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        UpperCamelCase						(  unittest.TestCase   ):




					def __init__(			self			:       Optional[int]   ,_lowerCAmelCase			:       Optional[Any]   ,_lowerCAmelCase			:       str=7   ,_lowerCAmelCase			:       List[str]=3   ,_lowerCAmelCase			:       Optional[int]=18   ,_lowerCAmelCase			:       Tuple=30   ,_lowerCAmelCase			:       Tuple=400   ,_lowerCAmelCase			:       str=True   ,_lowerCAmelCase			:       List[str]=None   ,_lowerCAmelCase			:       Tuple=True   ,_lowerCAmelCase			:       Union[str, Any]=False   ,_lowerCAmelCase			:       str=True   ,_lowerCAmelCase			:       Optional[Any]=True   ,_lowerCAmelCase			:       List[Any]=[0.5, 0.5, 0.5]   ,_lowerCAmelCase			:       Optional[int]=[0.5, 0.5, 0.5]   ,):


							"""simple docstring"""




							__snake_case									=  parent
							__snake_case									=  batch_size
							__snake_case									=  num_channels
							__snake_case									=  image_size
							__snake_case									=  min_resolution
							__snake_case									=  max_resolution
							__snake_case									=  do_resize
							__snake_case									=  size if size is not None else {'height': 18, 'width': 20}
							__snake_case									=  do_thumbnail
							__snake_case									=  do_align_axis
							__snake_case									=  do_pad
							__snake_case									=  do_normalize
							__snake_case									=  image_mean
							__snake_case									=  image_std







					def 				UpperCamelCase_    (			self			:       List[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        UpperCamelCase						(  SCREAMING_SNAKE_CASE_      ,   unittest.TestCase   ):
					__UpperCamelCase							= DonutImageProcessor if is_vision_available() else None




					def 				UpperCamelCase_    (			self			:       List[str]    ):


							"""simple docstring"""




							__snake_case									=  DonutImageProcessingTester(self    )




					@property
					def 				UpperCamelCase_    (			self			:       Tuple    ):


							"""simple docstring"""




							return self.image_processor_tester.prepare_image_processor_dict()




					def 				UpperCamelCase_    (			self			:       Optional[Any]    ):


							"""simple docstring"""




							__snake_case									=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"do_resize"    )    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"size"    )    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"do_thumbnail"    )    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"do_align_long_axis"    )    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"do_pad"    )    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"do_normalize"    )    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"image_mean"    )    )
							self.assertTrue(hasattr(_lowerCAmelCase   ,"image_std"    )    )




					def 				UpperCamelCase_    (			self			:       Dict    ):


							"""simple docstring"""




							__snake_case									=  self.image_processing_class.from_dict(self.image_processor_dict    )
							self.assertEqual(image_processor.size   ,{"height": 18, "width": 20}    )

							__snake_case									=  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
							__snake_case									=  self.image_processing_class.from_dict(self.image_processor_dict   ,size=(42, 84)    )
							self.assertEqual(image_processor.size   ,{"height": 84, "width": 42}    )




					def 				UpperCamelCase_    (			self			:       List[Any]    ):


							"""simple docstring"""




							pass




					@is_flaky()
					def 				UpperCamelCase_    (			self			:       List[Any]    ):


							"""simple docstring"""




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

							# Test not batched input
							__snake_case									=  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
							__snake_case									=  image_processing(_lowerCAmelCase   ,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 				UpperCamelCase_    (			self			:       List[Any]    ):


							"""simple docstring"""




							__snake_case									=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							__snake_case									=  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,numpify=_lowerCAmelCase    )
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase   ,np.ndarray    )

							# Test not batched input
							__snake_case									=  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
							__snake_case									=  image_processing(_lowerCAmelCase   ,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 				UpperCamelCase_    (			self			:       Union[str, Any]    ):


							"""simple docstring"""




							__snake_case									=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							__snake_case									=  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,torchify=_lowerCAmelCase    )
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase   ,torch.Tensor    )

							# Test not batched input
							__snake_case									=  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
							__snake_case									=  image_processing(_lowerCAmelCase   ,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"""







# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


a_							=    re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")







@total_ordering
@dataclass
class 					A_:







			"""simple docstring"""



			a_       :			str
			a_       :			Optional[str]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    _str_to_version_tuple(self.version_str							)
			def __repr__(	self							):
						return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
			@property
			def    _lowerCAmelCase		(	self							):
						return self.major, self.minor, self.patch
			def    _lowerCAmelCase		(	self				,   A							):
						if isinstance(A				,   A							):
									return Version(A							)
						elif isinstance(A				,   A							):
									return other
						raise TypeError(F"{other} (type {type(A							)}) cannot be compared to version."							)
			def __eq__(	self				,   A							):
						try:
									_lowerCamelCase					:					Dict	       =    self._validate_operand(A							)
						except (TypeError, ValueError):
									return False
						else:
									return self.tuple == other.tuple
			def __lt__(	self				,   A							):
						_lowerCamelCase					:					Any	       =    self._validate_operand(A							)
						return self.tuple < other.tuple
			def __hash__(	self							):
						return hash(_version_tuple_to_str(self.tuple							)							)
			@classmethod
			def    _lowerCAmelCase		(	cls				,   A							):
						_lowerCamelCase					:					Optional[Any]	       =    {f.name for f in dataclasses.fields(cls							)}
						return cls(**{k: v for k, v in dic.items() if k in field_names}							)

			def    _lowerCAmelCase		(	self							):
						return self.version_str






def     UpperCAmelCase_					(				__a   :			Any						):




			'''simple docstring'''



			_lowerCamelCase					:					Union[str, Any]	       =    _VERSION_REG.match(__a						)
			if not res:
						raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."						)
			return tuple(int(__a						) for v in [res.group('major'						), res.group('minor'						), res.group('patch'						)]						)



def     UpperCAmelCase_					(				__a   :			List[str]						):




			'''simple docstring'''



			return ".".join(str(__a						) for v in version_tuple						)





