






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 ViTImageProcessor


class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def __init__(					self							:						int       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Union[str, Any]=13       ,			UpperCAmelCase_							:						Tuple=3       ,			UpperCAmelCase_							:						Dict=224       ,			UpperCAmelCase_							:						Tuple=30       ,			UpperCAmelCase_							:						Tuple=400       ,			UpperCAmelCase_							:						Union[str, Any]=True       ,			UpperCAmelCase_							:						Dict=None       ,			UpperCAmelCase_							:						Union[str, Any]=True       ,			UpperCAmelCase_							:						Union[str, Any]=[0.5, 0.5, 0.5]       ,			UpperCAmelCase_							:						str=[0.5, 0.5, 0.5]       ,			):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     size if size is not None else {"height": 18, "width": 18}
       SCREAMING_SNAKE_CASE							:   str						    =     parent
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     batch_size
       SCREAMING_SNAKE_CASE							:   int						    =     num_channels
       SCREAMING_SNAKE_CASE							:   str						    =     image_size
       SCREAMING_SNAKE_CASE							:   Tuple						    =     min_resolution
       SCREAMING_SNAKE_CASE							:   int						    =     max_resolution
       SCREAMING_SNAKE_CASE							:   Tuple						    =     do_resize
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     size
       SCREAMING_SNAKE_CASE							:   str						    =     do_normalize
       SCREAMING_SNAKE_CASE							:   List[str]						    =     image_mean
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     image_std






     def 					_A							(					self							:						List[str]   ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }





@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Any								=						ViTImageProcessor if is_vision_available() else None


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     EfficientFormerImageProcessorTester(self   )


     @property
     def 					_A							(					self							:						Optional[Any]   ):
       return self.image_proc_tester.prepare_image_processor_dict()


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(UpperCAmelCase_       ,			"image_mean"   )   )
       self.assertTrue(hasattr(UpperCAmelCase_       ,			"image_std"   )   )
       self.assertTrue(hasattr(UpperCAmelCase_       ,			"do_normalize"   )   )
       self.assertTrue(hasattr(UpperCAmelCase_       ,			"do_resize"   )   )
       self.assertTrue(hasattr(UpperCAmelCase_       ,			"size"   )   )


     def 					_A							(					self							:						Optional[Any]   ):
       pass


     def 					_A							(					self							:						List[str]   ):
       # Initialize image_processor
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     prepare_image_inputs(self.image_proc_tester       ,			equal_resolution=UpperCAmelCase_   )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_       ,			Image.Image   )

       # Test not batched input
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     image_processor(image_inputs[0]       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,			)

       # Test batched
       SCREAMING_SNAKE_CASE							:   List[str]						    =     image_processor(UpperCAmelCase_       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,			)


     def 					_A							(					self							:						Dict   ):
       # Initialize image_processor
       SCREAMING_SNAKE_CASE							:   Any						    =     self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE							:   Dict						    =     prepare_image_inputs(self.image_proc_tester       ,			equal_resolution=UpperCAmelCase_       ,			numpify=UpperCAmelCase_   )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_       ,			np.ndarray   )

       # Test not batched input
       SCREAMING_SNAKE_CASE							:   Any						    =     image_processor(image_inputs[0]       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,			)

       # Test batched
       SCREAMING_SNAKE_CASE							:   Tuple						    =     image_processor(UpperCAmelCase_       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,			)






     def 					_A							(					self							:						Tuple   ):
       # Initialize image_processor
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     prepare_image_inputs(self.image_proc_tester       ,			equal_resolution=UpperCAmelCase_       ,			torchify=UpperCAmelCase_   )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_       ,			torch.Tensor   )

       # Test not batched input
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     image_processor(image_inputs[0]       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,			)

       # Test batched
       SCREAMING_SNAKE_CASE							:   List[str]						    =     image_processor(UpperCAmelCase_       ,			return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,			(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,			)














from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						@staticmethod
						@abstractmethod
						def        UpperCAmelCase__					(__UpperCAmelCase:					ArgumentParser   )    ->     Tuple:




											'''simple docstring'''



											raise NotImplementedError()



						@abstractmethod
						def        UpperCAmelCase__					(self:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											raise NotImplementedError()





