


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			(_a						):
       _lowercase											=     None
       _lowercase											=     None
       _lowercase											=     None
       _lowercase											=     None



class 	__lowerCamelCase			(_a						):







       def __init__(			self: Optional[Any],A_: str=1,A_: int=0,A_: Tuple=2,A_: Optional[int]=512,A_: Tuple="cls",A_: Any=False,A_: int=True,**A_: Optional[Any],):






            '''simple docstring'''




            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			(_a						):
       _lowercase											=     [R"""pooler""", R"""logit_scale"""]
       _lowercase											=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       _lowercase											=     """roberta"""
       _lowercase											=     RobertaSeriesConfig







       def __init__(			self: int,A_: List[str]					):






            '''simple docstring'''




            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 		snake_case_	(			self: List[Any],A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[bool] = None,A_: Optional[bool] = None,A_: Optional[bool] = None,):






            '''simple docstring'''




            __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,)




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


