





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 ConvNextImageProcessor

class 		__a							(	unittest.TestCase      ):



       def __init__(     self	:   Dict					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Any=7					,			UpperCAmelCase	:   List[Any]=3					,			UpperCAmelCase	:   Optional[int]=18					,			UpperCAmelCase	:   Optional[int]=30					,			UpperCAmelCase	:   Tuple=4_00					,			UpperCAmelCase	:   List[str]=True					,			UpperCAmelCase	:   Dict=None					,			UpperCAmelCase	:   Optional[int]=0.875					,			UpperCAmelCase	:   str=True					,			UpperCAmelCase	:   Union[str, Any]=[0.5, 0.5, 0.5]					,			UpperCAmelCase	:   str=[0.5, 0.5, 0.5]					,			):
           lowerCAmelCase_					:	Tuple						=   size if size is not None else {"shortest_edge": 20}
           lowerCAmelCase_					:	Union[str, Any]						=   parent
           lowerCAmelCase_					:	Optional[int]						=   batch_size
           lowerCAmelCase_					:	Optional[Any]						=   num_channels
           lowerCAmelCase_					:	List[str]						=   image_size
           lowerCAmelCase_					:	Optional[Any]						=   min_resolution
           lowerCAmelCase_					:	Tuple						=   max_resolution
           lowerCAmelCase_					:	Dict						=   do_resize
           lowerCAmelCase_					:	Tuple						=   size
           lowerCAmelCase_					:	Any						=   crop_pct
           lowerCAmelCase_					:	Optional[int]						=   do_normalize
           lowerCAmelCase_					:	List[Any]						=   image_mean
           lowerCAmelCase_					:	List[Any]						=   image_std



       def  A							(     self	:   List[str]			):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "crop_pct": self.crop_pct,
           }






@require_torch
@require_vision
class 		__a							(	a__			,unittest.TestCase      ):
       __snake_case						:   Optional[Any]      =		ConvNextImageProcessor if is_vision_available() else None



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	List[str]						=   ConvNextImageProcessingTester(self			)



       @property
       def  A							(     self	:   Dict			):
           return self.image_processor_tester.prepare_image_processor_dict()



       def  A							(     self	:   Tuple			):
           lowerCAmelCase_					:	str						=   self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(__A					,			"""do_resize"""			)			)
           self.assertTrue(hasattr(__A					,			"""size"""			)			)
           self.assertTrue(hasattr(__A					,			"""crop_pct"""			)			)
           self.assertTrue(hasattr(__A					,			"""do_normalize"""			)			)
           self.assertTrue(hasattr(__A					,			"""image_mean"""			)			)
           self.assertTrue(hasattr(__A					,			"""image_std"""			)			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	Optional[Any]						=   self.image_processing_class.from_dict(self.image_processor_dict			)
           self.assertEqual(image_processor.size					,			{"""shortest_edge""": 20}			)

           lowerCAmelCase_					:	List[Any]						=   self.image_processing_class.from_dict(self.image_processor_dict					,			size=42			)
           self.assertEqual(image_processor.size					,			{"""shortest_edge""": 42}			)



       def  A							(     self	:   str			):
           pass



       def  A							(     self	:   Optional[int]			):
           lowerCAmelCase_					:	Tuple						=   self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           lowerCAmelCase_					:	Optional[int]						=   prepare_image_inputs(self.image_processor_tester					,			equal_resolution=__A			)
           for image in image_inputs:
               self.assertIsInstance(__A					,			Image.Image			)

           # Test not batched input
           lowerCAmelCase_					:	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.size["""shortest_edge"""],
                   self.image_processor_tester.size["""shortest_edge"""],
               )					,			)

           # Test batched
           lowerCAmelCase_					:	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.size["""shortest_edge"""],
                   self.image_processor_tester.size["""shortest_edge"""],
               )					,			)



       def  A							(     self	:   Dict			):
           lowerCAmelCase_					:	str						=   self.image_processing_class(**self.image_processor_dict			)
           # create random numpy tensors
           lowerCAmelCase_					:	Union[str, Any]						=   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
           lowerCAmelCase_					:	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.size["""shortest_edge"""],
                   self.image_processor_tester.size["""shortest_edge"""],
               )					,			)

           # Test batched
           lowerCAmelCase_					:	Union[str, 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.size["""shortest_edge"""],
                   self.image_processor_tester.size["""shortest_edge"""],
               )					,			)



       def  A							(     self	:   Tuple			):
           lowerCAmelCase_					:	Optional[int]						=   self.image_processing_class(**self.image_processor_dict			)
           # create random PyTorch tensors
           lowerCAmelCase_					:	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
           lowerCAmelCase_					:	Optional[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.size["""shortest_edge"""],
                   self.image_processor_tester.size["""shortest_edge"""],
               )					,			)

           # Test batched
           lowerCAmelCase_					:	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.size["""shortest_edge"""],
                   self.image_processor_tester.size["""shortest_edge"""],
               )					,			)









from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


__UpperCAmelCase						=      AttentionProcessor

class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[Any]					,			*UpperCAmelCase	:   List[Any]					,			**UpperCAmelCase	:   Any			):
           lowerCAmelCase_					:	str						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[Any]					,			*UpperCAmelCase	:   List[Any]					,			**UpperCAmelCase	:   List[str]			):
           lowerCAmelCase_					:	List[Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[str]					,			*UpperCAmelCase	:   str					,			**UpperCAmelCase	:   Union[str, Any]			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[int]					,			*UpperCAmelCase	:   Union[str, Any]					,			**UpperCAmelCase	:   Any			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   str					,			*UpperCAmelCase	:   Any					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[str]					,			*UpperCAmelCase	:   Union[str, Any]					,			**UpperCAmelCase	:   Tuple			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Dict					,			*UpperCAmelCase	:   int					,			**UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	List[str]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[Any]					,			*UpperCAmelCase	:   Any					,			**UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	Union[str, Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)


