

'''simple docstring'''






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase__   		=      TypeVar('''KEY''')
lowerCAmelCase__   		=      TypeVar('''VAL''')






@dataclass(frozen=lowerCamelCase__							,			slots=lowerCamelCase__ )
class      lowercase_      (Generic[KEY, VAL] ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    KEY
  SCREAMING_SNAKE_CASE       :    VAL






class      lowercase_      (_Item ):




  """simple docstring"""




  def __init__( self  :	Optional[int]     ):
   super().__init__(lowercase__   ,lowercase__     )






  def __bool__( self  :	List[str]     ):
   return False
lowerCAmelCase__   		=      _DeletedItem()






class      lowercase_      (MutableMapping[KEY, VAL] ):




  """simple docstring"""




  def __init__( self  :	Dict   ,lowercase__  :	int = 8   ,lowercase__  :	float = 0.7_5     ):
   __lowercase				    =					initial_block_size
   __lowercase				    =					[None] * initial_block_size
   assert 0.0 < capacity_factor < 1.0
   __lowercase				    =					capacity_factor
   __lowercase				    =					0






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	KEY     ):
   return hash(lowercase__     ) % len(self._buckets     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	int     ):
   return (ind + 1) % len(self._buckets     )






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	int   ,lowercase__  :	KEY   ,lowercase__  :	VAL     ):
   __lowercase				    =					self._buckets[ind]
   if not stored:
    __lowercase				    =					_Item(lowercase__   ,lowercase__     )
    self._len += 1
    return True
   elif stored.key == key:
    __lowercase				    =					_Item(lowercase__   ,lowercase__     )
    return True
   else:
    return False






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					len(self._buckets     ) * self._capacity_factor
   return len(self     ) >= int(lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   if len(self._buckets     ) <= self._initial_block_size:
    return False
   __lowercase				    =					len(self._buckets     ) * self._capacity_factor / 2
   return len(self     ) < limit






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	int     ):
   __lowercase				    =					self._buckets
   __lowercase				    =					[None] * new_size
   __lowercase				    =					0
   for item in old_buckets:
    if item:
     self._add_item(item.key   ,item.val     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   self._resize(len(self._buckets     ) * 2     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   self._resize(len(self._buckets     ) // 2     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	KEY     ):
   __lowercase				    =					self._get_bucket_index(lowercase__     )
   for _ in range(len(self._buckets     )     ):
    yield ind
    __lowercase				    =					self._get_next_ind(lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	KEY   ,lowercase__  :	VAL     ):
   for ind in self._iterate_buckets(lowercase__     ):
    if self._try_set(lowercase__   ,lowercase__   ,lowercase__     ):
     break






  def __setitem__( self  :	str   ,lowercase__  :	KEY   ,lowercase__  :	VAL     ):
   if self._is_full():
    self._size_up()

   self._add_item(lowercase__   ,lowercase__     )






  def __delitem__( self  :	Tuple   ,lowercase__  :	KEY     ):
   for ind in self._iterate_buckets(lowercase__     ):
    __lowercase				    =					self._buckets[ind]
    if item is None:
     raise KeyError(lowercase__     )
    if item is _deleted:
     continue
    if item.key == key:
     __lowercase				    =					_deleted
     self._len -= 1
     break
   if self._is_sparse():
    self._size_down()






  def __getitem__( self  :	Tuple   ,lowercase__  :	KEY     ):
   for ind in self._iterate_buckets(lowercase__     ):
    __lowercase				    =					self._buckets[ind]
    if item is None:
     break
    if item is _deleted:
     continue
    if item.key == key:
     return item.val
   raise KeyError(lowercase__     )






  def __len__( self  :	Optional[int]     ):
   return self._len






  def __iter__( self  :	str     ):
   yield from (item.key for item in self._buckets if item)






  def __repr__( self  :	Optional[Any]     ):
   __lowercase				    =					''' ,'''.join(
       F"{item.key}: {item.val}" for item in self._buckets if item     )
   return F"HashMap({val_string})"









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			:	Union[str, Any]								=logging.get_logger(__name__)



def  a__ (__lowercase							:str   )		-> List[List[ImageInput]]:
				if isinstance(__lowercase					,  (list, tuple)   ) and isinstance(videos[0]					,  (list, tuple)   ) and is_valid_image(videos[0][0]   ):
								return videos

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

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

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






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BILINEAR       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = True       ,A__ = None       ,A__ = None       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				Tuple   =      size if size is not None else {'''shortest_edge''': 256}
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				str   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				str   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						_A	:				Tuple   =      do_resize
						_A	:				Optional[Any]   =      size
						_A	:				Optional[Any]   =      do_center_crop
						_A	:				List[str]   =      crop_size
						_A	:				Dict   =      resample
						_A	:				Tuple   =      do_rescale
						_A	:				int   =      rescale_factor
						_A	:				List[str]   =      offset
						_A	:				List[Any]   =      do_normalize
						_A	:				List[Any]   =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_A	:				List[str]   =      image_std if image_std is not None else IMAGENET_STANDARD_STD







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BILINEAR       ,A__ = None       ,**A__       ,):
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" in size:
										_A	:				Optional[Any]   =      get_resize_output_image_size(A__       ,size['''shortest_edge''']       ,default_to_square=A__		)
						elif "height" in size and "width" in size:
										_A	:				str   =      (size['''height'''], size['''width'''])
						else:
										raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Optional[int]   =      get_size_dict(A__		)
						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(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				Any   =      image.astype(np.floataa		)
						if offset:
										_A	:				List[str]   =      image - (scale / 2)
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,):
						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	:				Dict   =      to_numpy_array(A__		)

						if do_resize:
										_A	:				Any   =      self.resize(image=A__       ,size=A__       ,resample=A__		)

						if do_center_crop:
										_A	:				str   =      self.center_crop(A__       ,size=A__		)

						if do_rescale:
										_A	:				Optional[int]   =      self.rescale(image=A__       ,scale=A__       ,offset=A__		)

						if do_normalize:
										_A	:				Dict   =      self.normalize(image=A__       ,mean=A__       ,std=A__		)

						_A	:				Union[str, Any]   =      to_channel_dimension_format(A__       ,A__		)
						return image







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				Optional[int]   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      resample if resample is not None else self.resample
						_A	:				Union[str, Any]   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Tuple   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				List[str]   =      offset if offset is not None else self.offset
						_A	:				Optional[int]   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Optional[Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				Dict   =      image_std if image_std is not None else self.image_std

						_A	:				str   =      size if size is not None else self.size
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				Optional[int]   =      get_size_dict(A__       ,param_name='''crop_size'''		)

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

						_A	:				List[str]   =      make_batched(A__		)

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

						_A	:				Optional[int]   =      {'''pixel_values''': videos}
						return BatchFeature(data=A__       ,tensor_type=A__		)



