
"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import 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 DeiTImageProcessor




class 					UpperCamelCase     (	unittest.TestCase				):

    """simple docstring"""



    def __init__(    self		,     lowercase__		,     lowercase__=7		,     lowercase__=3		,     lowercase__=18		,     lowercase__=30		,     lowercase__=400		,     lowercase__=True		,     lowercase__=None		,     lowercase__=True		,     lowercase__=None		,     lowercase__=True		,     lowercase__=[0.5, 0.5, 0.5]		,     lowercase__=[0.5, 0.5, 0.5]		,     )   ->   str:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     size if size is not None else {'height': 20, 'width': 20}
         SCREAMING_SNAKE_CASE           =     crop_size if crop_size is not None else {'height': 18, 'width': 18}

         SCREAMING_SNAKE_CASE           =     parent
         SCREAMING_SNAKE_CASE           =     batch_size
         SCREAMING_SNAKE_CASE           =     num_channels
         SCREAMING_SNAKE_CASE           =     image_size
         SCREAMING_SNAKE_CASE           =     min_resolution
         SCREAMING_SNAKE_CASE           =     max_resolution
         SCREAMING_SNAKE_CASE           =     do_resize
         SCREAMING_SNAKE_CASE           =     size
         SCREAMING_SNAKE_CASE           =     do_center_crop
         SCREAMING_SNAKE_CASE           =     crop_size
         SCREAMING_SNAKE_CASE           =     do_normalize
         SCREAMING_SNAKE_CASE           =     image_mean
         SCREAMING_SNAKE_CASE           =     image_std




    def 							A (    self      )   ->   Optional[Any]:
         """simple docstring"""







         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }






@require_torch
@require_vision
class 					UpperCamelCase     (	__magic_name__  ,				unittest.TestCase				):

    """simple docstring"""



    UpperCAmelCase_    :				Optional[Any]						  =    DeiTImageProcessor if is_vision_available() else None
    UpperCAmelCase_    :				Union[str, Any]						  =    True


    def 							A (    self      )   ->   List[Any]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     DeiTImageProcessingTester(self      )


    @property
    def 							A (    self      )   ->   List[Any]:
         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()


    def 							A (    self      )   ->   List[str]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(lowercase__		,     'do_resize'      )      )
         self.assertTrue(hasattr(lowercase__		,     'size'      )      )
         self.assertTrue(hasattr(lowercase__		,     'do_center_crop'      )      )
         self.assertTrue(hasattr(lowercase__		,     'center_crop'      )      )
         self.assertTrue(hasattr(lowercase__		,     'do_normalize'      )      )
         self.assertTrue(hasattr(lowercase__		,     'image_mean'      )      )
         self.assertTrue(hasattr(lowercase__		,     'image_std'      )      )


    def 							A (    self      )   ->   int:
         """simple docstring"""







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

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


    def 							A (    self      )   ->   Optional[Any]:
         """simple docstring"""







         pass


    def 							A (    self      )   ->   Optional[int]:
         """simple docstring"""







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

         # Test not batched input
         SCREAMING_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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )		,     )

         # Test batched
         SCREAMING_SNAKE_CASE           =     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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )		,     )


    def 							A (    self      )   ->   int:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         SCREAMING_SNAKE_CASE           =     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
         SCREAMING_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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )		,     )

         # Test batched
         SCREAMING_SNAKE_CASE           =     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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )		,     )




    def 							A (    self      )   ->   Optional[int]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE           =     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
         SCREAMING_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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )		,     )

         # Test batched
         SCREAMING_SNAKE_CASE           =     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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )		,     )


"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
