





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     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


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





					"""simple docstring"""







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


			def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       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     SCREAMING_SNAKE_CASE       (__lowerCAmelCase      ,     unittest.TestCase				):
			_UpperCamelCase   :		Tuple			 =	DonutImageProcessor if is_vision_available() else None


			def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       int:





					"""simple docstring"""







					lowercase__           =  DonutImageProcessingTester(self	)


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Optional[int]:





					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Optional[Any]:





					"""simple docstring"""







					lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Tuple:





					"""simple docstring"""







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

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


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       str:





					"""simple docstring"""







					pass


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





					"""simple docstring"""







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

					# Test not batched input
					lowercase__           =  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
					lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       List[str]:





					"""simple docstring"""







					lowercase__           =  self.image_processing_class(**self.image_processor_dict	)
					# create random numpy tensors
					lowercase__           =  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
					lowercase__           =  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
					lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Tuple:





					"""simple docstring"""







					lowercase__           =  self.image_processing_class(**self.image_processor_dict	)
					# create random PyTorch tensors
					lowercase__           =  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
					lowercase__           =  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
					lowercase__           =  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'],
					    )      ,					)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	field(default='language-modeling'      ,     metadata={'include_in_asdict_even_if_is_default': True}				)

   _UpperCamelCase   :		ClassVar[Features]			 =	Features({'text': Value('string'				)}				)
   _UpperCamelCase   :		ClassVar[Features]			 =	Features({}				)
   _UpperCamelCase   :		str			 =	"text"


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, str]:





     """simple docstring"""







     return {self.text_column: "text"}


