



"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
				import PIL

if is_torch_available():
				import torch


lowerCAmelCase__					  =  logging.get_logger(__name__)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		["""pixel_values"""]







					def __init__(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BILINEAR				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Union[int, float] = 1 / 255				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     bool = True				,       **SCREAMING_SNAKE_CASE :     Tuple				,       ):
									super().__init__(**__UpperCAmelCase					)
									lowercase__					:   Any													=					size if size is not None else {"shortest_edge": 224}
									lowercase__					:   List[str]													=					get_size_dict(__UpperCAmelCase				,       default_to_square=__UpperCAmelCase					)
									lowercase__					:   int													=					crop_size if crop_size is not None else {"height": 256, "width": 256}
									lowercase__					:   str													=					get_size_dict(__UpperCAmelCase				,       param_name="crop_size"					)

									lowercase__					:   Any													=					do_resize
									lowercase__					:   List[Any]													=					size
									lowercase__					:   Optional[int]													=					resample
									lowercase__					:   Optional[Any]													=					do_rescale
									lowercase__					:   Optional[Any]													=					rescale_factor
									lowercase__					:   Tuple													=					do_center_crop
									lowercase__					:   str													=					crop_size
									lowercase__					:   str													=					do_flip_channel_order







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Dict[str, int]				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PIL.Image.BILINEAR				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     List[Any]				,       ):
									lowercase__					:   List[str]													=					get_size_dict(__UpperCAmelCase				,       default_to_square=__UpperCAmelCase					)
									if "shortest_edge" not in size:
													raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""					)
									lowercase__					:   str													=					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 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Dict[str, int]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     Any				,       ):
									lowercase__					:   List[Any]													=					get_size_dict(__UpperCAmelCase					)
									if "height" not in size or "width" not in size:
													raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""					)
									return center_crop(__UpperCAmelCase				,       size=(size["height"], size["width"])				,       data_format=__UpperCAmelCase				,       **__UpperCAmelCase					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[int, float]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     Optional[int]				,       ):
									return rescale(__UpperCAmelCase				,       scale=__UpperCAmelCase				,       data_format=__UpperCAmelCase				,       **__UpperCAmelCase					)







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None					):
									return flip_channel_order(__UpperCAmelCase				,       data_format=__UpperCAmelCase					)







					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     ImageInput				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     float = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       SCREAMING_SNAKE_CASE :     ChannelDimension = ChannelDimension.FIRST				,       **SCREAMING_SNAKE_CASE :     int				,       ):
									lowercase__					:   List[Any]													=					do_resize if do_resize is not None else self.do_resize
									lowercase__					:   List[Any]													=					resample if resample is not None else self.resample
									lowercase__					:   List[Any]													=					do_rescale if do_rescale is not None else self.do_rescale
									lowercase__					:   int													=					rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__					:   int													=					do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase__					:   List[Any]													=					(
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									lowercase__					:   Optional[int]													=					size if size is not None else self.size
									lowercase__					:   List[Any]													=					get_size_dict(__UpperCAmelCase				,       default_to_square=__UpperCAmelCase					)
									lowercase__					:   int													=					crop_size if crop_size is not None else self.crop_size
									lowercase__					:   Dict													=					get_size_dict(__UpperCAmelCase				,       param_name="crop_size"					)

									lowercase__					:   Optional[Any]													=					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_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."					)

									if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."					)

									# All transformations expect numpy arrays.
									lowercase__					:   Union[str, Any]													=					[to_numpy_array(__UpperCAmelCase					) for image in images]

									if do_resize:
													lowercase__					:   int													=					[self.resize(image=__UpperCAmelCase				,       size=__UpperCAmelCase				,       resample=__UpperCAmelCase					) for image in images]

									if do_center_crop:
													lowercase__					:   Union[str, Any]													=					[self.center_crop(image=__UpperCAmelCase				,       size=__UpperCAmelCase					) for image in images]

									if do_rescale:
													lowercase__					:   Union[str, Any]													=					[self.rescale(image=__UpperCAmelCase				,       scale=__UpperCAmelCase					) for image in images]

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
													lowercase__					:   List[Any]													=					[self.flip_channel_order(image=__UpperCAmelCase					) for image in images]

									lowercase__					:   Optional[Any]													=					[to_channel_dimension_format(__UpperCAmelCase				,       __UpperCAmelCase					) for image in images]

									lowercase__					:   Any													=					{"pixel_values": images}
									return BatchFeature(data=__UpperCAmelCase				,       tensor_type=__UpperCAmelCase					)







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     List[Tuple] = None					):
									lowercase__					:   Optional[int]													=					outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
													if len(__UpperCAmelCase					) != len(__UpperCAmelCase					):
																	raise ValueError(
																	    "Make sure that you pass in as many target sizes as the batch dimension of the logits"					)

													if is_torch_tensor(__UpperCAmelCase					):
																	lowercase__					:   Optional[Any]													=					target_sizes.numpy()

													lowercase__					:   Optional[int]													=					[]

													for idx in range(len(__UpperCAmelCase					)					):
																	lowercase__					:   Optional[Any]													=					torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0					)				,       size=target_sizes[idx]				,       mode="bilinear"				,       align_corners=__UpperCAmelCase					)
																	lowercase__					:   Optional[int]													=					resized_logits[0].argmax(dim=0					)
																	semantic_segmentation.append(__UpperCAmelCase					)
									else:
													lowercase__					:   Union[str, Any]													=					logits.argmax(dim=1					)
													lowercase__					:   Any													=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

									return semantic_segmentation









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

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

@dataclass(frozen=_UpperCamelCase    ,      slots=_UpperCamelCase      )
class 				snake_case__(Generic[KEY, VAL]      ):

					"""simple docstring"""






					lowercase_								=		42
					lowercase_								=		42






class 				snake_case__(_Item      ):

					"""simple docstring"""






					def __init__(							self :     List[str]					):
									super().__init__(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __bool__(							self :     Tuple					):
									return False






lowerCAmelCase__					  =  _DeletedItem()

class 				snake_case__(MutableMapping[KEY, VAL]      ):

					"""simple docstring"""






					def __init__(							self :     Any				,       SCREAMING_SNAKE_CASE :     int = 8				,       SCREAMING_SNAKE_CASE :     float = 0.75					):
									lowercase__					:   Any													=					initial_block_size
									lowercase__					:   list[_Item | None]													=					[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase__					:   Dict													=					capacity_factor
									lowercase__					:   Optional[int]													=					0







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									return hash(SCREAMING_SNAKE_CASE					) % len(self._buckets					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     int					):
									return (ind + 1) % len(self._buckets					)







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







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







					def 			snake_case					(							self :     List[str]					):
									if len(self._buckets					) <= self._initial_block_size:
													return False
									lowercase__					:   Optional[Any]													=					len(self._buckets					) * self._capacity_factor / 2
									return len(self					) < limit







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







					def 			snake_case					(							self :     int					):
									self._resize(len(self._buckets					) * 2					)







					def 			snake_case					(							self :     Optional[Any]					):
									self._resize(len(self._buckets					) // 2					)







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									lowercase__					:   Tuple													=					self._get_bucket_index(SCREAMING_SNAKE_CASE					)
									for _ in range(len(self._buckets					)					):
													yield ind
													lowercase__					:   Union[str, Any]													=					self._get_next_ind(SCREAMING_SNAKE_CASE					)







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







					def __setitem__(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									if self._is_full():
													self._size_up()

									self._add_item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







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







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







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







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







					def __repr__(							self :     str					):
									lowercase__					:   int													=					" ,".join(
									    f"""{item.key}: {item.val}""" for item in self._buckets if item					)
									return f"""HashMap({val_string})"""




