



import numpy as np

__snake_case    :		Tuple  =[
    ['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__      :




		'''simple docstring'''



		def __init__(self			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					np.array(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					np.where(letter == self.SQUARE			)
						lowerCAmelCase__					:    str												=					np.concatenate([indexa + 1, indexa + 1]			)
						return indexes



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.SQUARE[indexa - 1, indexa - 1]
						return letter



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					message.lower()
						lowerCAmelCase__					:    Optional[int]												=					message.replace(''' '''		,''''''			)
						lowerCAmelCase__					:    List[Any]												=					message.replace('''j'''		,'''i'''			)

						lowerCAmelCase__					:    Union[str, Any]												=					np.empty((2, len(__lowerCamelCase			))			)
						for letter_index in range(len(__lowerCamelCase			)			):
										lowerCAmelCase__					:    List[str]												=					self.letter_to_numbers(message[letter_index]			)

										lowerCAmelCase__					:    List[Any]												=					numbers[0]
										lowerCAmelCase__					:    Union[str, Any]												=					numbers[1]

						lowerCAmelCase__					:    Dict												=					first_step.reshape(2 * len(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					''''''
						for numbers_index in range(len(__lowerCamelCase			)			):
										lowerCAmelCase__					:    str												=					int(second_step[numbers_index * 2]			)
										lowerCAmelCase__					:    Optional[int]												=					int(second_step[(numbers_index * 2) + 1]			)
										lowerCAmelCase__					:    Dict												=					self.numbers_to_letter(__lowerCamelCase		,__lowerCamelCase			)
										lowerCAmelCase__					:    Dict												=					encoded_message + letter

						return encoded_message



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					message.lower()
						message.replace(''' '''		,''''''			)
						lowerCAmelCase__					:    Optional[int]												=					np.empty(2 * len(__lowerCamelCase			)			)
						for letter_index in range(len(__lowerCamelCase			)			):
										lowerCAmelCase__					:    Any												=					self.letter_to_numbers(message[letter_index]			)
										lowerCAmelCase__					:    List[str]												=					numbers[0]
										lowerCAmelCase__					:    Tuple												=					numbers[1]

						lowerCAmelCase__					:    Union[str, Any]												=					first_step.reshape((2, len(__lowerCamelCase			))			)
						lowerCAmelCase__					:    int												=					''''''
						for numbers_index in range(len(__lowerCamelCase			)			):
										lowerCAmelCase__					:    Any												=					int(second_step[0, numbers_index]			)
										lowerCAmelCase__					:    int												=					int(second_step[1, numbers_index]			)
										lowerCAmelCase__					:    List[Any]												=					self.numbers_to_letter(__lowerCamelCase		,__lowerCamelCase			)
										lowerCAmelCase__					:    Any												=					decoded_message + letter

						return decoded_message






from typing import TYPE_CHECKING

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


__snake_case    :		Any  ={
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Union[str, Any]  =['BlenderbotSmallTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotSmallForCausalLM',
							    'BlenderbotSmallForConditionalGeneration',
							    'BlenderbotSmallModel',
							    'BlenderbotSmallPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		str  =[
							    'TFBlenderbotSmallForConditionalGeneration',
							    'TFBlenderbotSmallModel',
							    'TFBlenderbotSmallPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Dict  =[
							    'FlaxBlenderbotSmallForConditionalGeneration',
							    'FlaxBlenderbotSmallModel',
							    'FlaxBlenderbotSmallPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_blenderbot_small import (
							    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotSmallConfig,
							    BlenderbotSmallOnnxConfig,
							)
							from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot_small import (
														    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotSmallForCausalLM,
														    BlenderbotSmallForConditionalGeneration,
														    BlenderbotSmallModel,
														    BlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot_small import (
														    TFBlenderbotSmallForConditionalGeneration,
														    TFBlenderbotSmallModel,
														    TFBlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot_small import (
														    FlaxBlenderbotSmallForConditionalGeneration,
														    FlaxBlenderbotSmallModel,
														    FlaxBlenderbotSmallPreTrainedModel,
														)

else:
							import sys

							__snake_case    :		Optional[int]  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

