





"""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 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  None
       lowerCAmelCase__						 =  None
       lowerCAmelCase__						 =  None
       lowerCAmelCase__						 =  None



class 	lowerCAmelCase					(      snake_case							):






       def __init__(   self    ,  a__=1    ,  a__=0    ,  a__=2    ,  a__=5_12    ,  a__="cls"    ,  a__=False    ,  a__=True    ,  **a__    ,  ):
              super().__init__(pad_token_id=a__    ,  bos_token_id=a__    ,  eos_token_id=a__    ,  **a__     )
              _UpperCAmelCase     =     project_dim
              _UpperCAmelCase     =     pooler_fn
              _UpperCAmelCase     =     learn_encoder
              _UpperCAmelCase     =     use_attention_mask



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__						 =  [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__						 =  """roberta"""
       lowerCAmelCase__						 =  RobertaSeriesConfig






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






       def 							__A (   self    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  ):
              _UpperCAmelCase     =     return_dict if return_dict is not None else self.config.use_return_dict

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

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

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













"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)



@dataclass
class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  42
       lowerCAmelCase__						 =  42
       lowerCAmelCase__						 =  42


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker






