

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       snake_case__  :







			def __init__(			self			: str			,  lowercase			: Any			,  lowercase			: Optional[int]=sys.maxsize  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				"bilinear"
								UpperCAmelCase			:   Optional[int]      =				max_size
								UpperCAmelCase			:   Optional[int]      =				short_edge_length







			def __call__(			self			: Dict			,  lowercase			: int  ):





								'''simple docstring'''



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

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

								return img_augs







class       snake_case__  :







			def __init__(			self			: Optional[Any]			,  lowercase			: str  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				tuple(max(lowercase  ) for s in zip(*[img.shape for img in images]  )  )
								UpperCAmelCase			:   Tuple      =				[im.shape[-2:] for im in images]
								UpperCAmelCase			:   Dict      =				[
								    nn.functional.pad(
								        lowercase			,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,  value=self.pad_value			,  )
								    for size, im in zip(lowercase			,  lowercase  )
								]

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







			def __call__(			self			: Union[str, Any]			,  lowercase			: List[Any]			,  lowercase			: Optional[Any]=False  ):





								'''simple docstring'''



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

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



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		List[str]	):




					'''simple docstring'''






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




def   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		Tuple[int, int]	):




					'''simple docstring'''






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





"""simple docstring"""






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case_   :   Union[str, Any]        =       abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					config.addinivalue_line(
					    "markers"							,      "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"	)
					config.addinivalue_line(
					    "markers"							,      "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"	)
					config.addinivalue_line("markers"							,      "is_pipeline_test: mark test to run only when pipelines are tested"	)
					config.addinivalue_line("markers"							,      "is_staging_test: mark test to run only in the staging environment"	)
					config.addinivalue_line("markers"							,      "accelerate_tests: mark test that require accelerate"	)
					config.addinivalue_line("markers"							,      "tool_tests: mark the tool tests that are run on their specific schedule"	)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(_lowercase	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_terminal_summary_main

					UpperCAmelCase			:   Optional[int]      =				terminalreporter.config.getoption("--make-reports"	)
					if make_reports:
										pytest_terminal_summary_main(_lowercase							,      id=_lowercase	)



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		Dict	):




					'''simple docstring'''






					if exitstatus == 5:
										UpperCAmelCase			:   List[str]      =				0


# Doctest custom flag to ignore output.
snake_case_   :   Union[str, Any]        =       doctest.register_optionflag("""IGNORE_RESULT""")

snake_case_   :   Optional[int]        =       doctest.OutputChecker







class       snake_case__  ( lowerCAmelCase_     ):







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Tuple			,  lowercase			: Optional[Any]			,  lowercase			: int  ):





								'''simple docstring'''



								if IGNORE_RESULT & optionflags:
													return True
								return OutputChecker.check_output(self			,  lowercase			,  lowercase			,  lowercase  )

snake_case_   :   List[str]        =       CustomOutputChecker
snake_case_   :   Optional[Any]        =       HfDoctestModule




snake_case_   :   List[str]        =       HfDocTestParser


