

'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = 8		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=do_rescale
										lowercase :							List[Any]    		=rescale_factor
										lowercase :							Tuple    		=do_pad
										lowercase :							List[str]    		=pad_size







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		float		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Union[str, Any]    		=get_image_size(UpperCAmelCase__   )
										lowercase :							Tuple    		=(old_height // size + 1) * size - old_height
										lowercase :							Tuple    		=(old_width // size + 1) * size - old_width

										return pad(UpperCAmelCase__		,					((0, pad_height), (0, pad_width))		,					mode='''symmetric'''		,					data_format=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[float] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										lowercase :							int    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							Tuple    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							int    		=do_pad if do_pad is not None else self.do_pad
										lowercase :							List[Any]    		=pad_size if pad_size is not None else self.pad_size

										lowercase :							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_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										# All transformations expect numpy arrays.
										lowercase :							Dict    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							Tuple    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_pad:
																lowercase :							Union[str, Any]    		=[self.pad(UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										lowercase :							Dict    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )








'''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.
UpperCamelCase_    						=       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 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Tuple:
						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 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Any:
						from transformers.testing_utils import pytest_addoption_shared

						pytest_addoption_shared(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     Any						)				->  Any:
						from transformers.testing_utils import pytest_terminal_summary_main

						lowercase :							Optional[Any]    		=terminalreporter.config.getoption('''--make-reports'''						)
						if make_reports:
												pytest_terminal_summary_main(__magic_name__   , id=__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple   , __magic_name__						:     Union[str, Any]						)				->  List[str]:
						# If no tests are collected, pytest exists with code 5, which makes the CI fail.
						if exitstatus == 5:
												lowercase :							Optional[int]    		=0


# Doctest custom flag to ignore output.
UpperCamelCase_    						=       doctest.register_optionflag("""IGNORE_RESULT""")

UpperCamelCase_    						=       doctest.OutputChecker





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):







				def  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										if IGNORE_RESULT & optionflags:
																return True
										return OutputChecker.check_output(self		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )



UpperCamelCase_    						=       CustomOutputChecker
UpperCamelCase_    						=       HfDoctestModule







UpperCamelCase_    						=       HfDocTestParser





