


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


__snake_case   							=						logging.get_logger(__name__)



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""pixel_values"""]







       def __init__(			self: List[str],A_: bool = True,A_: Dict[str, int] = None,A_: int = 0.9,A_: PILImageResampling = PILImageResampling.BICUBIC,A_: bool = True,A_: Dict[str, int] = None,A_: Union[int, float] = 1 / 255,A_: bool = True,A_: bool = True,A_: Optional[Union[float, List[float]]] = None,A_: Optional[Union[float, List[float]]] = None,**A_: int,):






            '''simple docstring'''




            super().__init__(**A_					)
            __UpperCamelCase =			size if size is not None else {'shortest_edge': 224}
            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)
            __UpperCamelCase =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
            __UpperCamelCase =			get_size_dict(A_,param_name='crop_size'					)

            __UpperCamelCase =			do_resize
            __UpperCamelCase =			size
            __UpperCamelCase =			crop_pct
            __UpperCamelCase =			resample
            __UpperCamelCase =			do_center_crop
            __UpperCamelCase =			crop_size
            __UpperCamelCase =			do_rescale
            __UpperCamelCase =			rescale_factor
            __UpperCamelCase =			do_normalize
            __UpperCamelCase =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            __UpperCamelCase =			image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def 		snake_case_	(			self: Union[str, Any],A_: np.ndarray,A_: Dict[str, int],A_: Optional[float] = None,A_: PILImageResampling = PILImageResampling.BICUBIC,A_: Optional[Union[str, ChannelDimension]] = None,**A_: List[Any],):






            '''simple docstring'''




            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)
            if "shortest_edge" not in size and ("height" not in size or "width" not in size):
                 raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
            if crop_pct is not None:
                 if "shortest_edge" in size:
                      __UpperCamelCase =			int(size['shortest_edge'] / crop_pct					)
                 elif "height" in size and "width" in size:
                      if size["height"] == size["width"]:
                           __UpperCamelCase =			int(size['height'] / crop_pct					)
                      else:
                           __UpperCamelCase =			(int(size['height'] / crop_pct					), int(size['width'] / crop_pct					))
                 else:
                      raise ValueError('Invalid size for resize: {}'.format(A_					)					)

                 __UpperCamelCase =			get_resize_output_image_size(A_,size=A_,default_to_square=A_					)
            else:
                 if "shortest_edge" in size:
                      __UpperCamelCase =			get_resize_output_image_size(A_,size=size['shortest_edge'],default_to_square=A_					)
                 elif "height" in size and "width" in size:
                      __UpperCamelCase =			(size['height'], size['width'])
                 else:
                      raise ValueError('Invalid size for resize: {}'.format(A_					)					)

            return resize(A_,size=A_,resample=A_,data_format=A_,**A_					)







       def 		snake_case_	(			self: int,A_: np.ndarray,A_: Dict[str, int],A_: Optional[Union[str, ChannelDimension]] = None,**A_: str,):






            '''simple docstring'''




            __UpperCamelCase =			get_size_dict(A_					)
            if "height" not in size or "width" not in size:
                 raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''					)
            return center_crop(A_,size=(size['height'], size['width']),data_format=A_,**A_					)







       def 		snake_case_	(			self: Optional[Any],A_: np.ndarray,A_: Union[int, float],A_: Optional[Union[str, ChannelDimension]] = None,**A_: Optional[int],):






            '''simple docstring'''




            return rescale(A_,scale=A_,data_format=A_,**A_					)







       def 		snake_case_	(			self: List[Any],A_: np.ndarray,A_: Union[float, List[float]],A_: Union[float, List[float]],A_: Optional[Union[str, ChannelDimension]] = None,**A_: Any,):






            '''simple docstring'''




            return normalize(A_,mean=A_,std=A_,data_format=A_,**A_					)







       def 		snake_case_	(			self: Tuple,A_: ImageInput,A_: bool = None,A_: Dict[str, int] = None,A_: int = None,A_: PILImageResampling = None,A_: bool = None,A_: Dict[str, int] = None,A_: bool = None,A_: float = None,A_: bool = None,A_: Optional[Union[float, List[float]]] = None,A_: Optional[Union[float, List[float]]] = None,A_: Optional[Union[str, TensorType]] = None,A_: ChannelDimension = ChannelDimension.FIRST,**A_: int,):






            '''simple docstring'''




            __UpperCamelCase =			do_resize if do_resize is not None else self.do_resize
            __UpperCamelCase =			crop_pct if crop_pct is not None else self.crop_pct
            __UpperCamelCase =			resample if resample is not None else self.resample
            __UpperCamelCase =			do_center_crop if do_center_crop is not None else self.do_center_crop
            __UpperCamelCase =			do_rescale if do_rescale is not None else self.do_rescale
            __UpperCamelCase =			rescale_factor if rescale_factor is not None else self.rescale_factor
            __UpperCamelCase =			do_normalize if do_normalize is not None else self.do_normalize
            __UpperCamelCase =			image_mean if image_mean is not None else self.image_mean
            __UpperCamelCase =			image_std if image_std is not None else self.image_std

            __UpperCamelCase =			size if size is not None else self.size
            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)
            __UpperCamelCase =			crop_size if crop_size is not None else self.crop_size
            __UpperCamelCase =			get_size_dict(A_,param_name='crop_size'					)

            __UpperCamelCase =			make_list_of_images(A_					)

            if not valid_images(A_					):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'					)

            if do_resize and size is None or resample is None:
                 raise ValueError('Size and resample must be specified if do_resize is True.'					)

            if do_center_crop and crop_pct is None:
                 raise ValueError('Crop_pct must be specified if do_center_crop is True.'					)

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

            # All transformations expect numpy arrays.
            __UpperCamelCase =			[to_numpy_array(A_					) for image in images]

            if do_resize:
                 __UpperCamelCase =			[self.resize(image=A_,size=A_,crop_pct=A_,resample=A_					) for image in images]

            if do_center_crop:
                 __UpperCamelCase =			[self.center_crop(image=A_,size=A_					) for image in images]

            if do_rescale:
                 __UpperCamelCase =			[self.rescale(image=A_,scale=A_					) for image in images]

            if do_normalize:
                 __UpperCamelCase =			[self.normalize(image=A_,mean=A_,std=A_					) for image in images]

            __UpperCamelCase =			[to_channel_dimension_format(A_,A_					) for image in images]

            __UpperCamelCase =			{'pixel_values': images}
            return BatchFeature(data=A_,tensor_type=A_					)






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 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    _lowerCamelCase=512   ,    _lowerCamelCase="cls"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Tuple									=			project_dim
       a		:Optional[int]									=			pooler_fn
       a		:int									=			learn_encoder
       a		:int									=			use_attention_mask







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[r'pooler', r'logit_scale']
 SCREAMING_SNAKE_CASE__ =			[r'position_ids', r'predictions.decoder.bias']
 SCREAMING_SNAKE_CASE__ =			'roberta'
 SCREAMING_SNAKE_CASE__ =			RobertaSeriesConfig




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )
       a		:Tuple									=			XLMRobertaModel(_lowerCamelCase       )
       a		:Optional[Any]									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
       a		:Optional[int]									=			getattr(_lowerCamelCase   ,    '''has_pre_transformation'''   ,    _lowerCamelCase       )
       if self.has_pre_transformation:
             a		:Tuple									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
             a		:Union[str, Any]									=			nn.LayerNorm(config.hidden_size   ,    eps=config.layer_norm_eps       )
       self.post_init()




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    ):
       a		:Optional[int]									=			return_dict if return_dict is not None else self.config.use_return_dict

       a		:int									=			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:
             a		:Optional[int]									=			outputs['''hidden_states'''][-2]
             a		:List[Any]									=			self.pre_LN(_lowerCamelCase       )
             a		:Optional[Any]									=			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:
             a		:List[str]									=			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   ,    )





