
"""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 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=sys.maxsize       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       "bilinear"
										__lowercase       =       max_size
										__lowercase       =       short_edge_length





							def __call__(     self  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										__lowercase       =       []
										for img in imgs:
													__lowercase   ,					__lowercase       =       img.shape[:2]
													# later: provide list and randomly choose index for resize
													__lowercase       =       np.random.randint(self.short_edge_length[0]  ,    self.short_edge_length[1] + 1       )
													if size == 0:
																return img
													__lowercase       =       size * 1.0 / min(_lowerCamelCase  ,    _lowerCamelCase       )
													if h < w:
																__lowercase   ,					__lowercase       =       size, scale * w
													else:
																__lowercase   ,					__lowercase       =       scale * h, size
													if max(_lowerCamelCase  ,    _lowerCamelCase       ) > self.max_size:
																__lowercase       =       self.max_size * 1.0 / max(_lowerCamelCase  ,    _lowerCamelCase       )
																__lowercase       =       newh * scale
																__lowercase       =       neww * scale
													__lowercase       =       int(neww + 0.5       )
													__lowercase       =       int(newh + 0.5       )

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

										return img_augs





class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,    cfg.INPUT.MAX_SIZE_TEST       )
										__lowercase       =       cfg.INPUT.FORMAT
										__lowercase       =       cfg.SIZE_DIVISIBILITY
										__lowercase       =       cfg.PAD_VALUE
										__lowercase       =       cfg.INPUT.MAX_SIZE_TEST
										__lowercase       =       cfg.MODEL.DEVICE
										__lowercase       =       torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )  ,    1  ,    1       )
										__lowercase       =       torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )  ,    1  ,    1       )
										__lowercase       =       lambda _lowerCamelCase       : (x - self.pixel_mean) / self.pixel_std
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       tuple(max(_lowerCamelCase       ) for s in zip(*[img.shape for img in images]       )       )
										__lowercase       =       [im.shape[-2:] for im in images]
										__lowercase       =       [
										    nn.functional.pad(
										        _lowerCamelCase  ,    [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]  ,    value=self.pad_value  ,    )
										    for size, im in zip(_lowerCamelCase  ,    _lowerCamelCase       )
										]

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





							def __call__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=False       )			->							Any:




										'''simple docstring'''
										with torch.no_grad():
													if not isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
																__lowercase       =       [images]
													if single_image:
																assert len(_lowerCamelCase       ) == 1
													for i in range(len(_lowerCamelCase       )       ):
																if isinstance(images[i]  ,    torch.Tensor       ):
																			images.insert(_lowerCamelCase  ,    images.pop(_lowerCamelCase       ).to(self.device       ).float()       )
																elif not isinstance(images[i]  ,    torch.Tensor       ):
																			images.insert(
																			    _lowerCamelCase  ,    torch.as_tensor(img_tensorize(images.pop(_lowerCamelCase       )  ,    input_format=self.input_format       )       )
																			    .to(self.device       )
																			    .float()  ,    )
            # resize smallest edge
													__lowercase       =       torch.tensor([im.shape[:2] for im in images]       )
													__lowercase       =       self.aug(_lowerCamelCase       )
													# 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
													__lowercase       =       [self.normalizer(_lowerCamelCase       ) for x in images]
													# now pad them to do the following operations
													__lowercase   ,					__lowercase       =       self.pad(_lowerCamelCase       )
													# Normalize

													if self.size_divisibility > 0:
																raise NotImplementedError()
													# pad
													__lowercase       =       torch.true_divide(_lowerCamelCase  ,    _lowerCamelCase       )
													if single_image:
																return images[0], sizes[0], scales_yx[0]
													else:
																return images, sizes, scales_yx





def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Dict:
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes







def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Tuple:
			assert torch.isfinite(__magic_name__      ).all(), "Box tensor contains infinite or NaN!"
			__lowercase   ,					__lowercase       =       box_size
			tensor[:, 0].clamp_(min=0			,   max=__magic_name__      )
			tensor[:, 1].clamp_(min=0			,   max=__magic_name__      )
			tensor[:, 2].clamp_(min=0			,   max=__magic_name__      )
			tensor[:, 3].clamp_(min=0			,   max=__magic_name__      )







"""simple docstring"""






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_lowercase							=				get_logger(__name__)

class 					__a     ( enum.Enum       ):



							'''simple docstring'''







							_lowerCamelCase					:			Tuple          =   """all_checks"""
							_lowerCamelCase					:			Optional[int]          =   """basic_checks"""
							_lowerCamelCase					:			str          =   """no_checks"""





class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__=None      )						->Union[str, Any]:
			if expected_checksums is None:
						logger.info("Unable to verify checksums."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedDownloadedFile(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			__lowercase       =       " for " + verification_name if verification_name is not None else ""
			if len(__magic_name__      ) > 0:
						raise NonMatchingChecksumError(
						    F'''Checksums didn\'t match{for_verification_name}:\n'''
						    F'''{bad_urls}\n'''
						    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"      )
			logger.info("All the checksums matched successfully" + for_verification_name      )

class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Any:
			if expected_splits is None:
						logger.info("Unable to verify splits sizes."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [
			    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(__magic_name__      ) > 0:
						raise NonMatchingSplitsSizesError(str(__magic_name__      )      )
			logger.info("All the splits matched successfully."      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__ = True      )						->dict:
			if record_checksum:
						__lowercase       =       shaaaa()
						with open(__magic_name__			,   "rb"      ) as f:
									for chunk in iter(lambda: f.read(1 << 2_0      )			,   b""      ):
												m.update(__magic_name__      )
									__lowercase       =       m.hexdigest()
			else:
						__lowercase       =       None
			return {"num_bytes": os.path.getsize(__magic_name__      ), "checksum": checksum}







def      lowerCAmelCase__			(		__magic_name__      )						->List[str]:
			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False





