

'''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 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


lowerCamelCase				:   int      				=  logging.get_logger(__name__)







class 							A__					( UpperCamelCase_						):
  A__					=       ['pixel_values']





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





       '''simple docstring'''






       super().__init__(**__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE												=size if size is not None else {'''height''': 224, '''width''': 224}
       _SCREAMING_SNAKE_CASE												=get_size_dict(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE												=do_resize
       _SCREAMING_SNAKE_CASE												=do_rescale
       _SCREAMING_SNAKE_CASE												=do_normalize
       _SCREAMING_SNAKE_CASE												=size
       _SCREAMING_SNAKE_CASE												=resample
       _SCREAMING_SNAKE_CASE												=rescale_factor
       _SCREAMING_SNAKE_CASE												=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _SCREAMING_SNAKE_CASE												=image_std if image_std is not None else IMAGENET_STANDARD_STD





  def 		A						(	self   :			Union[str, Any]							,      _a   :			str							,      _a   :			List[Any]							,      _a   :			Dict = PILImageResampling.BILINEAR							,      _a   :			Dict = None							,      **_a   :			Union[str, Any]							,      )			->					np.ndarray:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=get_size_dict(__UpperCamelCase				)
       if "height" not in size or "width" not in size:
            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"				)
       return resize(
           __UpperCamelCase							,      size=(size['height'], size['width'])							,      resample=__UpperCamelCase							,      data_format=__UpperCamelCase							,      **__UpperCamelCase				)





  def 		A						(	self   :			Tuple							,      _a   :			int							,      _a   :			Dict							,      _a   :			Optional[int] = None							,      **_a   :			List[str]				)			->					np.ndarray:





       '''simple docstring'''






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





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





       '''simple docstring'''






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





  def 		A						(	self   :			Any							,      _a   :			Union[str, Any]							,      _a   :			List[str] = None							,      _a   :			int = None							,      _a   :			str = None							,      _a   :			Any = None							,      _a   :			int = None							,      _a   :			Tuple = None							,      _a   :			Optional[int] = None							,      _a   :			int = None							,      _a   :			int = None							,      _a   :			Any = ChannelDimension.FIRST							,      **_a   :			int							,      )			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=do_resize if do_resize is not None else self.do_resize
       _SCREAMING_SNAKE_CASE												=do_rescale if do_rescale is not None else self.do_rescale
       _SCREAMING_SNAKE_CASE												=do_normalize if do_normalize is not None else self.do_normalize
       _SCREAMING_SNAKE_CASE												=resample if resample is not None else self.resample
       _SCREAMING_SNAKE_CASE												=rescale_factor if rescale_factor is not None else self.rescale_factor
       _SCREAMING_SNAKE_CASE												=image_mean if image_mean is not None else self.image_mean
       _SCREAMING_SNAKE_CASE												=image_std if image_std is not None else self.image_std

       _SCREAMING_SNAKE_CASE												=size if size is not None else self.size
       _SCREAMING_SNAKE_CASE												=get_size_dict(__UpperCamelCase				)

       _SCREAMING_SNAKE_CASE												=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_rescale and rescale_factor is None:
            raise ValueError('Rescale factor must be specified if do_rescale is True.'				)

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

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

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

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

       _SCREAMING_SNAKE_CASE												=[to_channel_dimension_format(__UpperCamelCase							,      __UpperCamelCase				) for image in images]

       _SCREAMING_SNAKE_CASE												={'''pixel_values''': images}
       return BatchFeature(data=__UpperCamelCase							,      tensor_type=__UpperCamelCase				)






'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase				:   Dict      				=  logging.get_logger(__name__)

lowerCamelCase				:   Dict      				=  {
    "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 							A__					( A__						):
  A__					=       'nystromformer'





  def __init__(	self   :			List[str]							,      _a   :			Dict=3_0000							,      _a   :			Dict=768							,      _a   :			Union[str, Any]=12							,      _a   :			Optional[int]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Optional[Any]="gelu_new"							,      _a   :			Dict=0.1							,      _a   :			Dict=0.1							,      _a   :			Union[str, Any]=510							,      _a   :			Any=2							,      _a   :			List[Any]=64							,      _a   :			int=64							,      _a   :			Optional[Any]=65							,      _a   :			str=False							,      _a   :			List[Any]=0.02							,      _a   :			List[str]=1e-5							,      _a   :			Optional[Any]=1							,      _a   :			Optional[Any]=0							,      _a   :			List[Any]=2							,      **_a   :			List[Any]							,      )			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=segment_means_seq_len
       _SCREAMING_SNAKE_CASE												=num_landmarks
       _SCREAMING_SNAKE_CASE												=conv_kernel_size
       _SCREAMING_SNAKE_CASE												=inv_coeff_init_option
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       super().__init__(pad_token_id=_a							,      bos_token_id=_a							,      eos_token_id=_a							,      **_a				)
