
'''simple docstring'''







import numpy as np

_snake_case				:   Optional[int]     =				[
    ["""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 			lowerCAmelCase :




		def __init__(				self      ):
								_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= np.where(letter == self.SQUARE      )
								_SCREAMING_SNAKE_CASE				= np.concatenate([indexa + 1, indexa + 1]      )
								return indexes




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.SQUARE[indexa - 1, indexa - 1]
								return letter




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= message.lower()
								_SCREAMING_SNAKE_CASE				= message.replace(" "					,						""      )
								_SCREAMING_SNAKE_CASE				= message.replace("j"					,						"i"      )

								_SCREAMING_SNAKE_CASE				= np.empty((2, len(UpperCamelCase      ))      )
								for letter_index in range(len(UpperCamelCase      )      ):
														_SCREAMING_SNAKE_CASE				= self.letter_to_numbers(message[letter_index]      )

														_SCREAMING_SNAKE_CASE				= numbers[0]
														_SCREAMING_SNAKE_CASE				= numbers[1]

								_SCREAMING_SNAKE_CASE				= first_step.reshape(2 * len(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= ""
								for numbers_index in range(len(UpperCamelCase      )      ):
														_SCREAMING_SNAKE_CASE				= int(second_step[numbers_index * 2]      )
														_SCREAMING_SNAKE_CASE				= int(second_step[(numbers_index * 2) + 1]      )
														_SCREAMING_SNAKE_CASE				= self.numbers_to_letter(UpperCamelCase					,						UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= encoded_message + letter

								return encoded_message




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= message.lower()
								message.replace(" "					,						""      )
								_SCREAMING_SNAKE_CASE				= np.empty(2 * len(UpperCamelCase      )      )
								for letter_index in range(len(UpperCamelCase      )      ):
														_SCREAMING_SNAKE_CASE				= self.letter_to_numbers(message[letter_index]      )
														_SCREAMING_SNAKE_CASE				= numbers[0]
														_SCREAMING_SNAKE_CASE				= numbers[1]

								_SCREAMING_SNAKE_CASE				= first_step.reshape((2, len(UpperCamelCase      ))      )
								_SCREAMING_SNAKE_CASE				= ""
								for numbers_index in range(len(UpperCamelCase      )      ):
														_SCREAMING_SNAKE_CASE				= int(second_step[0, numbers_index]      )
														_SCREAMING_SNAKE_CASE				= int(second_step[1, numbers_index]      )
														_SCREAMING_SNAKE_CASE				= self.numbers_to_letter(UpperCamelCase					,						UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= decoded_message + letter

								return decoded_message

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """TFRagModel""",
						    """TFRagPreTrainedModel""",
						    """TFRagSequenceForGeneration""",
						    """TFRagTokenForGeneration""",
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						_snake_case				:   str     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)