			with open(path[0] + '/dictionary.txt'	) as dictionary_file:
						for word in dictionary_file.read().split('\n'	):
									lowerCAmelCase__ : List[str]   =						None
			return english_words


lowerCamelCase__        =						load_dictionary()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						float:
			lowerCAmelCase__ : int   =						message.upper()
			lowerCAmelCase__ : Optional[Any]   =						remove_non_letters(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Any   =						message.split()
			lowerCAmelCase__ : Any   =						len([word for word in possible_words if word in ENGLISH_WORDS]	)
			return float(SCREAMING_SNAKE_CASE_	) / len(SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						str:
			return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_ = 20			,				SCREAMING_SNAKE_CASE_ = 85	)							->						bool:
			lowerCAmelCase__ : List[Any]   =						get_english_count(SCREAMING_SNAKE_CASE_	) * 100 >= word_percentage
			lowerCAmelCase__ : Optional[int]   =						len(remove_non_letters(SCREAMING_SNAKE_CASE_	)	)
			lowerCAmelCase__ : Tuple   =						(float(SCREAMING_SNAKE_CASE_	) / len(SCREAMING_SNAKE_CASE_	)) * 100
			lowerCAmelCase__ : Optional[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_tf_available,
    is_torch_available,
)


lowerCamelCase__        =						{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RegNetForImageClassification""",
					    """RegNetModel""",
					    """RegNetPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRegNetForImageClassification""",
					    """TFRegNetModel""",
					    """TFRegNetPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """FlaxRegNetForImageClassification""",
					    """FlaxRegNetModel""",
					    """FlaxRegNetPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_regnet import (
										    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RegNetForImageClassification,
										    RegNetModel,
										    RegNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_regnet import (
										    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRegNetForImageClassification,
										    TFRegNetModel,
										    TFRegNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_regnet import (
										    FlaxRegNetForImageClassification,
										    FlaxRegNetModel,
										    FlaxRegNetPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase__        =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)