





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

_snake_case	:						int					=					ascii_letters + ' \t\n'






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


_snake_case	:						Optional[Any]					=					load_dictionary()






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






def    A__							(     UpperCamelCase			):
							return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE			)






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


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Tuple   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				List[str]   							=       """AutoImageProcessor"""
			__magic_name__ :				List[str]   							=       """AutoTokenizer"""



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       Any				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       str				=    kwargs.pop("feature_extractor"					)

									snake_case :       Optional[int]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You have to specify an image_processor."					)
									if tokenizer is None:
															raise ValueError("You have to specify a tokenizer."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       Optional[int]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and images is None:
															raise ValueError("You have to specify either text or images. Both cannot be none."					)

									if text is not None:
															snake_case :       Optional[int]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if images is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and images is not None:
															snake_case :       str				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.tokenizer.model_input_names
									snake_case :       Any				=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





