




'''simple docstring'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase      :   Tuple      =       {
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   str      =       ["""VivitImageProcessor"""]


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   Optional[int]      =       [
          """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VivitModel""",
          """VivitPreTrainedModel""",
          """VivitForVideoClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_vivit import VivitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vivit import (
                VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VivitForVideoClassification,
                VivitModel,
                VivitPreTrainedModel,
            )


else:
      import sys

      lowercase      :   Tuple      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''simple docstring'''







import numpy as np

lowercase      :   str      =       [
    ["""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 					_a :





     '''simple docstring'''



     def __init__(				self							)      ->				None:
          snake_case			:      Dict        =			np.array(__a							)







     def 			snake_case_      (				self							,__a							)      ->				np.ndarray:
          snake_case ,		snake_case			:      Optional[Any]        =			np.where(letter == self.SQUARE							)
          snake_case			:      List[Any]        =			np.concatenate([indexa + 1, indexa + 1]							)
          return indexes







     def 			snake_case_      (				self							,__a							,__a							)      ->				str:
          snake_case			:      List[Any]        =			self.SQUARE[indexa - 1, indexa - 1]
          return letter







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      List[Any]        =			message.lower()
          snake_case			:      Dict        =			message.replace(""" """							,""""""							)
          snake_case			:      List[str]        =			message.replace("""j"""							,"""i"""							)

          snake_case			:      Optional[int]        =			np.empty((2, len(__a							))							)
          for letter_index in range(len(__a							)							):
               snake_case			:      Tuple        =			self.letter_to_numbers(message[letter_index]							)

               snake_case			:      Optional[Any]        =			numbers[0]
               snake_case			:      List[str]        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape(2 * len(__a							)							)
          snake_case			:      List[Any]        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      List[str]        =			int(second_step[numbers_index * 2]							)
               snake_case			:      List[Any]        =			int(second_step[(numbers_index * 2) + 1]							)
               snake_case			:      str        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      Any        =			encoded_message + letter

          return encoded_message







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Any        =			message.lower()
          message.replace(""" """							,""""""							)
          snake_case			:      Dict        =			np.empty(2 * len(__a							)							)
          for letter_index in range(len(__a							)							):
               snake_case			:      str        =			self.letter_to_numbers(message[letter_index]							)
               snake_case			:      Any        =			numbers[0]
               snake_case			:      int        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape((2, len(__a							))							)
          snake_case			:      Dict        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      Optional[Any]        =			int(second_step[0, numbers_index]							)
               snake_case			:      Optional[int]        =			int(second_step[1, numbers_index]							)
               snake_case			:      Tuple        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      int        =			decoded_message + letter

          return decoded_message



