"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_SCREAMING_SNAKE_CASE	:		Dict   =   logging.get_logger(__name__)


if is_vision_available():
     import PIL






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							int    =    ["pixel_values"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE = True ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,				_SCREAMING_SNAKE_CASE = True ,				_SCREAMING_SNAKE_CASE = False ,				_SCREAMING_SNAKE_CASE = True ,				_SCREAMING_SNAKE_CASE = True ,				_SCREAMING_SNAKE_CASE = 1 / 255 ,				_SCREAMING_SNAKE_CASE = True ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      super().__init__(**_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					size if size is not None else {'height': 2560, 'width': 1920}
      if isinstance(_SCREAMING_SNAKE_CASE ,				(tuple, list)	):
         # The previous feature extractor size parameter was in (width, height) format
         __lowerCamelCase    		=					size[::-1]
      __lowerCamelCase    		=					get_size_dict(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					do_resize
      __lowerCamelCase    		=					size
      __lowerCamelCase    		=					resample
      __lowerCamelCase    		=					do_thumbnail
      __lowerCamelCase    		=					do_align_long_axis
      __lowerCamelCase    		=					do_pad
      __lowerCamelCase    		=					do_rescale
      __lowerCamelCase    		=					rescale_factor
      __lowerCamelCase    		=					do_normalize
      __lowerCamelCase    		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __lowerCamelCase    		=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					get_image_size(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase		,__lowerCamelCase    		=					size['height'], size['width']

      if (output_width < output_height and input_width > input_height) or (
          output_width > output_height and input_width < input_height
      ):
         __lowerCamelCase    		=					np.rotaa(_SCREAMING_SNAKE_CASE ,				3	)

      if data_format is not None:
         __lowerCamelCase    		=					to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      return image







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = False ,				_SCREAMING_SNAKE_CASE = None ,				):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					size['height'], size['width']
      __lowerCamelCase		,__lowerCamelCase    		=					get_image_size(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					output_width - input_width
      __lowerCamelCase    		=					output_height - input_height

      if random_padding:
         __lowerCamelCase    		=					np.random.randint(low=0 ,				high=delta_height + 1	)
         __lowerCamelCase    		=					np.random.randint(low=0 ,				high=delta_width + 1	)
      else:
         __lowerCamelCase    		=					delta_height // 2
         __lowerCamelCase    		=					delta_width // 2

      __lowerCamelCase    		=					delta_height - pad_top
      __lowerCamelCase    		=					delta_width - pad_left

      __lowerCamelCase    		=					((pad_top, pad_bottom), (pad_left, pad_right))
      return pad(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				data_format=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'	)
      return self.pad_image(*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,				_SCREAMING_SNAKE_CASE = None ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					get_image_size(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase		,__lowerCamelCase    		=					size['height'], size['width']

      # We always resize to the smallest of either the input or output size.
      __lowerCamelCase    		=					min(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					min(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      if height == input_height and width == input_width:
         return image

      if input_height > input_width:
         __lowerCamelCase    		=					int(input_width * height / input_height	)
      elif input_width > input_height:
         __lowerCamelCase    		=					int(input_height * width / input_width	)

      return resize(
          _SCREAMING_SNAKE_CASE ,				size=(height, width) ,				resample=_SCREAMING_SNAKE_CASE ,				reducing_gap=2.0 ,				data_format=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,				_SCREAMING_SNAKE_CASE = None ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					get_size_dict(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					min(size['height'] ,				size['width']	)
      __lowerCamelCase    		=					get_resize_output_image_size(_SCREAMING_SNAKE_CASE ,				size=_SCREAMING_SNAKE_CASE ,				default_to_square=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					resize(_SCREAMING_SNAKE_CASE ,				size=_SCREAMING_SNAKE_CASE ,				resample=_SCREAMING_SNAKE_CASE ,				data_format=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)
      return resized_image







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = False ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					do_resize if do_resize is not None else self.do_resize
      __lowerCamelCase    		=					size if size is not None else self.size
      if isinstance(_SCREAMING_SNAKE_CASE ,				(tuple, list)	):
         # Previous feature extractor had size in (width, height) format
         __lowerCamelCase    		=					size[::-1]
      __lowerCamelCase    		=					get_size_dict(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					resample if resample is not None else self.resample
      __lowerCamelCase    		=					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
      __lowerCamelCase    		=					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
      __lowerCamelCase    		=					do_pad if do_pad is not None else self.do_pad
      __lowerCamelCase    		=					do_rescale if do_rescale is not None else self.do_rescale
      __lowerCamelCase    		=					rescale_factor if rescale_factor is not None else self.rescale_factor
      __lowerCamelCase    		=					do_normalize if do_normalize is not None else self.do_normalize
      __lowerCamelCase    		=					image_mean if image_mean is not None else self.image_mean
      __lowerCamelCase    		=					image_std if image_std is not None else self.image_std

      __lowerCamelCase    		=					make_list_of_images(_SCREAMING_SNAKE_CASE	)

      if not valid_images(_SCREAMING_SNAKE_CASE	):
         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.'	)

      if do_pad and size is None:
         raise ValueError('Size must be specified if do_pad 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    		=					[to_numpy_array(_SCREAMING_SNAKE_CASE	) for image in images]

      if do_align_long_axis:
         __lowerCamelCase    		=					[self.align_long_axis(_SCREAMING_SNAKE_CASE ,				size=_SCREAMING_SNAKE_CASE	) for image in images]

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

      if do_thumbnail:
         __lowerCamelCase    		=					[self.thumbnail(image=_SCREAMING_SNAKE_CASE ,				size=_SCREAMING_SNAKE_CASE	) for image in images]

      if do_pad:
         __lowerCamelCase    		=					[self.pad_image(image=_SCREAMING_SNAKE_CASE ,				size=_SCREAMING_SNAKE_CASE ,				random_padding=_SCREAMING_SNAKE_CASE	) for image in images]

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

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

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

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









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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






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

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
