
"""simple docstring"""
from typing import Mapping

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


_a      :			Tuple					=							logging.get_logger(__name__)

_a      :			Optional[int]					=							{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class      _UpperCAmelCase				(					__UpperCAmelCase):
    __lowercase							: List[str]  =					"""umt5"""
    __lowercase							: Union[str, Any]  =					["""past_key_values"""]
    def __init__(    self						,    snake_case_=25_01_12						,    snake_case_=5_12						,    snake_case_=64						,    snake_case_=10_24						,    snake_case_=8						,    snake_case_=None						,    snake_case_=6						,    snake_case_=32						,    snake_case_=1_28						,    snake_case_=0.1						,    snake_case_=1E-6						,    snake_case_=1.0						,    snake_case_="gated-gelu"						,    snake_case_=True						,    snake_case_=True						,    snake_case_="T5Tokenizer"						,    snake_case_=True						,    snake_case_=0						,    snake_case_=1						,    snake_case_=0						,    **snake_case_						,    ):
      super().__init__(
          is_encoder_decoder=snake_case_						,    tokenizer_class=snake_case_						,    tie_word_embeddings=snake_case_						,    pad_token_id=snake_case_						,    eos_token_id=snake_case_						,    decoder_start_token_id=snake_case_						,    **snake_case_						,    )
      _snake_case     :						str						=   vocab_size
      _snake_case     :						int						=   d_model
      _snake_case     :						str						=   d_kv
      _snake_case     :						Optional[Any]						=   d_ff
      _snake_case     :						List[str]						=   num_layers
      _snake_case     :						Optional[Any]						=   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      _snake_case     :						Optional[Any]						=   num_heads
      _snake_case     :						List[Any]						=   relative_attention_num_buckets
      _snake_case     :						str						=   relative_attention_max_distance
      _snake_case     :						Tuple						=   dropout_rate
      _snake_case     :						List[str]						=   layer_norm_epsilon
      _snake_case     :						Dict						=   initializer_factor
      _snake_case     :						str						=   feed_forward_proj
      _snake_case     :						Dict						=   use_cache

      _snake_case     :						int						=   self.feed_forward_proj.split("-"     )
      _snake_case     :						Any						=   act_info[-1]
      _snake_case     :						Any						=   act_info[0] == "gated"

      if len(snake_case_     ) > 1 and act_info[0] != "gated" or len(snake_case_     ) > 2:
        raise ValueError(
            F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "\'gated-gelu\' or \'relu\'"     )

      if feed_forward_proj == "gated-gelu":
        _snake_case     :						Dict						=   "gelu_new"
    @property
    def 					lowerCamelCase__	(    self     ):
      return self.d_model
    @property
    def 					lowerCamelCase__	(    self     ):
      return self.num_heads


    @property
    def 					lowerCamelCase__	(    self     ):
      return self.num_layers






class      _UpperCAmelCase				(					__UpperCAmelCase):
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   {
          "input_ids": {0: "batch", 1: "encoder_sequence"},
          "attention_mask": {0: "batch", 1: "encoder_sequence"},
      }
      if self.use_past:
        _snake_case     :						int						=   "past_encoder_sequence + sequence"
        _snake_case     :						List[str]						=   {0: "batch"}
        _snake_case     :						Tuple						=   {0: "batch", 1: "past_decoder_sequence + sequence"}
      else:
        _snake_case     :						List[Any]						=   {0: "batch", 1: "decoder_sequence"}
        _snake_case     :						Optional[Any]						=   {0: "batch", 1: "decoder_sequence"}

      if self.use_past:
        self.fill_with_past_key_values_(snake_case_						,    direction="inputs"     )

      return common_inputs
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def 					lowerCamelCase__	(    self     ):
      return 13


    @property
    def 					lowerCamelCase__	(    self     ):
      return 5E-4



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a      :			List[Any]					=							{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							["""BartphoTokenizer"""]

if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bartpho import BartphoTokenizer

else:
    import sys

    _a      :			Optional[int]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

