from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						None
  lowerCamelCase__     =						None
  lowerCamelCase__     =						None
  lowerCamelCase__     =						None



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]=1  ,     __magic_name__ :       Tuple=0  ,     __magic_name__ :       Optional[int]=2  ,     __magic_name__ :       Tuple=512  ,     __magic_name__ :       Any="cls"  ,     __magic_name__ :       List[str]=False  ,     __magic_name__ :       Optional[int]=True  ,     **__magic_name__ :       Union[str, Any]  ,     )  ->	Dict:
        super().__init__(pad_token_id=__magic_name__  ,     bos_token_id=__magic_name__  ,     eos_token_id=__magic_name__  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  project_dim
        SCREAMING_SNAKE_CASE_          =  pooler_fn
        SCREAMING_SNAKE_CASE_          =  learn_encoder
        SCREAMING_SNAKE_CASE_          =  use_attention_mask



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						[r'''pooler''', r'''logit_scale''']
  lowerCamelCase__     =						[r'''position_ids''', r'''predictions.decoder.bias''']
  lowerCamelCase__     =						'''roberta'''
  lowerCamelCase__     =						RobertaSeriesConfig







  def __init__(							self :       Dict  ,     __magic_name__ :       Union[str, Any]   )  ->	Union[str, Any]:
        super().__init__(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  nn.Linear(config.hidden_size  ,     config.project_dim   )
        SCREAMING_SNAKE_CASE_          =  getattr(__magic_name__  ,     "has_pre_transformation"  ,     __magic_name__   )
        if self.has_pre_transformation:
              SCREAMING_SNAKE_CASE_          =  nn.Linear(config.hidden_size  ,     config.project_dim   )
              SCREAMING_SNAKE_CASE_          =  nn.LayerNorm(config.hidden_size  ,     eps=config.layer_norm_eps   )
        self.post_init()







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[torch.Tensor] = None  ,     __magic_name__ :       Optional[bool] = None  ,     __magic_name__ :       Optional[bool] = None  ,     __magic_name__ :       Optional[bool] = None  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  return_dict if return_dict is not None else self.config.use_return_dict

        SCREAMING_SNAKE_CASE_          =  self.base_model(
            input_ids=__magic_name__  ,     attention_mask=__magic_name__  ,     token_type_ids=__magic_name__  ,     position_ids=__magic_name__  ,     head_mask=__magic_name__  ,     inputs_embeds=__magic_name__  ,     encoder_hidden_states=__magic_name__  ,     encoder_attention_mask=__magic_name__  ,     output_attentions=__magic_name__  ,     output_hidden_states=True if self.has_pre_transformation else output_hidden_states  ,     return_dict=__magic_name__  ,     )

        if self.has_pre_transformation:
              SCREAMING_SNAKE_CASE_          =  outputs["hidden_states"][-2]
              SCREAMING_SNAKE_CASE_          =  self.pre_LN(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  self.transformation_pre(__magic_name__   )

              return TransformationModelOutput(
                  projection_state=__magic_name__  ,     last_hidden_state=outputs.last_hidden_state  ,     hidden_states=outputs.hidden_states  ,     attentions=outputs.attentions  ,     )
        else:
              SCREAMING_SNAKE_CASE_          =  self.transformation(outputs.last_hidden_state   )
              return TransformationModelOutput(
                  projection_state=__magic_name__  ,     last_hidden_state=outputs.last_hidden_state  ,     hidden_states=outputs.hidden_states  ,     attentions=outputs.attentions  ,     )



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

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


