



import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def 	_UpperCAmelCase       (   snake_case			,		snake_case=2_56    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					min(img.size    )

					if s < target_image_size:
										raise ValueError(F'min dim for image {s} < {target_image_size}'    )

					_lowerCAmelCase      			=					target_image_size / s
					_lowerCAmelCase      			=					(round(r * img.size[1]    ), round(r * img.size[0]    ))
					_lowerCAmelCase      			=					TF.resize(snake_case			,		snake_case			,		interpolation=PIL.Image.LANCZOS    )
					_lowerCAmelCase      			=					TF.center_crop(snake_case			,		output_size=2 * [target_image_size]    )
					_lowerCAmelCase      			=					torch.unsqueeze(T.ToTensor()(snake_case    )			,		0    )
					return img


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					2.0 * x - 1.0
					return x


def 	_UpperCAmelCase       (   snake_case			,		snake_case=True			,		snake_case="RGB"    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					x.detach().cpu()
					if process:
										_lowerCAmelCase      			=					post_process_tensor(snake_case    )
					_lowerCAmelCase      			=					x.numpy()
					if process:
										_lowerCAmelCase      			=					(2_55 * x).astype(np.uinta    )
					_lowerCAmelCase      			=					Image.fromarray(snake_case    )
					if not x.mode == mode:
										_lowerCAmelCase      			=					x.convert(snake_case    )
					return x


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					torch.clamp(snake_case			,		-1.0			,		1.0    )
					_lowerCAmelCase      			=					(x + 1.0) / 2.0
					_lowerCAmelCase      			=					x.permute(1			,		2			,		0    )
					return x


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					post_process_tensor(x.squeeze()    )
					return x.permute(2			,		0			,		1    ).unsqueeze(0    )





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

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


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__			   = abspath(join(dirname(dirname(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 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(snake_case    )


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					from transformers.testing_utils import pytest_terminal_summary_main

					_lowerCAmelCase      			=					terminalreporter.config.getoption("""--make-reports"""    )
					if make_reports:
										pytest_terminal_summary_main(snake_case			,		id=snake_case    )



