



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

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


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

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








from typing import Mapping

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


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''t5'''
      __snake_case       =  ['''past_key_values''']
      __snake_case       =  {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Optional[Any]=32_128 ,					__UpperCAmelCase      :						List[Any]=512 ,					__UpperCAmelCase      :						Dict=64 ,					__UpperCAmelCase      :						Tuple=2_048 ,					__UpperCAmelCase      :						int=6 ,					__UpperCAmelCase      :						Optional[int]=None ,					__UpperCAmelCase      :						Optional[int]=8 ,					__UpperCAmelCase      :						str=32 ,					__UpperCAmelCase      :						Tuple=128 ,					__UpperCAmelCase      :						Optional[Any]=0.1 ,					__UpperCAmelCase      :						int=1e-6 ,					__UpperCAmelCase      :						int=1.0 ,					__UpperCAmelCase      :						List[str]="relu" ,					__UpperCAmelCase      :						int=True ,					__UpperCAmelCase      :						int=True ,					__UpperCAmelCase      :						List[Any]=0 ,					__UpperCAmelCase      :						int=1 ,					**__UpperCAmelCase      :						str ,					)    ->Optional[Any]:




       """simple docstring"""


       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(__UpperCAmelCase  ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase  ) > 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\''''  )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
        a		  =      '''gelu_new'''

       super().__init__(
           pad_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					is_encoder_decoder=__UpperCAmelCase ,					**__UpperCAmelCase ,					)






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->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_(__UpperCAmelCase ,					direction='''inputs'''  )

       return common_inputs







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




       """simple docstring"""


       return 13


