"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch





def        a_  (   _lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       for param in module.parameters():
              lowercase__    :	List[str]     					=			False





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              lowercase__    :	List[str]     					=			'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'  )
       return device





def        a_  (   _lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			plt.imshow(_lowerCAmelCase  )
       fig.axes.get_xaxis().set_visible(_lowerCAmelCase  )
       fig.axes.get_yaxis().set_visible(_lowerCAmelCase  )
       plt.show()






def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			datetime.now()
       lowercase__    :	List[Any]     					=			current_time.strftime('%H:%M:%S'  )
       return timestamp


"""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 MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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
             lowercase__    :	List[Any]     					=			image_processing(a	,    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 	_UpperCAmelCase (     self							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	List[Any]     					=			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
             lowercase__    :	Any     					=			image_processing(a	,    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 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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
             lowercase__    :	Tuple     					=			image_processing(a	,    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'],
                 )	,    )

