

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DonutImageProcessor







class       snake_case__  ( unittest.TestCase     ):







			def __init__(			self			: Dict			,  lowercase			: int			,  lowercase			: Union[str, Any]=7			,  lowercase			: Union[str, Any]=3			,  lowercase			: Any=18			,  lowercase			: Union[str, Any]=30			,  lowercase			: Any=4_00			,  lowercase			: List[str]=True			,  lowercase			: Dict=None			,  lowercase			: List[str]=True			,  lowercase			: int=False			,  lowercase			: Union[str, Any]=True			,  lowercase			: str=True			,  lowercase			: Optional[int]=[0.5, 0.5, 0.5]			,  lowercase			: List[Any]=[0.5, 0.5, 0.5]			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				parent
								UpperCAmelCase			:   Union[str, Any]      =				batch_size
								UpperCAmelCase			:   int      =				num_channels
								UpperCAmelCase			:   List[str]      =				image_size
								UpperCAmelCase			:   Any      =				min_resolution
								UpperCAmelCase			:   Optional[Any]      =				max_resolution
								UpperCAmelCase			:   Union[str, Any]      =				do_resize
								UpperCAmelCase			:   str      =				size if size is not None else {"height": 18, "width": 20}
								UpperCAmelCase			:   Any      =				do_thumbnail
								UpperCAmelCase			:   Optional[int]      =				do_align_axis
								UpperCAmelCase			:   List[Any]      =				do_pad
								UpperCAmelCase			:   Optional[int]      =				do_normalize
								UpperCAmelCase			:   List[Any]      =				image_mean
								UpperCAmelCase			:   List[str]      =				image_std







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}







@require_torch
@require_vision
class       snake_case__  ( lowercase__					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						DonutImageProcessor if is_vision_available() else None







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				DonutImageProcessingTester(self  )







			@property
			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								return self.image_processor_tester.prepare_image_processor_dict()







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(__lowercase			,  "do_resize"  )  )
								self.assertTrue(hasattr(__lowercase			,  "size"  )  )
								self.assertTrue(hasattr(__lowercase			,  "do_thumbnail"  )  )
								self.assertTrue(hasattr(__lowercase			,  "do_align_long_axis"  )  )
								self.assertTrue(hasattr(__lowercase			,  "do_pad"  )  )
								self.assertTrue(hasattr(__lowercase			,  "do_normalize"  )  )
								self.assertTrue(hasattr(__lowercase			,  "image_mean"  )  )
								self.assertTrue(hasattr(__lowercase			,  "image_std"  )  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size			,  {"height": 18, "width": 20}  )

								UpperCAmelCase			:   Optional[int]      =				self.image_processing_class.from_dict(self.image_processor_dict			,  size=42  )
								self.assertEqual(image_processor.size			,  {"height": 42, "width": 42}  )

								# Previous config had dimensions in (width, height) order
								UpperCAmelCase			:   Tuple      =				self.image_processing_class.from_dict(self.image_processor_dict			,  size=(42, 84)  )
								self.assertEqual(image_processor.size			,  {"height": 84, "width": 42}  )







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								pass







			@is_flaky()
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								UpperCAmelCase			:   Dict      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__lowercase  )
								for image in image_inputs:
													self.assertIsInstance(__lowercase			,  Image.Image  )

								# Test not batched input
								UpperCAmelCase			:   Union[str, Any]      =				image_processing(image_inputs[0]			,  return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )

								# Test batched
								UpperCAmelCase			:   List[Any]      =				image_processing(__lowercase			,  return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )







			@is_flaky()
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								UpperCAmelCase			:   List[str]      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__lowercase			,  numpify=__lowercase  )
								for image in image_inputs:
													self.assertIsInstance(__lowercase			,  np.ndarray  )

								# Test not batched input
								UpperCAmelCase			:   Tuple      =				image_processing(image_inputs[0]			,  return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )

								# Test batched
								UpperCAmelCase			:   List[Any]      =				image_processing(__lowercase			,  return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )







			@is_flaky()
			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								UpperCAmelCase			:   Dict      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__lowercase			,  torchify=__lowercase  )
								for image in image_inputs:
													self.assertIsInstance(__lowercase			,  torch.Tensor  )

								# Test not batched input
								UpperCAmelCase			:   Dict      =				image_processing(image_inputs[0]			,  return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )

								# Test batched
								UpperCAmelCase			:   List[str]      =				image_processing(__lowercase			,  return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )





"""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


