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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_      =      logging.get_logger(__name__)






class      __lowercase					(    _UpperCAmelCase    ):
						lowercase					 =     ['pixel_values']

						def __init__(	self				:      Union[str, Any]							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      Optional[Dict[str, int]] = None							,							__lowerCamelCase				:      PILImageResampling = PILImageResampling.BICUBIC							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      Union[int, float] = 1 / 2_55							,							__lowerCamelCase				:      Dict[str, int] = None							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      Optional[Union[float, List[float]]] = None							,							__lowerCamelCase				:      Optional[Union[float, List[float]]] = None							,							**__lowerCamelCase				:      Any							,							)							->		None:


									'''simple docstring'''

									super().__init__(**__UpperCamelCase   )
									lowercase							=		size if size is not None else {'''height''': 2_24, '''width''': 2_24}
									lowercase							=		get_size_dict(__UpperCamelCase   )
									lowercase							=		crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
									lowercase							=		get_size_dict(__UpperCamelCase							,							default_to_square=__UpperCamelCase							,							param_name='''crop_size'''   )

									lowercase							=		do_resize
									lowercase							=		do_rescale
									lowercase							=		do_normalize
									lowercase							=		do_center_crop
									lowercase							=		crop_size
									lowercase							=		size
									lowercase							=		resample
									lowercase							=		rescale_factor
									lowercase							=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									lowercase							=		image_std if image_std is not None else IMAGENET_DEFAULT_STD

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      np.ndarray							,							__lowerCamelCase				:      Dict[str, int]							,							__lowerCamelCase				:      PILImageResampling = PILImageResampling.BILINEAR							,							__lowerCamelCase				:      Optional[Union[str, ChannelDimension]] = None							,							**__lowerCamelCase				:      str							,							)							->		np.ndarray:


									'''simple docstring'''

									lowercase							=		get_size_dict(__UpperCamelCase   )

									if "shortest_edge" in size:
												lowercase							=		get_resize_output_image_size(__UpperCamelCase							,							size=size['''shortest_edge''']							,							default_to_square=__UpperCamelCase   )
												# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
									elif "height" in size and "width" in size:
												lowercase							=		(size['''height'''], size['''width'''])
									else:
												raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
									return resize(__UpperCamelCase							,							size=__UpperCamelCase							,							resample=__UpperCamelCase							,							data_format=__UpperCamelCase							,							**__UpperCamelCase   )

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


									'''simple docstring'''

									lowercase							=		get_size_dict(__UpperCamelCase   )
									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(__UpperCamelCase							,							size=(size['''height'''], size['''width'''])							,							data_format=__UpperCamelCase							,							**__UpperCamelCase   )

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      np.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      Optional[Union[str, ChannelDimension]] = None							,							**__lowerCamelCase				:      List[str]   )							->		np.ndarray:


									'''simple docstring'''

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

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      np.ndarray							,							__lowerCamelCase				:      Union[float, List[float]]							,							__lowerCamelCase				:      Union[float, List[float]]							,							__lowerCamelCase				:      Optional[Union[str, ChannelDimension]] = None							,							**__lowerCamelCase				:      Dict							,							)							->		np.ndarray:


									'''simple docstring'''

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



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      ImageInput							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Dict[str, int] = None							,							__lowerCamelCase				:      PILImageResampling = None							,							__lowerCamelCase				:      bool = None							,							__lowerCamelCase				:      int = None							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Optional[float] = None							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Optional[Union[float, List[float]]] = None							,							__lowerCamelCase				:      Optional[Union[float, List[float]]] = None							,							__lowerCamelCase				:      Optional[Union[str, TensorType]] = None							,							__lowerCamelCase				:      Union[str, ChannelDimension] = ChannelDimension.FIRST							,							**__lowerCamelCase				:      Dict							,							)							->		BatchFeature:


									'''simple docstring'''

									lowercase							=		do_resize if do_resize is not None else self.do_resize
									lowercase							=		do_rescale if do_rescale is not None else self.do_rescale
									lowercase							=		do_normalize if do_normalize is not None else self.do_normalize
									lowercase							=		do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase							=		crop_size if crop_size is not None else self.crop_size
									lowercase							=		get_size_dict(__UpperCamelCase							,							param_name='''crop_size'''							,							default_to_square=__UpperCamelCase   )
									lowercase							=		resample if resample is not None else self.resample
									lowercase							=		rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase							=		image_mean if image_mean is not None else self.image_mean
									lowercase							=		image_std if image_std is not None else self.image_std

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

									if not is_batched(__UpperCamelCase   ):
												lowercase							=		[images]

									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.'''   )

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

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

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

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

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

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

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






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

A_      =      TypeVar("KEY")
A_      =      TypeVar("VAL")






@dataclass(frozen=_A       ,     slots=_A    )
class      __lowercase					(    Generic[KEY, VAL]    ):
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _Item    ):

						def __init__(	self				:      Optional[int]   )							->		None:


									'''simple docstring'''

									super().__init__(__lowerCamelCase							,							__lowerCamelCase   )



						def __bool__(	self				:      str   )							->		bool:


									'''simple docstring'''

									return False





A_      =      _DeletedItem()






class      __lowercase					(    MutableMapping[KEY, VAL]    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      int = 8							,							__lowerCamelCase				:      float = 0.75   )							->		None:


									'''simple docstring'''

									lowercase							=		initial_block_size
									lowercase							=		[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase							=		capacity_factor
									lowercase							=		0

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KEY   )							->		int:


									'''simple docstring'''

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

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      int   )							->		int:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		bool:


									'''simple docstring'''

									lowercase							=		self._buckets[ind]
									if not stored:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												self._len += 1
												return True
									elif stored.key == key:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												return True
									else:
												return False

						def        __a		(	self				:      Optional[Any]   )							->		bool:


									'''simple docstring'''

									lowercase							=		len(self._buckets   ) * self._capacity_factor
									return len(self   ) >= int(__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		bool:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int   )							->		None:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]   )							->		None:


									'''simple docstring'''

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

						def        __a		(	self				:      Any   )							->		None:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY   )							->		Iterator[int]:


									'''simple docstring'''

									lowercase							=		self._get_bucket_index(__lowerCamelCase   )
									for _ in range(len(self._buckets   )   ):
												yield ind
												lowercase							=		self._get_next_ind(__lowerCamelCase   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

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

						def __setitem__(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

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

									self._add_item(__lowerCamelCase							,							__lowerCamelCase   )

						def __delitem__(	self				:      List[str]							,							__lowerCamelCase				:      KEY   )							->		None:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												lowercase							=		self._buckets[ind]
												if item is None:
															raise KeyError(__lowerCamelCase   )
												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				:      Any							,							__lowerCamelCase				:      KEY   )							->		VAL:


									'''simple docstring'''

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

						def __len__(	self				:      str   )							->		int:


									'''simple docstring'''

									return self._len

						def __iter__(	self				:      Union[str, Any]   )							->		Iterator[KEY]:


									'''simple docstring'''

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



						def __repr__(	self				:      Any   )							->		str:


									'''simple docstring'''

									lowercase							=		''' ,'''.join(
									    f'{item.key}: {item.val}' for item in self._buckets if item   )
									return f'HashMap({val_string})'





