



from typing import TYPE_CHECKING

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


snake_case__					:     str    		=   {
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Optional[Any]    		=   [
	    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """Data2VecAudioForAudioFrameClassification""",
	    """Data2VecAudioForCTC""",
	    """Data2VecAudioForSequenceClassification""",
	    """Data2VecAudioForXVector""",
	    """Data2VecAudioModel""",
	    """Data2VecAudioPreTrainedModel""",
	]
	snake_case__					:     Dict    		=   [
	    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """Data2VecTextForCausalLM""",
	    """Data2VecTextForMaskedLM""",
	    """Data2VecTextForMultipleChoice""",
	    """Data2VecTextForQuestionAnswering""",
	    """Data2VecTextForSequenceClassification""",
	    """Data2VecTextForTokenClassification""",
	    """Data2VecTextModel""",
	    """Data2VecTextPreTrainedModel""",
	]
	snake_case__					:     Dict    		=   [
	    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """Data2VecVisionForImageClassification""",
	    """Data2VecVisionForMaskedImageModeling""",
	    """Data2VecVisionForSemanticSegmentation""",
	    """Data2VecVisionModel""",
	    """Data2VecVisionPreTrainedModel""",
	]

if is_tf_available():
	snake_case__					:     List[str]    		=   [
	    """TFData2VecVisionForImageClassification""",
	    """TFData2VecVisionForSemanticSegmentation""",
	    """TFData2VecVisionModel""",
	    """TFData2VecVisionPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	snake_case__					:     List[str]    		=   _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



