



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__a           =							logging.get_logger(__name__)

__a           =							{
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Union[str, Any]		=	"""plbart"""
					_A						:     Tuple		=	["""past_key_values"""]
					_A						:     Any		=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






					def __init__(			self:				str		,	snake_case:				Optional[Any]=50_005		,	snake_case:				int=1_024		,	snake_case:				Optional[int]=6		,	snake_case:				List[Any]=3_072		,	snake_case:				List[str]=12		,	snake_case:				List[Any]=6		,	snake_case:				str=3_072		,	snake_case:				int=12		,	snake_case:				Optional[Any]=0.0		,	snake_case:				Any=0.0		,	snake_case:				Optional[int]=True		,	snake_case:				Optional[Any]=True		,	snake_case:				Dict="gelu"		,	snake_case:				Tuple=768		,	snake_case:				List[Any]=0.1		,	snake_case:				Optional[int]=0.1		,	snake_case:				str=0.0		,	snake_case:				List[Any]=0.0_2		,	snake_case:				Tuple=0.0		,	snake_case:				Union[str, Any]=True		,	snake_case:				Optional[int]=1		,	snake_case:				str=0		,	snake_case:				Dict=2		,	snake_case:				Dict=2		,	**snake_case:				Union[str, Any]		,	)  ->      Union[str, Any]:
											snake_case_					:List[str]		=     vocab_size
											snake_case_					:Optional[int]		=     max_position_embeddings
											snake_case_					:Any		=     d_model
											snake_case_					:Tuple		=     encoder_ffn_dim
											snake_case_					:List[Any]		=     encoder_layers
											snake_case_					:Optional[int]		=     encoder_attention_heads
											snake_case_					:Optional[Any]		=     decoder_ffn_dim
											snake_case_					:Any		=     decoder_layers
											snake_case_					:List[Any]		=     decoder_attention_heads
											snake_case_					:Tuple		=     dropout
											snake_case_					:Dict		=     attention_dropout
											snake_case_					:int		=     activation_dropout
											snake_case_					:List[Any]		=     activation_function
											snake_case_					:Any		=     init_std
											snake_case_					:Optional[Any]		=     encoder_layerdrop
											snake_case_					:Optional[Any]		=     decoder_layerdrop
											snake_case_					:int		=     classifier_dropout
											snake_case_					:Dict		=     use_cache
											snake_case_					:List[str]		=     encoder_layers
											snake_case_					:int		=     scale_embedding  # scale factor will be sqrt(d_model) if True
											super().__init__(
											    pad_token_id=snake_case		,	bos_token_id=snake_case		,	eos_token_id=snake_case		,	is_encoder_decoder=snake_case		,	forced_eos_token_id=snake_case		,	**snake_case		,	)

class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					@property
					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
											    ]		)






					@property
					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Mapping[str, Mapping[int, str]]:
											if self.use_past:
																	return OrderedDict(
																	    [
																	        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
																	        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	    ]		)
											else:
																	return OrderedDict(
																	    [
																	        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	    ]		)






"""simple docstring"""
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,
)


_SCREAMING_SNAKE_CASE						 =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5Tokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """T5EncoderModel""",
        """T5ForConditionalGeneration""",
        """T5Model""",
        """T5PreTrainedModel""",
        """load_tf_weights_in_t5""",
        """T5ForQuestionAnswering""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFT5EncoderModel""",
        """TFT5ForConditionalGeneration""",
        """TFT5Model""",
        """TFT5PreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """FlaxT5EncoderModel""",
        """FlaxT5ForConditionalGeneration""",
        """FlaxT5Model""",
        """FlaxT5PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta import TaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta_fast import TaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ta import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TaEncoderModel,
            TaForConditionalGeneration,
            TaForQuestionAnswering,
            TaModel,
            TaPreTrainedModel,
            load_tf_weights_in_ta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ta import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTaEncoderModel,
            TFTaForConditionalGeneration,
            TFTaModel,
            TFTaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_ta import (
            FlaxTaEncoderModel,
            FlaxTaForConditionalGeneration,
            FlaxTaModel,
            FlaxTaPreTrainedModel,
        )


else:
    import sys

    _SCREAMING_SNAKE_CASE						 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



