"""simple docstring"""


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, 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, is_vision_available, logging


if is_vision_available():
    import PIL


__UpperCamelCase										=     logging.get_logger(__name__)




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = ["pixel_values"]







       def __init__(					self,				lowerCAmelCase__ = True,				lowerCAmelCase__ = None,				lowerCAmelCase__ = PIL.Image.NEAREST,				lowerCAmelCase__ = False,				lowerCAmelCase__ = None,				lowerCAmelCase__ = 1 / 255,				lowerCAmelCase__ = False,				lowerCAmelCase__ = True,				lowerCAmelCase__ = True,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				lowerCAmelCase__ = True,				**lowerCAmelCase__,				) -> None:
            super().__init__(**lowerCAmelCase__)
            snake_case_   			=   size if size is not None else {'height': 346, 'width': 346}
            snake_case_   			=   get_size_dict(lowerCAmelCase__)
            snake_case_   			=   crop_size if crop_size is not None else {'height': 289, 'width': 289}
            snake_case_   			=   get_size_dict(lowerCAmelCase__,				param_name='crop_size')

            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_   			=   rescale_offset
            snake_case_   			=   do_normalize
            snake_case_   			=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            snake_case_   			=   image_std if image_std is not None else IMAGENET_STANDARD_STD
            snake_case_   			=   include_top







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = PIL.Image.NEAREST,				lowerCAmelCase__ = None,				**lowerCAmelCase__,				) -> np.ndarray:
            snake_case_   			=   get_size_dict(lowerCAmelCase__)
            if "height" not in size or "width" not in size:
                 raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
            return resize(
                lowerCAmelCase__,				size=(size['height'], size['width']),				resample=lowerCAmelCase__,				data_format=lowerCAmelCase__,				**lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = None,				**lowerCAmelCase__,				) -> np.ndarray:
            snake_case_   			=   get_size_dict(lowerCAmelCase__)
            if "height" not in size or "width" not in size:
                 raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
            return center_crop(lowerCAmelCase__,				size=(size['height'], size['width']),				data_format=lowerCAmelCase__,				**lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = True,				lowerCAmelCase__ = None,				**lowerCAmelCase__,				) -> Any:
            if offset:
                 snake_case_   			=   (image - 127.5) * scale
                 if data_format is not None:
                      snake_case_   			=   to_channel_dimension_format(lowerCAmelCase__,				lowerCAmelCase__)
                 snake_case_   			=   rescaled_image.astype(np.floataa)
            else:
                 snake_case_   			=   rescale(lowerCAmelCase__,				scale=lowerCAmelCase__,				data_format=lowerCAmelCase__,				**lowerCAmelCase__)
            return rescaled_image







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = None,				**lowerCAmelCase__,				) -> np.ndarray:
            return normalize(lowerCAmelCase__,				mean=lowerCAmelCase__,				std=lowerCAmelCase__,				data_format=lowerCAmelCase__,				**lowerCAmelCase__)







       def 						a_		(					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__ = None,				lowerCAmelCase__ = ChannelDimension.FIRST,				**lowerCAmelCase__,				) -> PIL.Image.Image:
            snake_case_   			=   do_resize if do_resize is not None else self.do_resize
            snake_case_   			=   resample if resample is not None else self.resample
            snake_case_   			=   do_center_crop if do_center_crop is not None else self.do_center_crop
            snake_case_   			=   do_rescale if do_rescale is not None else self.do_rescale
            snake_case_   			=   rescale_factor if rescale_factor is not None else self.rescale_factor
            snake_case_   			=   rescale_offset if rescale_offset is not None else self.rescale_offset
            snake_case_   			=   do_normalize if do_normalize is not None else self.do_normalize
            snake_case_   			=   image_mean if image_mean is not None else self.image_mean
            snake_case_   			=   image_std if image_std is not None else self.image_std
            snake_case_   			=   include_top if include_top is not None else self.include_top

            snake_case_   			=   size if size is not None else self.size
            snake_case_   			=   get_size_dict(lowerCAmelCase__)
            snake_case_   			=   crop_size if crop_size is not None else self.crop_size
            snake_case_   			=   get_size_dict(lowerCAmelCase__,				param_name='crop_size')

            snake_case_   			=   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 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.')

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

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

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

            if do_rescale:
                 snake_case_   			=   [self.rescale(image=lowerCAmelCase__,				scale=lowerCAmelCase__,				offset=lowerCAmelCase__) for image in images]

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

            if include_top:
                 snake_case_   			=   [self.normalize(image=lowerCAmelCase__,				mean=[0, 0, 0],				std=lowerCAmelCase__) for image in images]

            snake_case_   			=   [to_channel_dimension_format(lowerCAmelCase__,				lowerCAmelCase__) for image in images]

            snake_case_   			=   {'pixel_values': images}
            return BatchFeature(data=lowerCAmelCase__,				tensor_type=lowerCAmelCase__)


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "nystromformer"







       def __init__(					self,				lowerCAmelCase__=3_0000,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu_new",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=510,				lowerCAmelCase__=2,				lowerCAmelCase__=64,				lowerCAmelCase__=64,				lowerCAmelCase__=65,				lowerCAmelCase__=False,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-5,				lowerCAmelCase__=1,				lowerCAmelCase__=0,				lowerCAmelCase__=2,				**lowerCAmelCase__,				) -> Optional[Any]:
            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   segment_means_seq_len
            snake_case_   			=   num_landmarks
            snake_case_   			=   conv_kernel_size
            snake_case_   			=   inv_coeff_init_option
            snake_case_   			=   layer_norm_eps
            super().__init__(pad_token_id=lowerCAmelCase__,				bos_token_id=lowerCAmelCase__,				eos_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

