






'''simple docstring'''



from typing import Mapping

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


__UpperCamelCase      :       str      			=      logging.get_logger(__name__)

__UpperCamelCase      :       List[str]      			=      {
    """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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="umt5"
      __a  					=["past_key_values"]




      def __init__(  self ,   lowerCamelCase=25_0112 ,   lowerCamelCase=512 ,   lowerCamelCase=64 ,   lowerCamelCase=1024 ,   lowerCamelCase=8 ,   lowerCamelCase=None ,   lowerCamelCase=6 ,   lowerCamelCase=32 ,   lowerCamelCase=128 ,   lowerCamelCase=0.1 ,   lowerCamelCase=1e-6 ,   lowerCamelCase=1.0 ,   lowerCamelCase="gated-gelu" ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase="T5Tokenizer" ,   lowerCamelCase=True ,   lowerCamelCase=0 ,   lowerCamelCase=1 ,   lowerCamelCase=0 ,   **lowerCamelCase ,   )							->List[Any]:



         '''simple docstring'''
         super().__init__(
             is_encoder_decoder=lowerCamelCase ,   tokenizer_class=lowerCamelCase ,   tie_word_embeddings=lowerCamelCase ,   pad_token_id=lowerCamelCase ,   eos_token_id=lowerCamelCase ,   decoder_start_token_id=lowerCamelCase ,   **lowerCamelCase ,   )
         __a				     =    vocab_size
         __a				     =    d_model
         __a				     =    d_kv
         __a				     =    d_ff
         __a				     =    num_layers
         __a				     =    (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         __a				     =    num_heads
         __a				     =    relative_attention_num_buckets
         __a				     =    relative_attention_max_distance
         __a				     =    dropout_rate
         __a				     =    layer_norm_epsilon
         __a				     =    initializer_factor
         __a				     =    feed_forward_proj
         __a				     =    use_cache

         __a				     =    self.feed_forward_proj.split('-'      )
         __a				     =    act_info[-1]
         __a				     =    act_info[0] == 'gated'

         if len(lowerCamelCase      ) > 1 and act_info[0] != "gated" or len(lowerCamelCase      ) > 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":
            __a				     =    'gelu_new'




      @property
      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         return self.d_model




      @property
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         return self.num_heads






      @property
      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         return self.num_layers







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def  __UpperCamelCase							(  self      )							->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a				     =    {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
            __a				     =    'past_encoder_sequence + sequence'
            __a				     =    {0: 'batch'}
            __a				     =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
            __a				     =    {0: 'batch', 1: 'decoder_sequence'}
            __a				     =    {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase ,   direction='inputs'      )

         return common_inputs




      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         return 13






      @property
      def  __UpperCamelCase							(  self      )							->float:



         '''simple docstring'''
         return 5e-4







'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Dict      			=      ["""DebertaTokenizerFast"""]

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      [
     """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

 __UpperCamelCase      :       List[Any]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)