


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







def        SCREAMING_SNAKE_CASE_						(			__A :			Iterable[str]  ,    __A :			int				)	->		Generator[tuple[str, ...], None, None]:

       """simple docstring"""

       a_					:		List[str]							=  iter(__A				)
       while True:
              a_					:		Optional[int]							=  tuple(itertools.islice(__A  ,    __A				)				)
              if not chunk:
                     return
              yield chunk







def        SCREAMING_SNAKE_CASE_						(			__A :			str				)	->		str:

       """simple docstring"""

       a_					:		Dict							=  ''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
       a_					:		Union[str, Any]							=  ''

       if len(__A				) < 2:
              return dirty

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

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

       clean += dirty[-1]

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

       return clean







def        SCREAMING_SNAKE_CASE_						(			__A :			str				)	->		list[str]:

       """simple docstring"""

       a_					:		Optional[Any]							=  '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_					:		Optional[Any]							=  []

       # 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(__A				)

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

       return table







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			str				)	->		str:

       """simple docstring"""

       a_					:		Optional[int]							=  generate_table(__A				)
       a_					:		Tuple							=  prepare_input(__A				)
       a_					:		List[Any]							=  ''

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(__A  ,    2				):
              a_							,		a_					:		List[Any]							=  divmod(table.index(__A				)  ,    5				)
              a_							,		a_					:		Dict							=  divmod(table.index(__A				)  ,    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        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			str				)	->		str:

       """simple docstring"""

       a_					:		Union[str, Any]							=  generate_table(__A				)
       a_					:		Union[str, Any]							=  ''

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(__A  ,    2				):
              a_							,		a_					:		Optional[Any]							=  divmod(table.index(__A				)  ,    5				)
              a_							,		a_					:		Dict							=  divmod(table.index(__A				)  ,    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









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

       a_ =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




