

from typing import TYPE_CHECKING

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


A									=  {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['DebertaTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DebertaForMaskedLM',
		    'DebertaForQuestionAnswering',
		    'DebertaForSequenceClassification',
		    'DebertaForTokenClassification',
		    'DebertaModel',
		    'DebertaPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDebertaForMaskedLM',
		    'TFDebertaForQuestionAnswering',
		    'TFDebertaForSequenceClassification',
		    'TFDebertaForTokenClassification',
		    'TFDebertaModel',
		    'TFDebertaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
		from .tokenization_deberta import DebertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_fast import DebertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta import (
				    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaForMaskedLM,
				    DebertaForQuestionAnswering,
				    DebertaForSequenceClassification,
				    DebertaForTokenClassification,
				    DebertaModel,
				    DebertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta import (
				    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaForMaskedLM,
				    TFDebertaForQuestionAnswering,
				    TFDebertaForSequenceClassification,
				    TFDebertaForTokenClassification,
				    TFDebertaModel,
				    TFDebertaPreTrainedModel,
				)


else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


A									=  logging.get_logger(__name__)

A									=  'T5Config'


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig
