



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       __lowerCAmelCase   (      unittest.TestCase						):



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



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""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       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					DonutImageProcessor if is_vision_available() else None



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       DonutImageProcessingTester(self)



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   List[Any]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(A			,		'do_resize'))
						self.assertTrue(hasattr(A			,		'size'))
						self.assertTrue(hasattr(A			,		'do_thumbnail'))
						self.assertTrue(hasattr(A			,		'do_align_long_axis'))
						self.assertTrue(hasattr(A			,		'do_pad'))
						self.assertTrue(hasattr(A			,		'do_normalize'))
						self.assertTrue(hasattr(A			,		'image_mean'))
						self.assertTrue(hasattr(A			,		'image_std'))



			def    _lowerCamelCase	(						self       :  Dict)       ->   Tuple:



						"""simple docstring"""






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

						_UpperCAmelCase			=       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			=       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       :  Any)       ->   Union[str, Any]:



						"""simple docstring"""






						pass



			@is_flaky()
			def    _lowerCamelCase	(						self       :  int)       ->   Optional[int]:



						"""simple docstring"""






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

						# Test not batched input
						_UpperCAmelCase			=       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			=       image_processing(A			,		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       :  Union[str, Any])       ->   Optional[int]:



						"""simple docstring"""






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

						# Test not batched input
						_UpperCAmelCase			=       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			=       image_processing(A			,		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       :  int)       ->   Any:



						"""simple docstring"""






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

						# Test not batched input
						_UpperCAmelCase			=       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			=       image_processing(A			,		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'],
						    )			,		)





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase__       =	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
							from .tokenization_bertweet import BertweetTokenizer

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
