
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 ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=3		,	lowerCamelCase=2_24		,	lowerCamelCase=30		,	lowerCamelCase=4_00		,	lowerCamelCase=True		,	lowerCamelCase=None		,	lowerCamelCase=True		,	lowerCamelCase=[0.5, 0.5, 0.5]		,	lowerCamelCase=[0.5, 0.5, 0.5]		,	):
          snake_case__						=				size if size is not None else {"height": 18, "width": 18}
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				num_channels
          snake_case__						=				image_size
          snake_case__						=				min_resolution
          snake_case__						=				max_resolution
          snake_case__						=				do_resize
          snake_case__						=				size
          snake_case__						=				do_normalize
          snake_case__						=				image_mean
          snake_case__						=				image_std



      def A_			(			self ):
          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  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		List[str]    	=     ViTImageProcessor if is_vision_available() else None
      def A_			(			self ):
          snake_case__						=				EfficientFormerImageProcessorTester(self )
      @property
      def A_			(			self ):
          return self.image_proc_tester.prepare_image_processor_dict()
      def A_			(			self ):
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(lowerCamelCase		,	"image_mean" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"image_std" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_normalize" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_resize" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"size" ) )
      def A_			(			self ):
          pass
      def A_			(			self ):
          # Initialize image_processor
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          snake_case__						=				prepare_image_inputs(self.image_proc_tester		,	equal_resolution=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	Image.Image )

          # Test not batched input
          snake_case__						=				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
          snake_case__						=				image_processor(lowerCamelCase		,	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 ):
          # Initialize image_processor
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          snake_case__						=				prepare_image_inputs(self.image_proc_tester		,	equal_resolution=lowerCamelCase		,	numpify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	np.ndarray )

          # Test not batched input
          snake_case__						=				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
          snake_case__						=				image_processor(lowerCamelCase		,	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 ):
          # Initialize image_processor
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          snake_case__						=				prepare_image_inputs(self.image_proc_tester		,	equal_resolution=lowerCamelCase		,	torchify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	torch.Tensor )

          # Test not batched input
          snake_case__						=				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
          snake_case__						=				image_processor(lowerCamelCase		,	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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F'''{price_plus_tax(100, 0.2_5) = }''')
     print(F'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''')



