"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   UpperCAmelCase__				(    _SCREAMING_SNAKE_CASE			):


	"""simple docstring"""
	lowerCAmelCase__    :					Tuple						      =							['image_processor', 'tokenizer']
	lowerCAmelCase__    :					Optional[Any]						      =							'VideoMAEImageProcessor'
	lowerCAmelCase__    :					int						      =							('CLIPTokenizer', 'CLIPTokenizerFast')







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     **_SCREAMING_SNAKE_CASE )    ->	Tuple:
							a_							:			Dict     		=					None
							if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."       ,     UpperCamelCase__       ,     )
													a_							:			Optional[Any]     		=					kwargs.pop("feature_extractor" )

							a_							:			Optional[Any]     		=					image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
													raise ValueError("You need to specify an `image_processor`." )
							if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`." )

							super().__init__(UpperCamelCase__       ,     UpperCamelCase__ )
							a_							:			Any     		=					self.image_processor







	def __call__(			self       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     **_SCREAMING_SNAKE_CASE )    ->	int:

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

							if text is not None:
													a_							:			Dict     		=					self.tokenizer(UpperCamelCase__       ,     return_tensors=UpperCamelCase__       ,     **UpperCamelCase__ )

							if videos is not None:
													a_							:			Tuple     		=					self.image_processor(UpperCamelCase__       ,     return_tensors=UpperCamelCase__       ,     **UpperCamelCase__ )

							if text is not None and videos is not None:
													a_							:			Dict     		=					image_features.pixel_values
													return encoding
							elif text is not None:
													return encoding
							else:
													return BatchEncoding(data=dict(**UpperCamelCase__ )       ,     tensor_type=UpperCamelCase__ )







	def       A    (			self       ,     *_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )    ->	str:
							return self.tokenizer.batch_decode(*UpperCamelCase__       ,     **UpperCamelCase__ )







	def       A    (			self       ,     *_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )    ->	List[str]:
							return self.tokenizer.decode(*UpperCamelCase__       ,     **UpperCamelCase__ )







	@property
	def       A    (			self )    ->	Union[str, Any]:
							return ["input_ids", "attention_mask", "position_ids", "pixel_values"]







	@property
	def       A    (			self )    ->	Optional[int]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,     UpperCamelCase__       ,     )
							return self.image_processor_class







	@property
	def       A    (			self )    ->	List[str]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,     UpperCamelCase__       ,     )
							return self.image_processor





"""simple docstring"""






import numpy as np

UpperCamelCase     					=     [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]
class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self )    ->	None:
							a_							:			List[Any]     		=					np.array(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	np.ndarray:
							a_		,					a_							:			List[str]     		=					np.where(letter == self.SQUARE )
							a_							:			Union[str, Any]     		=					np.concatenate([indexa + 1, indexa + 1] )
							return indexes







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							return self.SQUARE[indexa - 1, indexa - 1]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			List[str]     		=					message.lower()
							a_							:			Union[str, Any]     		=					message.replace("j"       ,     "i" )

							a_							:			Tuple     		=					""
							for letter_index in range(len(_SCREAMING_SNAKE_CASE ) ):
													if message[letter_index] != " ":
																			a_							:			Dict     		=					self.letter_to_numbers(message[letter_index] )
																			a_							:			str     		=					encoded_message + str(numbers[0] ) + str(numbers[1] )
													elif message[letter_index] == " ":
																			a_							:			Any     		=					encoded_message + " "

							return encoded_message







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			Dict     		=					message.replace(" "       ,     "  " )
							a_							:			str     		=					""
							for numbers_index in range(int(len(_SCREAMING_SNAKE_CASE ) / 2 ) ):
													if message[numbers_index * 2] != " ":
																			a_							:			str     		=					message[numbers_index * 2]
																			a_							:			str     		=					message[numbers_index * 2 + 1]

																			a_							:			Optional[Any]     		=					self.numbers_to_letter(int(_SCREAMING_SNAKE_CASE )       ,     int(_SCREAMING_SNAKE_CASE ) )
																			a_							:			Optional[Any]     		=					decoded_message + letter
													elif message[numbers_index * 2] == " ":
																			a_							:			str     		=					decoded_message + " "

							return decoded_message




