"""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 ViTImageProcessor






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=18 ,				_SCREAMING_SNAKE_CASE=30 ,				_SCREAMING_SNAKE_CASE=400 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					size if size is not None else {'height': 18, 'width': 18}
      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					min_resolution
      __lowerCamelCase    		=					max_resolution
      __lowerCamelCase    		=					do_resize
      __lowerCamelCase    		=					size
      __lowerCamelCase    		=					do_normalize
      __lowerCamelCase    		=					image_mean
      __lowerCamelCase    		=					image_std







   def    _A				(	self	):


      '''simple docstring'''

      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				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    ViTImageProcessor if is_vision_available() else None







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ViTImageProcessingTester(self	)







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'image_mean'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'image_std'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'do_normalize'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'do_resize'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'size'	)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size ,				{'height': 18, 'width': 18}	)

      __lowerCamelCase    		=					self.image_processing_class.from_dict(self.image_processor_dict ,				size=42	)
      self.assertEqual(image_processor.size ,				{'height': 42, 'width': 42}	)







   def    _A				(	self	):


      '''simple docstring'''

      pass







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      __lowerCamelCase    		=					prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				Image.Image	)

      # Test not batched input
      __lowerCamelCase    		=					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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      __lowerCamelCase    		=					prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_SCREAMING_SNAKE_CASE ,				numpify=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				np.ndarray	)

      # Test not batched input
      __lowerCamelCase    		=					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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      __lowerCamelCase    		=					prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_SCREAMING_SNAKE_CASE ,				torchify=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	)

      # Test not batched input
      __lowerCamelCase    		=					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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				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.size['height'],
              self.image_processor_tester.size['width'],
          ) ,				)










from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase									=				{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
 from .tokenization_phobert import PhobertTokenizer

else:
 import sys

 __lowerCAmelCase									=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


