



import numpy as np

a			:     Any     = [
    ['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							:

			def __init__(self				)					->      None:
						UpperCAmelCase_:		List[str]       			=    np.array(SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      np.ndarray:
						UpperCAmelCase_			,       UpperCAmelCase_:		Dict       			=    np.where(letter == self.SQUARE				)
						UpperCAmelCase_:		Optional[int]       			=    np.concatenate([indexa + 1, indexa + 1]				)
						return indexes

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      str:
						return self.SQUARE[indexa - 1, indexa - 1]

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      str:
						UpperCAmelCase_:		Dict       			=    message.lower()
						UpperCAmelCase_:		Optional[int]       			=    message.replace("""j""",      """i"""				)

						UpperCAmelCase_:		str       			=    """"""
						for letter_index in range(len(SCREAMING_SNAKE_CASE_				)				):
									if message[letter_index] != " ":
												UpperCAmelCase_:		Dict       			=    self.letter_to_numbers(message[letter_index]				)
												UpperCAmelCase_:		Union[str, Any]       			=    encoded_message + str(numbers[0]				) + str(numbers[1]				)
									elif message[letter_index] == " ":
												UpperCAmelCase_:		Any       			=    encoded_message + """ """

						return encoded_message

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      str:
						UpperCAmelCase_:		Optional[int]       			=    message.replace(""" """,      """  """				)
						UpperCAmelCase_:		str       			=    """"""
						for numbers_index in range(int(len(SCREAMING_SNAKE_CASE_				) / 2				)				):
									if message[numbers_index * 2] != " ":
												UpperCAmelCase_:		Any       			=    message[numbers_index * 2]
												UpperCAmelCase_:		List[str]       			=    message[numbers_index * 2 + 1]

												UpperCAmelCase_:		Optional[int]       			=    self.numbers_to_letter(int(SCREAMING_SNAKE_CASE_				),      int(SCREAMING_SNAKE_CASE_				)				)
												UpperCAmelCase_:		Union[str, Any]       			=    decoded_message + letter
									elif message[numbers_index * 2] == " ":
												UpperCAmelCase_:		List[Any]       			=    decoded_message + """ """

						return decoded_message











from typing import TYPE_CHECKING

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


a			:     Union[str, Any]     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Optional[int]     = [
	    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaubertForMultipleChoice',
	    'FlaubertForQuestionAnswering',
	    'FlaubertForQuestionAnsweringSimple',
	    'FlaubertForSequenceClassification',
	    'FlaubertForTokenClassification',
	    'FlaubertModel',
	    'FlaubertWithLMHeadModel',
	    'FlaubertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Dict     = [
	    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFFlaubertForMultipleChoice',
	    'TFFlaubertForQuestionAnsweringSimple',
	    'TFFlaubertForSequenceClassification',
	    'TFFlaubertForTokenClassification',
	    'TFFlaubertModel',
	    'TFFlaubertPreTrainedModel',
	    'TFFlaubertWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	a			:     Tuple     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






