






'''simple docstring'''
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				list[list[str]]									=[[] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				Tuple									=key - 1

      if key <= 0:
            raise ValueError('''Height of grid can\'t be 0 or negative'''							)
      if key == 1 or len(UpperCamelCase__							) <= key:
            return input_string

      for position, character in enumerate(UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				List[str]									=position % (lowest * 2)  # puts it in bounds
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=min(UpperCamelCase__,	lowest * 2 - num							)  # creates zigzag pattern
            temp_grid[num].append(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				List[str]									=[''''''.join(UpperCamelCase__							) for row in temp_grid]
      SCREAMING_SNAKE_CASE__     :				Tuple									=''''''.join(UpperCamelCase__							)

      return output_string





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=[]
      SCREAMING_SNAKE_CASE__     :				Any									=key - 1

      if key <= 0:
            raise ValueError('''Height of grid can\'t be 0 or negative'''							)
      if key == 1:
            return input_string

      SCREAMING_SNAKE_CASE__     :				list[list[str]]									=[[] for _ in range(UpperCamelCase__							)]  # generates template
      for position in range(len(UpperCamelCase__							)							):
            SCREAMING_SNAKE_CASE__     :				str									=position % (lowest * 2)  # puts it in bounds
            SCREAMING_SNAKE_CASE__     :				int									=min(UpperCamelCase__,	lowest * 2 - num							)  # creates zigzag pattern
            temp_grid[num].append('''*'''							)

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=0
      for row in temp_grid:  # fills in the characters
            SCREAMING_SNAKE_CASE__     :				List[str]									=input_string[counter : counter + len(UpperCamelCase__							)]
            grid.append(list(UpperCamelCase__							)							)
            counter += len(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=''''''  # reads as zigzag
      for position in range(len(UpperCamelCase__							)							):
            SCREAMING_SNAKE_CASE__     :				Tuple									=position % (lowest * 2)  # puts it in bounds
            SCREAMING_SNAKE_CASE__     :				Any									=min(UpperCamelCase__,	lowest * 2 - num							)  # creates zigzag pattern
            output_string += grid[num][0]
            grid[num].pop(0							)
      return output_string





def  _a(  UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									={}
      for key_guess in range(1,	len(UpperCamelCase__							)							):  # tries every key
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=decrypt(UpperCamelCase__,	UpperCamelCase__							)
      return results


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

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