


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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase       :       List[Any]						       =				logging.get_logger(__name__)

class       __magic_name__	(     __lowerCAmelCase):
     A:		Tuple			=      ["pixel_values"]


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Optional[Dict[str, int]] = None							,	lowerCamelCase__							:     PILImageResampling = PILImageResampling.BILINEAR							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Dict[str, int] = None							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Union[int, float] = 1 / 255							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Optional[Union[float, List[float]]] = None							,	lowerCamelCase__							:     Optional[Union[float, List[float]]] = None							,	**lowerCamelCase__							:     Tuple							,	)  ->      None:





      '''simple docstring'''






      super().__init__(**lowerCamelCase__			)
      UpperCamelCase__					:    int          =   size if size is not None else {'''shortest_edge''': 256}
      UpperCamelCase__					:    Optional[Any]          =   get_size_dict(lowerCamelCase__							,	default_to_square=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      UpperCamelCase__					:    List[Any]          =   get_size_dict(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[Any]          =   do_resize
      UpperCamelCase__					:    List[Any]          =   size
      UpperCamelCase__					:    List[str]          =   resample
      UpperCamelCase__					:    str          =   do_center_crop
      UpperCamelCase__					:    Optional[Any]          =   crop_size
      UpperCamelCase__					:    Tuple          =   do_rescale
      UpperCamelCase__					:    Optional[Any]          =   rescale_factor
      UpperCamelCase__					:    Optional[int]          =   do_normalize
      UpperCamelCase__					:    Union[str, Any]          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCamelCase__					:    Tuple          =   image_std if image_std is not None else IMAGENET_STANDARD_STD


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     np.ndarray							,	lowerCamelCase__							:     Dict[str, int]							,	lowerCamelCase__							:     PILImageResampling = PILImageResampling.BICUBIC							,	lowerCamelCase__							:     Optional[Union[str, ChannelDimension]] = None							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      np.ndarray:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   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__					:    int          =   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      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     np.ndarray							,	lowerCamelCase__							:     Dict[str, int]							,	lowerCamelCase__							:     Optional[Union[str, ChannelDimension]] = None							,	**lowerCamelCase__							:     List[str]							,	)  ->      np.ndarray:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   get_size_dict(lowerCamelCase__			)
      return center_crop(lowerCamelCase__							,	size=(size['''height'''], size['''width'''])							,	data_format=lowerCamelCase__							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     np.ndarray							,	lowerCamelCase__							:     float							,	lowerCamelCase__							:     Optional[Union[str, ChannelDimension]] = None							,	**lowerCamelCase__							:     Dict			)  ->      np.ndarray:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     np.ndarray							,	lowerCamelCase__							:     Union[float, List[float]]							,	lowerCamelCase__							:     Union[float, List[float]]							,	lowerCamelCase__							:     Optional[Union[str, ChannelDimension]] = None							,	**lowerCamelCase__							:     Union[str, Any]							,	)  ->      np.ndarray:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     ImageInput							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Dict[str, int] = None							,	lowerCamelCase__							:     PILImageResampling = None							,	lowerCamelCase__							:     bool = None							,	lowerCamelCase__							:     Dict[str, int] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[float] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[Union[float, List[float]]] = None							,	lowerCamelCase__							:     Optional[Union[float, List[float]]] = None							,	lowerCamelCase__							:     Optional[Union[str, TensorType]] = None							,	lowerCamelCase__							:     Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**lowerCamelCase__							:     List[str]							,	)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__					:    List[str]          =   size if size is not None else self.size
      UpperCamelCase__					:    int          =   get_size_dict(lowerCamelCase__							,	default_to_square=lowerCamelCase__			)
      UpperCamelCase__					:    Any          =   resample if resample is not None else self.resample
      UpperCamelCase__					:    Tuple          =   do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__					:    Tuple          =   crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__					:    Dict          =   get_size_dict(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__					:    int          =   rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__					:    Dict          =   do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase__					:    str          =   image_mean if image_mean is not None else self.image_mean
      UpperCamelCase__					:    int          =   image_std if image_std is not None else self.image_std

      UpperCamelCase__					:    int          =   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.'''			)

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

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

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

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

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

      UpperCamelCase__					:    Any          =   [to_channel_dimension_format(lowerCamelCase__							,	lowerCamelCase__			) for image in images]

      UpperCamelCase__					:    str          =   {'''pixel_values''': images}
      return BatchFeature(data=lowerCamelCase__							,	tensor_type=lowerCamelCase__			)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






      super().__init__(pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__			)

      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_size
      UpperCamelCase__					:    List[str]          =   num_hidden_layers
      UpperCamelCase__					:    Optional[int]          =   num_attention_heads
      UpperCamelCase__					:    List[str]          =   hidden_act
      UpperCamelCase__					:    Optional[int]          =   intermediate_size
      UpperCamelCase__					:    Optional[int]          =   hidden_dropout_prob
      UpperCamelCase__					:    List[str]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   max_position_embeddings
      UpperCamelCase__					:    Optional[Any]          =   type_vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Union[str, Any]          =   position_embedding_type
      UpperCamelCase__					:    Optional[int]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout



class       __magic_name__	(     __lowerCAmelCase):


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






      if self.task == "multiple-choice":
       UpperCamelCase__					:    Dict          =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       UpperCamelCase__					:    Any          =   {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]			)


