






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 MobileNetVaImageProcessor




class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







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





       def      _a       (   self				):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						Any										=MobileNetVaImageProcessor if is_vision_available() else None





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							MobileNetVaImageProcessingTester(self				)





       @property
       def      _a       (   self				):
         return self.image_processor_tester.prepare_image_processor_dict()





       def      _a       (   self				):
         UpperCamelCase_:   Tuple        =							self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'do_resize'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'size'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'do_center_crop'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'crop_size'				)				)





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							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}				)

         UpperCamelCase_:   List[Any]        =							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      _a       (   self				):
         pass





       def      _a       (   self				):
         # Initialize image_processing
         UpperCamelCase_:   Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCamelCase_:   Tuple        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     Image.Image				)

         # Test not batched input
         UpperCamelCase_:   List[str]        =							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
         UpperCamelCase_:   Tuple        =							image_processing(_lowerCamelCase    ,     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				):
         # Initialize image_processing
         UpperCamelCase_:   Tuple        =							self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCamelCase_:   Tuple        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase    ,     numpify=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     np.ndarray				)

         # Test not batched input
         UpperCamelCase_:   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
         UpperCamelCase_:   Optional[Any]        =							image_processing(_lowerCamelCase    ,     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				):
         # Initialize image_processing
         UpperCamelCase_:   Any        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCamelCase_:   Any        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase    ,     torchify=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     torch.Tensor				)

         # Test not batched input
         UpperCamelCase_:   int        =							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
         UpperCamelCase_:   Dict        =							image_processing(_lowerCamelCase    ,     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'],
             )    ,     )

__lowerCamelCase				=			"""Tobias Carryer"""

from time import time






class     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]=int(time()					)					)						->      List[Any]:  # noqa: B008


          '''simple docstring'''


          snake_case			:  Dict     							= multiplier
          snake_case			:  Dict     							= increment
          snake_case			:  Union[str, Any]     							= modulo
          snake_case			:  Tuple     							= seed



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= (self.multiplier * self.seed + self.increment) % self.modulo
          return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   __lowerCamelCase				=			LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
   while True:
      print(lcg.next_number())





