
"""simple docstring"""




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     lowercase (		unittest.TestCase       ):



		def __init__(			self    ,				A_    ,				A_=7    ,				A_=3    ,				A_=18    ,				A_=30    ,				A_=400    ,				A_=True    ,				A_=None    ,				A_=True    ,				A_=False    ,				A_=True    ,				A_=True    ,				A_=[0.5, 0.5, 0.5]    ,				A_=[0.5, 0.5, 0.5]    ,				)  -> Dict:
				"""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 __UpperCamelCase			(			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     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    DonutImageProcessor if is_vision_available() else None



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					DonutImageProcessingTester(self	)



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'do_resize'	)	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'size'	)	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'do_thumbnail'	)	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'do_align_long_axis'	)	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'do_pad'	)	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'do_normalize'	)	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'image_mean'	)	)
				self.assertTrue(hasattr(__UpperCAmelCase    ,				'image_std'	)	)



		def __UpperCamelCase			(			self	)  -> str:
				"""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 __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				pass



		@is_flaky()
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""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=__UpperCAmelCase	)
				for image in image_inputs:
						self.assertIsInstance(__UpperCAmelCase    ,				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(__UpperCAmelCase    ,				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	)  -> str:
				"""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=__UpperCAmelCase    ,				numpify=__UpperCAmelCase	)
				for image in image_inputs:
						self.assertIsInstance(__UpperCAmelCase    ,				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(__UpperCAmelCase    ,				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	)  -> str:
				"""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=__UpperCAmelCase    ,				torchify=__UpperCAmelCase	)
				for image in image_inputs:
						self.assertIsInstance(__UpperCAmelCase    ,				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(__UpperCAmelCase    ,				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 Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE       ):



		@register_to_config
		def __init__(			self    ,				A_ = 768    ,				)  -> List[Any]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase          =					nn.Parameter(torch.zeros(1    ,				A_	)	)
				UpperCamelCase          =					nn.Parameter(torch.ones(1    ,				A_	)	)



		def __UpperCamelCase			(			self    ,				A_ = None    ,				A_ = None    ,				)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					nn.Parameter(self.mean.to(A_	).to(A_	)	)
				UpperCamelCase          =					nn.Parameter(self.std.to(A_	).to(A_	)	)
				return self



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds - self.mean) * 1.0 / self.std
				return embeds



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds * self.std) + self.mean
				return embeds
