


'''simple docstring'''

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





def __snake_case					(      UpperCAmelCase_						:						Iterable[str]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  iter(UpperCAmelCase_		)
 while True:
  lowerCamelCase_      =  tuple(itertools.islice(UpperCAmelCase_					,				UpperCAmelCase_		)		)
  if not chunk:
   return
  yield chunk





def __snake_case					(      UpperCAmelCase_						:						str		):
 lowerCamelCase_      =  "".join([c.upper() for c in dirty if c in string.ascii_letters]		)
 lowerCamelCase_      =  ""

 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 __snake_case					(      UpperCAmelCase_						:						str		):
 # I and J are used interchangeably to allow
 # us to use a 5x5 table (25 letters)
 lowerCamelCase_      =  "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
 lowerCamelCase_      =  []

 # 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 __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						str		):
 lowerCamelCase_      =  generate_table(UpperCAmelCase_		)
 lowerCamelCase_      =  prepare_input(UpperCAmelCase_		)
 lowerCamelCase_      =  ""

 # https://en.wikipedia.org/wiki/Playfair_cipher#Description
 for chara, chara in chunker(UpperCAmelCase_					,				2		):
  lowerCamelCase_ ,lowerCamelCase_      =  divmod(table.index(UpperCAmelCase_		)					,				5		)
  lowerCamelCase_ ,lowerCamelCase_      =  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 __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						str		):
 lowerCamelCase_      =  generate_table(UpperCAmelCase_		)
 lowerCamelCase_      =  ""

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

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


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




