import jax.numpy as jnp

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


lowercase_		:				Union[str, Any]				=						logging.get_logger(__name__)

lowercase_		:				str				=						'''T5Config'''

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[str]  						=   jnp.zeros_like(__UpperCamelCase )
				_snake_case :			Any  						=   shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
				_snake_case :			str  						=   shifted_input_ids.at[:, 0].set(__UpperCamelCase )

				_snake_case :			Optional[int]  						=   jnp.where(shifted_input_ids == -1_00	,			__UpperCamelCase	,			__UpperCamelCase )
				return shifted_input_ids


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Dict								=	"mt5"
						_UpperCamelCase  :	List[str]								=	MTaConfig



class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[Any]								=	"mt5"
						_UpperCamelCase  :	Optional[int]								=	MTaConfig



class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[Any]								=	"mt5"
						_UpperCamelCase  :	Tuple								=	MTaConfig













from typing import TYPE_CHECKING

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


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CanineForMultipleChoice''',
		    '''CanineForQuestionAnswering''',
		    '''CanineForSequenceClassification''',
		    '''CanineForTokenClassification''',
		    '''CanineLayer''',
		    '''CanineModel''',
		    '''CaninePreTrainedModel''',
		    '''load_tf_weights_in_canine''',
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






