

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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BlipImageProcessor







class 		__magic_name__    (							unittest.TestCase							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_=7,					lowercase_=3,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=False,					lowercase_=[0.48145466, 0.4578275, 0.40821073],					lowercase_=[0.26862954, 0.26130258, 0.27577711],					lowercase_=True,					)							->					List[Any]:






     """simple docstring"""






     a__						=size if size is not None else {"height": 20, "width": 20}
     a__						=parent
     a__						=batch_size
     a__						=num_channels
     a__						=image_size
     a__						=min_resolution
     a__						=max_resolution
     a__						=do_resize
     a__						=size
     a__						=do_normalize
     a__						=image_mean
     a__						=image_std
     a__						=do_pad
     a__						=do_convert_rgb



   def 				_UpperCAmelCase				(       self )							->					str:






     """simple docstring"""






     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
         "do_pad": self.do_pad,
     }



   def 				_UpperCAmelCase				(       self,					lowercase_=False,					lowercase_=False,					lowercase_=False )							->					Optional[int]:






     """simple docstring"""






     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       a__						=[]
       for i in range(self.batch_size ):
         image_inputs.append(
             np.random.randint(
                 255,					size=(self.num_channels, self.max_resolution, self.max_resolution),					dtype=np.uinta ) )
     else:
       a__						=[]
       for i in range(self.batch_size ):
         a__						=np.random.choice(np.arange(self.min_resolution,					self.max_resolution ),					2 )
         image_inputs.append(np.random.randint(255,					size=(self.num_channels, width, height),					dtype=np.uinta ) )

     if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       a__						=[Image.fromarray(np.moveaxis(lowercase_,					0,					-1 ) ) for x in image_inputs]

     if torchify:
       a__						=[torch.from_numpy(lowercase_ ) for x in image_inputs]

     return image_inputs







@require_torch
@require_vision
class 		__magic_name__    (							lowerCamelCase_	, unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__				:			Dict												=     BlipImageProcessor if is_vision_available() else None



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






     """simple docstring"""






     a__						=BlipImageProcessingTester(self )



   @property
   def 				_UpperCAmelCase				(       self )							->					Dict:






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



   def 				_UpperCAmelCase				(       self )							->					Any:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''size''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_convert_rgb''' ) )



   def 				_UpperCAmelCase				(       self )							->					List[str]:






     """simple docstring"""






     pass



   def 				_UpperCAmelCase				(       self )							->					Any:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     a__						=self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_ )
     for image in image_inputs:
       self.assertIsInstance(lowercase_,					Image.Image )

     # Test not batched input
     a__						=image_processor(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
     a__						=image_processor(lowercase_,					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 				_UpperCAmelCase				(       self )							->					str:






     """simple docstring"""






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

     # Test not batched input
     a__						=image_processor(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
     a__						=image_processor(lowercase_,					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 				_UpperCAmelCase				(       self )							->					Union[str, Any]:






     """simple docstring"""






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

     # Test not batched input
     a__						=image_processor(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
     a__						=image_processor(lowercase_,					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'''],
         ),					)







@require_torch
@require_vision
class 		__magic_name__    (							lowerCamelCase_	, unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__				:			Union[str, Any]												=     BlipImageProcessor if is_vision_available() else None



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






     """simple docstring"""






     a__						=BlipImageProcessingTester(self,					num_channels=4 )
     a__						=3



   @property
   def 				_UpperCAmelCase				(       self )							->					List[Any]:






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



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






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''size''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_convert_rgb''' ) )



   def 				_UpperCAmelCase				(       self )							->					int:






     """simple docstring"""






     pass



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






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     a__						=self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_ )
     for image in image_inputs:
       self.assertIsInstance(lowercase_,					Image.Image )

     # Test not batched input
     a__						=image_processor(image_inputs[0],					return_tensors='''pt''' ).pixel_values
     self.assertEqual(
         encoded_images.shape,					(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ),					)

     # Test batched
     a__						=image_processor(lowercase_,					return_tensors='''pt''' ).pixel_values
     self.assertEqual(
         encoded_images.shape,					(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ),					)



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
   lowerCamelCase__				:			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
       from .modeling_roberta_series import RobertaSeriesModelWithTransformation
       from .pipeline_alt_diffusion import AltDiffusionPipeline
       from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
