






"""simple docstring"""



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        a     (   a_	):
    UpperCAmelCase_ :     Optional[torch.FloatTensor]	     =None
    UpperCAmelCase_ :     torch.FloatTensor	     =None
    UpperCAmelCase_ :     Optional[Tuple[torch.FloatTensor]]	     =None
    UpperCAmelCase_ :     Optional[Tuple[torch.FloatTensor]]	     =None


class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase=1							,		_lowerCamelCase=0							,		_lowerCamelCase=2							,		_lowerCamelCase=5_1_2							,		_lowerCamelCase="cls"							,		_lowerCamelCase=False							,		_lowerCamelCase=True							,		**_lowerCamelCase							,		):
      super().__init__(pad_token_id=_lowerCamelCase							,		bos_token_id=_lowerCamelCase							,		eos_token_id=_lowerCamelCase							,		**_lowerCamelCase       )
      lowercase							    =       project_dim
      lowercase							    =       pooler_fn
      lowercase							    =       learn_encoder
      lowercase							    =       use_attention_mask


class        a     (   a_	):
    UpperCAmelCase_ :     Optional[int]	     =[R"pooler", R"logit_scale"]
    UpperCAmelCase_ :     int	     =[R"position_ids", R"predictions.decoder.bias"]
    UpperCAmelCase_ :     List[str]	     ="roberta"
    UpperCAmelCase_ :     Dict	     =RobertaSeriesConfig







    def __init__(			self							,		_lowerCamelCase       ):
      super().__init__(_lowerCamelCase       )
      lowercase							    =       XLMRobertaModel(_lowerCamelCase       )
      lowercase							    =       nn.Linear(config.hidden_size							,		config.project_dim       )
      lowercase							    =       getattr(_lowerCamelCase							,		'has_pre_transformation'							,		_lowerCamelCase       )
      if self.has_pre_transformation:
        lowercase							    =       nn.Linear(config.hidden_size							,		config.project_dim       )
        lowercase							    =       nn.LayerNorm(config.hidden_size							,		eps=config.layer_norm_eps       )
      self.post_init()







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		):
      lowercase							    =       return_dict if return_dict is not None else self.config.use_return_dict

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

      if self.has_pre_transformation:
        lowercase							    =       outputs['hidden_states'][-2]
        lowercase							    =       self.pre_LN(_lowerCamelCase       )
        lowercase							    =       self.transformation_pre(_lowerCamelCase       )

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










"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       ArgumentParser('Diffusers CLI tool'		,					usage='diffusers-cli <command> [<args>]'    )
  lowercase							    =       parser.add_subparsers(help='diffusers-cli command helpers'    )

  # Register commands
  EnvironmentCommand.register_subcommand(__snake_case    )

  # Let's go
  lowercase							    =       parser.parse_args()

  if not hasattr(__snake_case		,					'func'    ):
    parser.print_help()
    exit(1    )

  # Run
  lowercase							    =       args.func(__snake_case    )
  service.run()


if __name__ == "__main__":
    main()


