
"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
		import PIL

UpperCamelCase_       :    str						      =			logging.get_logger(__name__)
def 				A_  (__a					):

					'''simple docstring'''





					if isinstance(__a					, (list, tuple)					) and isinstance(videos[0]					, (list, tuple)					) and is_valid_image(videos[0][0]					):
										return videos

					elif isinstance(__a					, (list, tuple)					) and is_valid_image(videos[0]					):
										return [videos]

					elif is_valid_image(__a					):
										return [[videos]]

					raise ValueError(f'Could not make batched video from {videos}'					)




class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					["pixel_values"]




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







								"""simple docstring"""
								super().__init__(**_snake_case						)
								A_								=    size if size is not None else {"shortest_edge": 256}
								A_								=    get_size_dict(_snake_case							, default_to_square=_snake_case						)
								A_								=    crop_size if crop_size is not None else {"height": 224, "width": 224}
								A_								=    get_size_dict(_snake_case							, param_name="crop_size"						)

								A_								=    do_resize
								A_								=    size
								A_								=    do_center_crop
								A_								=    crop_size
								A_								=    resample
								A_								=    do_rescale
								A_								=    rescale_factor
								A_								=    offset
								A_								=    do_normalize
								A_								=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								A_								=    image_std if image_std is not None else IMAGENET_STANDARD_STD




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







								"""simple docstring"""
								A_								=    get_size_dict(_snake_case							, default_to_square=_snake_case						)
								if "shortest_edge" in size:
													A_								=    get_resize_output_image_size(_snake_case							, size["shortest_edge"]							, default_to_square=_snake_case						)
								elif "height" in size and "width" in size:
													A_								=    (size["height"], size["width"])
								else:
													raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
								return resize(_snake_case							, size=_snake_case							, resample=_snake_case							, data_format=_snake_case							, **_snake_case						)




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







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




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       np.ndarray							, _snake_case    :       Union[int, float]							, _snake_case    :       bool = True							, _snake_case    :       Optional[Union[str, ChannelDimension]] = None							, **_snake_case    :       Any							, )   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    image.astype(np.floataa						)
								if offset:
													A_								=    image - (scale / 2)
								return rescale(_snake_case							, scale=_snake_case							, data_format=_snake_case							, **_snake_case						)




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







								"""simple docstring"""
								return normalize(_snake_case							, mean=_snake_case							, std=_snake_case							, data_format=_snake_case							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       ImageInput							, _snake_case    :       bool = None							, _snake_case    :       Dict[str, int] = None							, _snake_case    :       PILImageResampling = None							, _snake_case    :       bool = None							, _snake_case    :       Dict[str, int] = None							, _snake_case    :       bool = None							, _snake_case    :       float = None							, _snake_case    :       bool = None							, _snake_case    :       bool = None							, _snake_case    :       Optional[Union[float, List[float]]] = None							, _snake_case    :       Optional[Union[float, List[float]]] = None							, _snake_case    :       Optional[ChannelDimension] = ChannelDimension.FIRST							, )   ->			np.ndarray:







								"""simple docstring"""
								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_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."						)

								if offset and not do_rescale:
													raise ValueError("For offset, do_rescale must also be set to True."						)

								# All transformations expect numpy arrays.
								A_								=    to_numpy_array(_snake_case						)

								if do_resize:
													A_								=    self.resize(image=_snake_case							, size=_snake_case							, resample=_snake_case						)

								if do_center_crop:
													A_								=    self.center_crop(_snake_case							, size=_snake_case						)

								if do_rescale:
													A_								=    self.rescale(image=_snake_case							, scale=_snake_case							, offset=_snake_case						)

								if do_normalize:
													A_								=    self.normalize(image=_snake_case							, mean=_snake_case							, std=_snake_case						)

								A_								=    to_channel_dimension_format(_snake_case							, _snake_case						)
								return image






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







								"""simple docstring"""
								A_								=    do_resize if do_resize is not None else self.do_resize
								A_								=    resample if resample is not None else self.resample
								A_								=    do_center_crop if do_center_crop is not None else self.do_center_crop
								A_								=    do_rescale if do_rescale is not None else self.do_rescale
								A_								=    rescale_factor if rescale_factor is not None else self.rescale_factor
								A_								=    offset if offset is not None else self.offset
								A_								=    do_normalize if do_normalize is not None else self.do_normalize
								A_								=    image_mean if image_mean is not None else self.image_mean
								A_								=    image_std if image_std is not None else self.image_std

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

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

								A_								=    make_batched(_snake_case						)

								A_								=    [
								    [
								        self._preprocess_image(
								            image=_snake_case							, do_resize=_snake_case							, size=_snake_case							, resample=_snake_case							, do_center_crop=_snake_case							, crop_size=_snake_case							, do_rescale=_snake_case							, rescale_factor=_snake_case							, offset=_snake_case							, do_normalize=_snake_case							, image_mean=_snake_case							, image_std=_snake_case							, data_format=_snake_case							, )
								        for img in video
								    ]
								    for video in videos
								]

								A_								=    {"pixel_values": videos}
								return BatchFeature(data=_snake_case							, tensor_type=_snake_case						)






"""simple docstring"""





from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)

# TODO: upload to AWS
UpperCamelCase_       :    List[str]						      =			{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"retribert"




			def __init__(					self    :       str							, _snake_case    :       List[str]=30_522							, _snake_case    :       List[Any]=768							, _snake_case    :       List[Any]=8							, _snake_case    :       Union[str, Any]=12							, _snake_case    :       Optional[Any]=3_072							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       Dict=0.1							, _snake_case    :       str=512							, _snake_case    :       Tuple=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Optional[Any]=1e-12							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=128							, _snake_case    :       Union[str, Any]=0							, **_snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								super().__init__(pad_token_id=_snake_case							, **_snake_case						)

								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    hidden_act
								A_								=    intermediate_size
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    max_position_embeddings
								A_								=    type_vocab_size
								A_								=    initializer_range
								A_								=    layer_norm_eps
								A_								=    share_encoders
								A_								=    projection_dim




