



import itertools
import string
from collections.abc import Generator, Iterable


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Generator[tuple[str, ...], None, None]:
			A__       =    iter(__UpperCamelCase					)
			while True:
						A__       =    tuple(itertools.islice(__UpperCamelCase ,	__UpperCamelCase					)					)
						if not chunk:
									return
						yield chunk


def A						(       __UpperCamelCase					)						->      str:
			A__       =    ''.join([c.upper() for c in dirty if c in string.ascii_letters]					)
			A__       =    ''

			if len(__UpperCamelCase					) < 2:
						return dirty

			for i in range(len(__UpperCamelCase					) - 1					):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(__UpperCamelCase					) & 1:
						clean += "X"

			return clean


def A						(       __UpperCamelCase					)						->      list[str]:
			# I and J are used interchangeably to allow
			# us to use a 5x5 table (25 letters)
			A__       =    'ABCDEFGHIKLMNOPQRSTUVWXYZ'
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			A__       =    []

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(__UpperCamelCase					)

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(__UpperCamelCase					)

			return table


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      str:
			A__       =    generate_table(__UpperCamelCase					)
			A__       =    prepare_input(__UpperCamelCase					)
			A__       =    ''

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__UpperCamelCase ,	2					):
						A__     , A__       =    divmod(table.index(__UpperCamelCase					) ,	5					)
						A__     , A__       =    divmod(table.index(__UpperCamelCase					) ,	5					)

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      str:
			A__       =    generate_table(__UpperCamelCase					)
			A__       =    ''

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__UpperCamelCase ,	2					):
						A__     , A__       =    divmod(table.index(__UpperCamelCase					) ,	5					)
						A__     , A__       =    divmod(table.index(__UpperCamelCase					) ,	5					)

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								if truncation is not None:
											if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'					)
											A__       =    truncation

								A__       =    tokenize_kwargs

								A__       =    {}
								if return_tensors is not None:
											A__       =    return_tensors

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""simple docstring"""


								if return_tensors:
											return model_outputs[0]
								if self.framework == "pt":
											return model_outputs[0].tolist()
								elif self.framework == "tf":
											return model_outputs[0].numpy().tolist()



					def __call__(     self					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return super().__call__(*_snake_case				,					**_snake_case					)




