




'''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 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						None
   lowerCamelCase_      =						None
   lowerCamelCase_      =						None
   lowerCamelCase_      =						None







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[Any]=1  ,				__lowerCamelCase :     str=0  ,				__lowerCamelCase :     Dict=2  ,				__lowerCamelCase :     Tuple=5_1_2  ,				__lowerCamelCase :     Optional[int]="cls"  ,				__lowerCamelCase :     Union[str, Any]=False  ,				__lowerCamelCase :     Any=True  ,				**__lowerCamelCase :     Dict  ,				):

      """simple docstring"""
      super().__init__(pad_token_id=__lowerCamelCase  ,				bos_token_id=__lowerCamelCase  ,				eos_token_id=__lowerCamelCase  ,				**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				project_dim
      _SCREAMING_SNAKE_CASE	=				pooler_fn
      _SCREAMING_SNAKE_CASE	=				learn_encoder
      _SCREAMING_SNAKE_CASE	=				use_attention_mask







class 		lowercase_   (			A       ):


   """simple docstring"""

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



   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      super().__init__(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				XLMRobertaModel(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				nn.Linear(config.hidden_size  ,				config.project_dim   )
      _SCREAMING_SNAKE_CASE	=				getattr(__lowerCamelCase  ,				"has_pre_transformation"  ,				__lowerCamelCase   )
      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 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				):

      """simple docstring"""
      _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=__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:
         _SCREAMING_SNAKE_CASE	=				outputs["hidden_states"][-2]
         _SCREAMING_SNAKE_CASE	=				self.pre_LN(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				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:
         _SCREAMING_SNAKE_CASE	=				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'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase_			=						abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Dict:
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(__A  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			str:
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   _SCREAMING_SNAKE_CASE	=				terminalreporter.config.getoption("--make-reports"  )
   if make_reports:
      pytest_terminal_summary_main(__A	,					id=__A  )

