



from typing import TYPE_CHECKING

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


snake_case__					:     Union[str, Any]    		=   {
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Tuple    		=   ["""AlbertTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Any    		=   ["""AlbertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     List[Any]    		=   [
	    """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """AlbertForMaskedLM""",
	    """AlbertForMultipleChoice""",
	    """AlbertForPreTraining""",
	    """AlbertForQuestionAnswering""",
	    """AlbertForSequenceClassification""",
	    """AlbertForTokenClassification""",
	    """AlbertModel""",
	    """AlbertPreTrainedModel""",
	    """load_tf_weights_in_albert""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     List[Any]    		=   [
	    """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFAlbertForMaskedLM""",
	    """TFAlbertForMultipleChoice""",
	    """TFAlbertForPreTraining""",
	    """TFAlbertForQuestionAnswering""",
	    """TFAlbertForSequenceClassification""",
	    """TFAlbertForTokenClassification""",
	    """TFAlbertMainLayer""",
	    """TFAlbertModel""",
	    """TFAlbertPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Tuple    		=   [
	    """FlaxAlbertForMaskedLM""",
	    """FlaxAlbertForMultipleChoice""",
	    """FlaxAlbertForPreTraining""",
	    """FlaxAlbertForQuestionAnswering""",
	    """FlaxAlbertForSequenceClassification""",
	    """FlaxAlbertForTokenClassification""",
	    """FlaxAlbertModel""",
	    """FlaxAlbertPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

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








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   _A   :


						'''simple docstring'''





						def __init__(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			np.array(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			np.where(letter == self.SQUARE	)
							__lowercase		       =			np.concatenate([indexa + 1, indexa + 1]	)
							return indexes



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.SQUARE[indexa - 1, indexa - 1]
							return letter



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			message.lower()
							__lowercase		       =			message.replace(" "							,      ""	)
							__lowercase		       =			message.replace("j"							,      "i"	)

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

								__lowercase		       =			numbers[0]
								__lowercase		       =			numbers[1]

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

							return encoded_message



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



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

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

							return decoded_message



