



"""simple docstring"""


import os
from string import ascii_letters

snake_case_								=			ascii_letters + """ \t\n"""

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


snake_case_								=			load_dictionary()

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					message.upper()
						UpperCAmelCase			 =					remove_non_letters(lowercase_		)
						UpperCAmelCase			 =					message.split()
						UpperCAmelCase			 =					len([word for word in possible_words if word in ENGLISH_WORDS]		)
						return float(lowercase_		) / len(lowercase_		)

def 					_lowerCAmelCase (  lowercase_		):
						return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE		)

def 					_lowerCAmelCase (  lowercase_						,      lowercase_ = 20						,      lowercase_ = 85		):
						UpperCAmelCase			 =					get_english_count(lowercase_		) * 100 >= word_percentage
						UpperCAmelCase			 =					len(remove_non_letters(lowercase_		)		)
						UpperCAmelCase			 =					(float(lowercase_		) / len(lowercase_		)) * 100
						UpperCAmelCase			 =					message_letters_percentage >= letter_percentage
						return words_match and letters_match


if __name__ == "__main__":
				import doctest

				doctest.testmod()












"""simple docstring"""
from typing import TYPE_CHECKING

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


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'UniSpeechSatForAudioFrameClassification',
		    'UniSpeechSatForCTC',
		    'UniSpeechSatForPreTraining',
		    'UniSpeechSatForSequenceClassification',
		    'UniSpeechSatForXVector',
		    'UniSpeechSatModel',
		    'UniSpeechSatPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_snake_case	:						Union[str, Any]					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


