





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase__           =			logging.get_logger(__name__)


if is_vision_available():
					import PIL

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[int]      =							["""pixel_values"""]






							def __init__(    self	,			__lowerCAmelCase = True	,			__lowerCAmelCase = None	,			__lowerCAmelCase = PILImageResampling.BICUBIC	,			__lowerCAmelCase = True	,			__lowerCAmelCase = None	,			__lowerCAmelCase = True	,			__lowerCAmelCase = 1 / 255	,			__lowerCAmelCase = True	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = True	,			**__lowerCAmelCase	,			):
														super().__init__(**__lowerCAmelCase	)
														UpperCamelCase__					  =      size if size is not None else {"""shortest_edge""": 224}
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	,			default_to_square=__lowerCAmelCase	)
														UpperCamelCase__					  =      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	,			default_to_square=__lowerCAmelCase	,			param_name="""crop_size"""	)

														UpperCamelCase__					  =      do_resize
														UpperCamelCase__					  =      size
														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 OPENAI_CLIP_MEAN
														UpperCamelCase__					  =      image_std if image_std is not None else OPENAI_CLIP_STD
														UpperCamelCase__					  =      do_convert_rgb






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = PILImageResampling.BICUBIC	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	,			default_to_square=__lowerCAmelCase	)
														if "shortest_edge" not in size:
																					raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
														UpperCamelCase__					  =      get_resize_output_image_size(__lowerCAmelCase	,			size=size["""shortest_edge"""]	,			default_to_square=__lowerCAmelCase	)
														return resize(__lowerCAmelCase	,			size=__lowerCAmelCase	,			resample=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
														return center_crop(__lowerCAmelCase	,			size=(size["""height"""], size["""width"""])	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														return rescale(__lowerCAmelCase	,			scale=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														return normalize(__lowerCAmelCase	,			mean=__lowerCAmelCase	,			std=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = ChannelDimension.FIRST	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      do_resize if do_resize is not None else self.do_resize
														UpperCamelCase__					  =      size if size is not None else self.size
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	,			param_name="""size"""	,			default_to_square=__lowerCAmelCase	)
														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__					  =      crop_size if crop_size is not None else self.crop_size
														UpperCamelCase__					  =      get_size_dict(__lowerCAmelCase	,			param_name="""crop_size"""	,			default_to_square=__lowerCAmelCase	)
														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__					  =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														UpperCamelCase__					  =      make_list_of_images(__lowerCAmelCase	)

														if not valid_images(__lowerCAmelCase	):
																					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:
																					raise ValueError("""Size must be specified if do_resize is True."""	)

														if do_center_crop and crop_size is None:
																					raise ValueError("""Crop size 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."""	)

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					UpperCamelCase__					  =      [convert_to_rgb(__lowerCAmelCase	) for image in images]

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

														if do_resize:
																					UpperCamelCase__					  =      [self.resize(image=__lowerCAmelCase	,			size=__lowerCAmelCase	,			resample=__lowerCAmelCase	) for image in images]

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

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

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

														UpperCamelCase__					  =      [to_channel_dimension_format(__lowerCAmelCase	,			__lowerCAmelCase	) for image in images]

														UpperCamelCase__					  =      {"""pixel_values""": images}
														return BatchFeature(data=__lowerCAmelCase	,			tensor_type=__lowerCAmelCase	)











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 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[torch.FloatTensor]      =							None
							snake_case  :   torch.FloatTensor      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None






class 							__SCREAMING_SNAKE_CASE       (  _a							):






							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	)
														UpperCamelCase__					  =      project_dim
														UpperCamelCase__					  =      pooler_fn
														UpperCamelCase__					  =      learn_encoder
														UpperCamelCase__					  =      use_attention_mask






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   int      =							[r"""pooler""", r"""logit_scale"""]
							snake_case  :   Tuple      =							[r"""position_ids""", r"""predictions.decoder.bias"""]
							snake_case  :   str      =							"""roberta"""
							snake_case  :   Dict      =							RobertaSeriesConfig






							def __init__(    self	,			__lowerCAmelCase	):
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      XLMRobertaModel(__lowerCAmelCase	)
														UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""has_pre_transformation"""	,			__lowerCAmelCase	)
														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 					_lowerCamelCase		(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict

														UpperCamelCase__					  =      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:
																					UpperCamelCase__					  =      outputs["""hidden_states"""][-2]
																					UpperCamelCase__					  =      self.pre_LN(__lowerCAmelCase	)
																					UpperCamelCase__					  =      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:
																					UpperCamelCase__					  =      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	,			)




