





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_a     :    int         =						logging.get_logger(__name__)

_a     :    int         =						'T5Config'
def 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__   )				->			jnp.ndarray:




				"""simple docstring"""






				snake_case   :					int    						=				jnp.zeros_like(__a   )
				snake_case   :					str    						=				shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]   )
				snake_case   :					List[Any]    						=				shifted_input_ids.at[:, 0].set(__a   )

				snake_case   :					Optional[int]    						=				jnp.where(shifted_input_ids == -100		,     __a		,     __a   )
				return shifted_input_ids
class   a_ (			A__      ):
			A__      :			int       	=  'mt5'
			A__      :			Tuple       	=  MTaConfig
class   a_ (			A__      ):
			A__      :			Union[str, Any]       	=  'mt5'
			A__      :			Any       	=  MTaConfig
class   a_ (			A__      ):
			A__      :			List[Any]       	=  'mt5'
			A__      :			Union[str, Any]       	=  MTaConfig




'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case				 =	{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ConvBertForMaskedLM''',
		    '''ConvBertForMultipleChoice''',
		    '''ConvBertForQuestionAnswering''',
		    '''ConvBertForSequenceClassification''',
		    '''ConvBertForTokenClassification''',
		    '''ConvBertLayer''',
		    '''ConvBertModel''',
		    '''ConvBertPreTrainedModel''',
		    '''load_tf_weights_in_convbert''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFConvBertForMaskedLM''',
		    '''TFConvBertForMultipleChoice''',
		    '''TFConvBertForQuestionAnswering''',
		    '''TFConvBertForSequenceClassification''',
		    '''TFConvBertForTokenClassification''',
		    '''TFConvBertLayer''',
		    '''TFConvBertModel''',
		    '''TFConvBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
		from .tokenization_convbert import ConvBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_convbert_fast import ConvBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convbert import (
				    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvBertForMaskedLM,
				    ConvBertForMultipleChoice,
				    ConvBertForQuestionAnswering,
				    ConvBertForSequenceClassification,
				    ConvBertForTokenClassification,
				    ConvBertLayer,
				    ConvBertModel,
				    ConvBertPreTrainedModel,
				    load_tf_weights_in_convbert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convbert import (
				    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertLayer,
				    TFConvBertModel,
				    TFConvBertPreTrainedModel,
				)


else:
		import sys

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