




from typing import Dict, Iterable, 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, logging


_lowerCamelCase      :  List[str]      						=						logging.get_logger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     ["pixel_values"]



				def __init__(      self      :		int		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Dict[str, int] = None		,	_UpperCAmelCase      :		PILImageResampling = PILImageResampling.BICUBIC		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Dict[str, int] = None		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Union[int, float] = 1 / 255		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN		,	_UpperCAmelCase      :		Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD		,	**_UpperCAmelCase      :		Optional[int]		,	)  ->    None:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      size if size is not None else {"""shortest_edge""": 224}
								_lowerCAmelCase    :    str										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								_lowerCAmelCase    :    List[Any]										=      get_size_dict(_UpperCAmelCase		,	param_name="""crop_size"""      )

								_lowerCAmelCase    :    str										=      do_resize
								_lowerCAmelCase    :    List[Any]										=      size
								_lowerCAmelCase    :    Tuple										=      resample
								_lowerCAmelCase    :    Any										=      do_center_crop
								_lowerCAmelCase    :    List[str]										=      crop_size
								_lowerCAmelCase    :    Any										=      do_rescale
								_lowerCAmelCase    :    Union[str, Any]										=      rescale_factor
								_lowerCAmelCase    :    str										=      do_normalize
								_lowerCAmelCase    :    Any										=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								_lowerCAmelCase    :    Union[str, Any]										=      image_std if image_std is not None else IMAGENET_DEFAULT_STD



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





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
												_lowerCAmelCase    :    Union[str, Any]										=      int((256 / 224) * size["""shortest_edge"""]      )
												_lowerCAmelCase    :    int										=      get_resize_output_image_size(_UpperCAmelCase		,	size=_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
												_lowerCAmelCase    :    Union[str, Any]										=      {"""height""": output_size[0], """width""": output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
												raise ValueError(
												    f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"      )
								return resize(
								    _UpperCAmelCase		,	size=(size_dict["""height"""], size_dict["""width"""])		,	resample=_UpperCAmelCase		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Dict[str, int]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		Optional[Any]		,	)  ->    np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      get_size_dict(_UpperCAmelCase      )
								if "height" not in size or "width" not in size:
												raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"      )
								return center_crop(_UpperCAmelCase		,	size=(size["""height"""], size["""width"""])		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[int, float]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		Optional[Any]		,	)  ->    np.ndarray:





								'''simple docstring'''




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



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





								'''simple docstring'''




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



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase    :    str										=      resample if resample is not None else self.resample
								_lowerCAmelCase    :    List[str]										=      do_center_crop if do_center_crop is not None else self.do_center_crop
								_lowerCAmelCase    :    Optional[int]										=      do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase    :    Tuple										=      rescale_factor if rescale_factor is not None else self.rescale_factor
								_lowerCAmelCase    :    Dict										=      do_normalize if do_normalize is not None else self.do_normalize
								_lowerCAmelCase    :    Dict										=      image_mean if image_mean is not None else self.image_mean
								_lowerCAmelCase    :    str										=      image_std if image_std is not None else self.image_std

								_lowerCAmelCase    :    Tuple										=      size if size is not None else self.size
								_lowerCAmelCase    :    List[Any]										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      crop_size if crop_size is not None else self.crop_size
								_lowerCAmelCase    :    List[str]										=      get_size_dict(_UpperCAmelCase		,	param_name="""crop_size"""      )

								_lowerCAmelCase    :    Optional[int]										=      make_list_of_images(_UpperCAmelCase      )

								if not valid_images(_UpperCAmelCase      ):
												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.
								_lowerCAmelCase    :    Union[str, Any]										=      [to_numpy_array(_UpperCAmelCase      ) for image in images]

								if do_resize:
												_lowerCAmelCase    :    Union[str, Any]										=      [self.resize(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								if do_center_crop:
												_lowerCAmelCase    :    Optional[Any]										=      [self.center_crop(_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								if do_rescale:
												_lowerCAmelCase    :    Dict										=      [self.rescale(_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								if do_normalize:
												_lowerCAmelCase    :    List[str]										=      [self.normalize(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								_lowerCAmelCase    :    List[Any]										=      [to_channel_dimension_format(_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								_lowerCAmelCase    :    Tuple										=      {"""pixel_values""": images}
								return BatchFeature(data=_UpperCAmelCase		,	tensor_type=_UpperCAmelCase      )







import logging

from transformers.configuration_utils import PretrainedConfig


_lowerCamelCase      :  Union[str, Any]      						=						logging.getLogger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     "masked_bert"



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Union[str, Any]=3_0522		,	_UpperCAmelCase      :		Optional[Any]=768		,	_UpperCAmelCase      :		List[Any]=12		,	_UpperCAmelCase      :		Dict=12		,	_UpperCAmelCase      :		List[str]=3072		,	_UpperCAmelCase      :		List[Any]="gelu"		,	_UpperCAmelCase      :		Optional[Any]=0.1		,	_UpperCAmelCase      :		Optional[Any]=0.1		,	_UpperCAmelCase      :		List[Any]=512		,	_UpperCAmelCase      :		int=2		,	_UpperCAmelCase      :		Optional[int]=0.02		,	_UpperCAmelCase      :		str=1E-12		,	_UpperCAmelCase      :		Tuple=0		,	_UpperCAmelCase      :		Dict="topK"		,	_UpperCAmelCase      :		List[str]="constant"		,	_UpperCAmelCase      :		Optional[Any]=0.0		,	**_UpperCAmelCase      :		str		,	)  ->    Tuple:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase		,	**_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      vocab_size
								_lowerCAmelCase    :    List[str]										=      hidden_size
								_lowerCAmelCase    :    Any										=      num_hidden_layers
								_lowerCAmelCase    :    Optional[Any]										=      num_attention_heads
								_lowerCAmelCase    :    Optional[Any]										=      hidden_act
								_lowerCAmelCase    :    Union[str, Any]										=      intermediate_size
								_lowerCAmelCase    :    Tuple										=      hidden_dropout_prob
								_lowerCAmelCase    :    Optional[int]										=      attention_probs_dropout_prob
								_lowerCAmelCase    :    str										=      max_position_embeddings
								_lowerCAmelCase    :    int										=      type_vocab_size
								_lowerCAmelCase    :    List[str]										=      initializer_range
								_lowerCAmelCase    :    Optional[Any]										=      layer_norm_eps
								_lowerCAmelCase    :    Tuple										=      pruning_method
								_lowerCAmelCase    :    str										=      mask_init
								_lowerCAmelCase    :    List[str]										=      mask_scale

