
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    :		Optional[Any]     						=  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     ,		__lowerCAmelCase				):
    return round(float(moles / volume				) * nfactor				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (volume)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((moles * 0.0_821 * temperature) / (pressure)				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    return round(float((pressure * volume) / (0.0_821 * moles)				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



