






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

__snake_case      :      Optional[Any]             =       TypeVar("""KEY""")
__snake_case      :      str             =       TypeVar("""VAL""")






@dataclass(frozen=__lowercase						,   slots=__lowercase)
class       __SCREAMING_SNAKE_CASE   (				Generic[KEY, VAL]):
						_SCREAMING_SNAKE_CASE		:    KEY
						_SCREAMING_SNAKE_CASE		:    VAL






class       __SCREAMING_SNAKE_CASE   (				_Item):






						def __init__(		self				):






												"""simple docstring"""
												super().__init__(_UpperCamelCase       ,  _UpperCamelCase				)







						def __bool__(		self				):






												"""simple docstring"""
												return False




__snake_case      :      int             =       _DeletedItem()






class       __SCREAMING_SNAKE_CASE   (				MutableMapping[KEY, VAL]):






						def __init__(		self       ,  _UpperCamelCase = 8       ,  _UpperCamelCase = 0.75				):






												"""simple docstring"""
												lowerCAmelCase__        =  initial_block_size
												lowerCAmelCase__        =  [None] * initial_block_size
												assert 0.0 < capacity_factor < 1.0
												lowerCAmelCase__        =  capacity_factor
												lowerCAmelCase__        =  0






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return hash(_UpperCamelCase				) % len(self._buckets				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return (ind + 1) % len(self._buckets				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self._buckets[ind]
												if not stored:
																		lowerCAmelCase__        =  _Item(_UpperCamelCase       ,  _UpperCamelCase				)
																		self._len += 1
																		return True
												elif stored.key == key:
																		lowerCAmelCase__        =  _Item(_UpperCamelCase       ,  _UpperCamelCase				)
																		return True
												else:
																		return False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  len(self._buckets				) * self._capacity_factor
												return len(self				) >= int(_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if len(self._buckets				) <= self._initial_block_size:
																		return False
												lowerCAmelCase__        =  len(self._buckets				) * self._capacity_factor / 2
												return len(self				) < limit






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self._buckets
												lowerCAmelCase__        =  [None] * new_size
												lowerCAmelCase__        =  0
												for item in old_buckets:
																		if item:
																								self._add_item(item.key       ,  item.val				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self._resize(len(self._buckets				) * 2				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self._resize(len(self._buckets				) // 2				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self._get_bucket_index(_UpperCamelCase				)
												for _ in range(len(self._buckets				)				):
																		yield ind
																		lowerCAmelCase__        =  self._get_next_ind(_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												for ind in self._iterate_buckets(_UpperCamelCase				):
																		if self._try_set(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):
																								break






						def __setitem__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if self._is_full():
																		self._size_up()

												self._add_item(_UpperCamelCase       ,  _UpperCamelCase				)






						def __delitem__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												for ind in self._iterate_buckets(_UpperCamelCase				):
																		lowerCAmelCase__        =  self._buckets[ind]
																		if item is None:
																								raise KeyError(_UpperCamelCase				)
																		if item is _deleted:
																								continue
																		if item.key == key:
																								lowerCAmelCase__        =  _deleted
																								self._len -= 1
																								break
												if self._is_sparse():
																		self._size_down()






						def __getitem__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												for ind in self._iterate_buckets(_UpperCamelCase				):
																		lowerCAmelCase__        =  self._buckets[ind]
																		if item is None:
																								break
																		if item is _deleted:
																								continue
																		if item.key == key:
																								return item.val
												raise KeyError(_UpperCamelCase				)






						def __len__(		self				):






												"""simple docstring"""
												return self._len






						def __iter__(		self				):






												"""simple docstring"""
												yield from (item.key for item in self._buckets if item)







						def __repr__(		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  ' ,'.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 ...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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''pixel_values''']






						def __init__(		self       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PILImageResampling.BILINEAR       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  _UpperCamelCase = 1 / 2_55       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(**_UpperCamelCase				)
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 2_56}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  resample
												lowerCAmelCase__        =  do_center_crop
												lowerCAmelCase__        =  crop_size
												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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = PILImageResampling.BICUBIC       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												if "shortest_edge" not in size:
																		raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
												lowerCAmelCase__        =  get_resize_output_image_size(_UpperCamelCase       ,  size=size['shortest_edge']       ,  default_to_square=_UpperCamelCase				)
												return resize(_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												return center_crop(_UpperCamelCase       ,  size=(size['height'], size['width'])       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return rescale(_UpperCamelCase       ,  scale=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






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







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = ChannelDimension.FIRST       ,  **_UpperCamelCase       ,  ):






												"""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
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  resample if resample is not None else self.resample
												lowerCAmelCase__        =  do_center_crop if do_center_crop is not None else self.do_center_crop
												lowerCAmelCase__        =  crop_size if crop_size is not None else self.crop_size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												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(_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_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.
												lowerCAmelCase__        =  [to_numpy_array(_UpperCamelCase				) for image in images]

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

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

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

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

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

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





