






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 ViltImageProcessor
class 							A_			(  unittest.TestCase	):






 '''simple docstring'''




 def __init__(		self							,     _A							,     _A=7							,     _A=3							,     _A=18							,     _A=30							,     _A=400							,     _A=True							,     _A=None							,     _A=2							,     _A=True							,     _A=[0.5, 0.5, 0.5]							,     _A=[0.5, 0.5, 0.5]							,     )       ->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase :					Tuple	     = size if size is not None else {'shortest_edge': 30}
     _UpperCAmelCase :					Optional[Any]	     = parent
     _UpperCAmelCase :					Union[str, Any]	     = batch_size
     _UpperCAmelCase :					Optional[Any]	     = num_channels
     _UpperCAmelCase :					List[str]	     = image_size
     _UpperCAmelCase :					Optional[Any]	     = min_resolution
     _UpperCAmelCase :					Union[str, Any]	     = max_resolution
     _UpperCAmelCase :					Tuple	     = do_resize
     _UpperCAmelCase :					Optional[int]	     = size
     _UpperCAmelCase :					Any	     = size_divisor
     _UpperCAmelCase :					str	     = do_normalize
     _UpperCAmelCase :					Tuple	     = image_mean
     _UpperCAmelCase :					Dict	     = image_std


 def        snake_case__ (		self)       ->						Union[str, Any]:

     """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,
         "size_divisor": self.size_divisor,
     }


 def        snake_case__ (		self							,     _A							,     _A=False)       ->						List[Any]:

     """simple docstring"""



     if not batched:
         _UpperCAmelCase :					List[Any]	     = self.size['shortest_edge']
         _UpperCAmelCase :					Dict	     = image_inputs[0]
         if isinstance(_A							,     Image.Image):
             _UpperCAmelCase :					Union[str, Any]	     = image.size
         else:
             _UpperCAmelCase :					List[Any]	     = image.shape[1], image.shape[2]
         _UpperCAmelCase :					Optional[int]	     = size / min(_A							,     _A)
         if h < w:
             _UpperCAmelCase :					Union[str, Any]	     = size, scale * w
         else:
             _UpperCAmelCase :					int	     = scale * h, size

         _UpperCAmelCase :					Optional[int]	     = int((1333 / 800) * size)
         if max(_A							,     _A) > max_size:
             _UpperCAmelCase :					Dict	     = max_size / max(_A							,     _A)
             _UpperCAmelCase :					List[Any]	     = newh * scale
             _UpperCAmelCase :					List[Any]	     = neww * scale

         _UpperCAmelCase :					Optional[int]	     = int(newh + 0.5), int(neww + 0.5)
         _UpperCAmelCase :					Optional[Any]	     = (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

     else:
         _UpperCAmelCase :					Optional[Any]	     = []
         for image in image_inputs:
             _UpperCAmelCase :					List[str]	     = self.get_expected_values([image])
             expected_values.append((expected_height, expected_width))
         _UpperCAmelCase :					Union[str, Any]	     = max(_A							,     key=lambda _A: item[0])[0]
         _UpperCAmelCase :					List[str]	     = max(_A							,     key=lambda _A: item[1])[1]

     return expected_height, expected_width



@require_torch
@require_vision
class 							A_			(  __lowercase       ,      unittest.TestCase	):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:	Any       =    ViltImageProcessor if is_vision_available() else None


 def        snake_case__ (		self)       ->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase :					Union[str, Any]	     = ViltImageProcessingTester(self)


 @property
 def        snake_case__ (		self)       ->						Union[str, Any]:

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


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

     """simple docstring"""



     _UpperCAmelCase :					Union[str, Any]	     = self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(_A							,     '''image_mean'''))
     self.assertTrue(hasattr(_A							,     '''image_std'''))
     self.assertTrue(hasattr(_A							,     '''do_normalize'''))
     self.assertTrue(hasattr(_A							,     '''do_resize'''))
     self.assertTrue(hasattr(_A							,     '''size'''))
     self.assertTrue(hasattr(_A							,     '''size_divisor'''))


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

     """simple docstring"""



     _UpperCAmelCase :					Dict	     = self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size							,     {'''shortest_edge''': 30})

     _UpperCAmelCase :					Union[str, Any]	     = self.image_processing_class.from_dict(self.image_processor_dict							,     size=42)
     self.assertEqual(image_processor.size							,     {'''shortest_edge''': 42})


 def        snake_case__ (		self)       ->						Optional[int]:

     """simple docstring"""



     pass


 def        snake_case__ (		self)       ->						Dict:

     """simple docstring"""



     _UpperCAmelCase :					Optional[int]	     = self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     _UpperCAmelCase :					Dict	     = prepare_image_inputs(self.image_processor_tester							,     equal_resolution=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,     Image.Image)

     # Test not batched input
     _UpperCAmelCase :					int	     = image_processing(image_inputs[0]							,     return_tensors='''pt''').pixel_values

     _UpperCAmelCase :					int	     = self.image_processor_tester.get_expected_values(_A)
     self.assertEqual(
         encoded_images.shape							,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,     )

     # Test batched
     _UpperCAmelCase :					List[Any]	     = image_processing(_A							,     return_tensors='''pt''').pixel_values

     _UpperCAmelCase :					Optional[int]	     = self.image_processor_tester.get_expected_values(_A							,     batched=_A)
     self.assertEqual(
         encoded_images.shape							,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,     )


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

     """simple docstring"""



     _UpperCAmelCase :					Any	     = self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     _UpperCAmelCase :					List[str]	     = 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
     _UpperCAmelCase :					str	     = image_processing(image_inputs[0]							,     return_tensors='''pt''').pixel_values

     _UpperCAmelCase :					str	     = self.image_processor_tester.get_expected_values(_A)
     self.assertEqual(
         encoded_images.shape							,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,     )

     # Test batched
     _UpperCAmelCase :					List[str]	     = image_processing(_A							,     return_tensors='''pt''').pixel_values

     _UpperCAmelCase :					Optional[int]	     = self.image_processor_tester.get_expected_values(_A							,     batched=_A)
     self.assertEqual(
         encoded_images.shape							,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,     )


 def        snake_case__ (		self)       ->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase :					int	     = self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     _UpperCAmelCase :					List[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
     _UpperCAmelCase :					int	     = image_processing(image_inputs[0]							,     return_tensors='''pt''').pixel_values

     _UpperCAmelCase :					Optional[int]	     = self.image_processor_tester.get_expected_values(_A)
     self.assertEqual(
         encoded_images.shape							,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,     )

     # Test batched
     _UpperCAmelCase :					Union[str, Any]	     = image_processing(_A							,     return_tensors='''pt''').pixel_values

     _UpperCAmelCase :					List[Any]	     = self.image_processor_tester.get_expected_values(_A							,     batched=_A)
     self.assertEqual(
         encoded_images.shape							,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,     )












"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 						snake_case  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     IFImgaImgPipeline
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__         =     PipelineTesterMixin.required_optional_params - {'''latents'''}





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self._get_dummy_components()





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if str(A			).startswith('mps'			):
        a     :			Union[str, Any]				=   torch.manual_seed(A			)
      else:
        a     :			Optional[int]				=   torch.Generator(device=A			).manual_seed(A			)

      a     :			Any				=   floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(A			)			).to(A			)

      a     :			Dict				=   {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_save_load_optional_components()





    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,		reason='XFormers attention is only available with CUDA and `xformers` installed'				,		)
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_floataa_inference(expected_max_diff=1E-1			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      self._test_save_load_local()





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2				,		)




