





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase_    =    logging.get_logger(__name__)


if is_vision_available():
	import PIL



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''pixel_values''']






			def __init__(self ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = PILImageResampling.BICUBIC ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = 1 / 255 ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							**__magic_name__ ,							)					->       None:


							'''simple docstring'''



							super().__init__(**__magic_name__   )
							snake_case_						:   str           =  size if size is not None else {'''shortest_edge''': 224}
							snake_case_						:   Optional[Any]           =  get_size_dict(__magic_name__ ,							default_to_square=__magic_name__   )
							snake_case_						:   Optional[int]           =  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							snake_case_						:   Tuple           =  get_size_dict(__magic_name__ ,							default_to_square=__magic_name__ ,							param_name='''crop_size'''   )

							snake_case_						:   Tuple           =  do_resize
							snake_case_						:   List[str]           =  size
							snake_case_						:   Any           =  resample
							snake_case_						:   List[Any]           =  do_center_crop
							snake_case_						:   Optional[int]           =  crop_size
							snake_case_						:   List[Any]           =  do_rescale
							snake_case_						:   Tuple           =  rescale_factor
							snake_case_						:   Any           =  do_normalize
							snake_case_						:   Dict           =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							snake_case_						:   Dict           =  image_std if image_std is not None else OPENAI_CLIP_STD
							snake_case_						:   Optional[int]           =  do_convert_rgb






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = PILImageResampling.BICUBIC ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  get_size_dict(__magic_name__   )
							if "height" not in size or "width" not in size:
											raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''   )
							return center_crop(__magic_name__ ,							size=(size['''height'''], size['''width''']) ,							data_format=__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       Optional[int]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = ChannelDimension.FIRST ,							**__magic_name__ ,							)					->       PIL.Image.Image:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  do_resize if do_resize is not None else self.do_resize
							snake_case_						:   Dict           =  size if size is not None else self.size
							snake_case_						:   Optional[int]           =  get_size_dict(__magic_name__ ,							param_name='''size''' ,							default_to_square=__magic_name__   )
							snake_case_						:   Tuple           =  resample if resample is not None else self.resample
							snake_case_						:   Any           =  do_center_crop if do_center_crop is not None else self.do_center_crop
							snake_case_						:   Tuple           =  crop_size if crop_size is not None else self.crop_size
							snake_case_						:   Optional[int]           =  get_size_dict(__magic_name__ ,							param_name='''crop_size''' ,							default_to_square=__magic_name__   )
							snake_case_						:   Any           =  do_rescale if do_rescale is not None else self.do_rescale
							snake_case_						:   Union[str, Any]           =  rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_						:   List[str]           =  do_normalize if do_normalize is not None else self.do_normalize
							snake_case_						:   Tuple           =  image_mean if image_mean is not None else self.image_mean
							snake_case_						:   Optional[int]           =  image_std if image_std is not None else self.image_std
							snake_case_						:   Optional[Any]           =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							snake_case_						:   Optional[Any]           =  make_list_of_images(__magic_name__   )

							if not valid_images(__magic_name__   ):
											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.'''   )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
											snake_case_						:   Optional[int]           =  [convert_to_rgb(__magic_name__   ) for image in images]

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

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

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

							if do_rescale:
											snake_case_						:   List[Any]           =  [self.rescale(image=__magic_name__ ,							scale=__magic_name__   ) for image in images]

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

							snake_case_						:   str           =  [to_channel_dimension_format(__magic_name__ ,							__magic_name__   ) for image in images]

							snake_case_						:   Optional[Any]           =  {'''pixel_values''': images}
							return BatchFeature(data=__magic_name__ ,							tensor_type=__magic_name__   )







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

lowerCAmelCase_    =    TypeVar('''KEY''')
lowerCAmelCase_    =    TypeVar('''VAL''')



@dataclass(frozen=_a,					slots=_a					)
class  __lowerCAmelCase	(      Generic[KEY, VAL]					):
			lowerCamelCase_					:					KEY
			lowerCamelCase_					:					VAL




class  __lowerCAmelCase	(      _Item					):






			def __init__(self   )					->       None:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )






			def __bool__(self   )					->       bool:


							'''simple docstring'''



							return False




lowerCAmelCase_    =    _DeletedItem()



class  __lowerCAmelCase	(      MutableMapping[KEY, VAL]					):






			def __init__(self ,							__magic_name__ = 8 ,							__magic_name__ = 0.75   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  initial_block_size
							snake_case_						:   list[_Item | None]           =  [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							snake_case_						:   List[str]           =  capacity_factor
							snake_case_						:   int           =  0






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return hash(__magic_name__   ) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return (ind + 1) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self._buckets[ind]
							if not stored:
											snake_case_						:   Optional[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											self._len += 1
											return True
							elif stored.key == key:
											snake_case_						:   List[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											return True
							else:
											return False






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Any           =  len(self._buckets   ) * self._capacity_factor
							return len(self   ) >= int(__magic_name__   )






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self._buckets
							snake_case_						:   int           =  [None] * new_size
							snake_case_						:   Optional[int]           =  0
							for item in old_buckets:
											if item:
															self._add_item(item.key ,							item.val   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) * 2   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) // 2   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Iterator[int]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self._get_bucket_index(__magic_name__   )
							for _ in range(len(self._buckets   )   ):
											yield ind
											snake_case_						:   Tuple           =  self._get_next_ind(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											if self._try_set(__magic_name__ ,							__magic_name__ ,							__magic_name__   ):
															break






			def __setitem__(self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							if self._is_full():
											self._size_up()

							self._add_item(__magic_name__ ,							__magic_name__   )






			def __delitem__(self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   Optional[Any]           =  self._buckets[ind]
											if item is None:
															raise KeyError(__magic_name__   )
											if item is _deleted:
															continue
											if item.key == key:
															snake_case_						:   Union[str, Any]           =  _deleted
															self._len -= 1
															break
							if self._is_sparse():
											self._size_down()






			def __getitem__(self ,							__magic_name__   )					->       VAL:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   List[Any]           =  self._buckets[ind]
											if item is None:
															break
											if item is _deleted:
															continue
											if item.key == key:
															return item.val
							raise KeyError(__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self._len






			def __iter__(self   )					->       Iterator[KEY]:


							'''simple docstring'''



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






			def __repr__(self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  ''' ,'''.join(
							    F'''{item.key}: {item.val}''' for item in self._buckets if item   )
							return F'''HashMap({val_string})'''
