



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_lowercase				:					Optional[Any]     					=set()
_lowercase				:					Dict     					=get_logger(__name__)




def    UpperCAmelCase					(  lowercase__						:					Optional[str] = None	):



			'''simple docstring'''





			def decorator(lowercase__						:					Callable	):
						global _emitted_deprecation_warnings

						if inspect.isclass(_lowerCAmelCase	):
									a__					=  deprecated_class_or_function.__init__
									a__					=  deprecated_class_or_function.__name__
						else:
									a__					=  deprecated_class_or_function
									a__					=  deprecated_function.__name__
									# Support deprecating __init__ class method: class name instead
									a__					=  name if name != "__init__" else deprecated_function.__qualname__.split("""."""	)[-2]

						a__					=  (
						    f'{name} is deprecated and will be removed in the next major version of datasets.' + f' {help_message}'
						    if help_message
						    else ""
						)

						@wraps(_lowerCAmelCase	)
						def wrapper(*lowercase__						:					int				,					**lowercase__						:					Tuple	):
									a__					=  hash(_lowerCAmelCase	)
									if func_hash not in _emitted_deprecation_warnings:
												warnings.warn(_lowerCAmelCase				,					category=_lowerCAmelCase				,					stacklevel=2	)
												_emitted_deprecation_warnings.add(_lowerCAmelCase	)
									return deprecated_function(*_lowerCAmelCase				,					**_lowerCAmelCase	)

						a__					=  "deprecated"

						if inspect.isclass(_lowerCAmelCase	):
									a__					=  wrapper
									return deprecated_class_or_function
						else:
									return wrapper

			return decorator
class lowerCAmelCase_       (   enum.EnumMeta			):







	'''simple docstring'''




	def __getattribute__(	cls   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  super().__getattribute__(snake_case_       )
				if isinstance(snake_case_   ,       enum.Enum       ) and obj._on_access:
							obj._on_access()
				return obj

	def __getitem__(	cls   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  super().__getitem__(snake_case_       )
				if member._on_access:
							member._on_access()
				return member







	def __call__(	cls   ,       lowerCamelCase   ,       lowerCamelCase=None   ,       *, lowerCamelCase=None   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       lowerCamelCase=1       ):



				'''simple docstring'''


				a__					=  super().__call__(snake_case_   ,       snake_case_   ,       module=snake_case_   ,       qualname=snake_case_   ,       type=snake_case_   ,       start=snake_case_       )
				if isinstance(snake_case_   ,       enum.Enum       ) and obj._on_access:
							obj._on_access()
				return obj


class lowerCAmelCase_       (   enum.Enum ,metaclass=_UpperCAmelCase			):







	'''simple docstring'''




	def __new__(	cls   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  object.__new__(cls       )
				a__					=  value
				a__					=  member.deprecate
				return member

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return ""







	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  f' {self.help_message}' if self.help_message else ""
				warnings.warn(
				    f'\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'
				    + help_message   ,       snake_case_   ,       stacklevel=3   ,       )


'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






			def __call__(							self				,			snake_case_					):
									snake_case :       Optional[Any]				=    []
									for img in imgs:
															snake_case				,					snake_case :       Dict				=    img.shape[:2]
															# later: provide list and randomly choose index for resize
															snake_case :       Dict				=    np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1					)
															if size == 0:
																					return img
															snake_case :       Any				=    size * 1.0 / min(snake_case_				,			snake_case_					)
															if h < w:
																					snake_case				,					snake_case :       Optional[int]				=    size, scale * w
															else:
																					snake_case				,					snake_case :       Tuple				=    scale * h, size
															if max(snake_case_				,			snake_case_					) > self.max_size:
																					snake_case :       Optional[int]				=    self.max_size * 1.0 / max(snake_case_				,			snake_case_					)
																					snake_case :       Dict				=    newh * scale
																					snake_case :       List[Any]				=    neww * scale
															snake_case :       int				=    int(neww + 0.5					)
															snake_case :       List[Any]				=    int(newh + 0.5					)

															if img.dtype == np.uinta:
																					snake_case :       List[Any]				=    Image.fromarray(snake_case_					)
																					snake_case :       str				=    pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR					)
																					snake_case :       List[str]				=    np.asarray(snake_case_					)
															else:
																					snake_case :       List[Any]				=    img.permute(2				,			0				,			1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																					snake_case :       Tuple				=    nn.functional.interpolate(
																					    snake_case_				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=snake_case_					).squeeze(0					)
															img_augs.append(snake_case_					)

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





