





import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class     A     (_UpperCamelCase  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			str							     =       PriorTransformer
			__lowerCamelCase      :			str							     =       '''hidden_states'''






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







					"""simple docstring"""






					A__     =  4
					A__     =  8
					A__     =  7

					A__     =  floats_tensor((batch_size, embedding_dim)  ).to(lowerCamelCase_  )

					A__     =  floats_tensor((batch_size, embedding_dim)  ).to(lowerCamelCase_  )
					A__     =  floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(lowerCamelCase_  )

					return {
					    "hidden_states": hidden_states,
					    "timestep": 2,
					    "proj_embedding": proj_embedding,
					    "encoder_hidden_states": encoder_hidden_states,
					}






			def   a_			(     self  :       int				,      __lowerCAmelCase  :       Dict=0  )      ->      int:







					"""simple docstring"""






					torch.manual_seed(lowerCamelCase_  )
					A__     =  4
					A__     =  8
					A__     =  7

					A__     =  torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase_  )

					A__     =  torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase_  )
					A__     =  torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(lowerCamelCase_  )

					return {
					    "hidden_states": hidden_states,
					    "timestep": 2,
					    "proj_embedding": proj_embedding,
					    "encoder_hidden_states": encoder_hidden_states,
					}






			@property
			def   a_			(     self  :       str  )      ->      Optional[int]:







					"""simple docstring"""






					return (4, 8)






			@property
			def   a_			(     self  :       List[str]  )      ->      List[str]:







					"""simple docstring"""






					return (4, 8)






			def   a_			(     self  :       Dict  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  {
					    'num_attention_heads': 2,
					    'attention_head_dim': 4,
					    'num_layers': 2,
					    'embedding_dim': 8,
					    'num_embeddings': 7,
					    'additional_embeddings': 4,
					}
					A__     =  self.dummy_input
					return init_dict, inputs_dict






			def   a_			(     self  :       int  )      ->      int:







					"""simple docstring"""






					A__     =  PriorTransformer.from_pretrained(
					    """hf-internal-testing/prior-dummy"""				,      output_loading_info=lowerCamelCase_  )
					self.assertIsNotNone(lowerCamelCase_  )
					self.assertEqual(len(loading_info["""missing_keys"""]  )				,      0  )

					model.to(lowerCamelCase_  )
					A__     =  model(**self.dummy_input  )[0]

					assert hidden_states is not None, "Make sure output is not None"






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







					"""simple docstring"""






					A__     =  self.prepare_init_args_and_inputs_for_common()

					A__     =  self.model_class(**lowerCamelCase_  )
					A__     =  inspect.signature(model.forward  )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					A__     =  [*signature.parameters.keys()]

					A__     =  ['hidden_states', 'timestep']
					self.assertListEqual(arg_names[:2]				,      lowerCamelCase_  )







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







					"""simple docstring"""






					A__     =  PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""  )
					A__     =  model.to(lowerCamelCase_  )

					if hasattr(lowerCamelCase_				,      """set_default_attn_processor"""  ):
							model.set_default_attn_processor()

					A__     =  self.get_dummy_seed_input()

					with torch.no_grad():
							A__     =  model(**lowerCamelCase_  )[0]

					A__     =  output[0, :5].flatten().cpu()
					print(lowerCamelCase_  )

					# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
					# the expected output slices are not the same for CPU and GPU.
					A__     =  torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]  )
					self.assertTrue(torch_all_close(lowerCamelCase_				,      lowerCamelCase_				,      rtol=1e-2  )  )


@slow
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			def   a_			(     self  :       Tuple				,      __lowerCAmelCase  :       Union[str, Any]=1				,      __lowerCAmelCase  :       Tuple=7_68				,      __lowerCAmelCase  :       Dict=77				,      __lowerCAmelCase  :       Union[str, Any]=0  )      ->      Union[str, Any]:







					"""simple docstring"""






					torch.manual_seed(lowerCamelCase_  )
					A__     =  batch_size
					A__     =  embedding_dim
					A__     =  num_embeddings

					A__     =  torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase_  )

					A__     =  torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase_  )
					A__     =  torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(lowerCamelCase_  )

					return {
					    "hidden_states": hidden_states,
					    "timestep": 2,
					    "proj_embedding": proj_embedding,
					    "encoder_hidden_states": encoder_hidden_states,
					}






			def   a_			(     self  :       str  )      ->      Dict:







					"""simple docstring"""






					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
			        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
			        # fmt: on
			    ]  )
			def   a_			(     self  :       Optional[Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       str  )      ->      List[str]:







					"""simple docstring"""






					A__     =  PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,      subfolder="""prior"""  )
					model.to(lowerCamelCase_  )
					A__     =  self.get_dummy_seed_input(seed=lowerCamelCase_  )

					with torch.no_grad():
							A__     =  model(**lowerCamelCase_  )[0]

					assert list(sample.shape  ) == [1, 7_68]

					A__     =  sample[0, :8].flatten().cpu()
					print(lowerCamelCase_  )
					A__     =  torch.tensor(lowerCamelCase_  )

					assert torch_all_close(lowerCamelCase_				,      lowerCamelCase_				,      atol=1e-3  )










import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						int			):
			if dst_width < 0 or dst_height < 0:
					raise ValueError('Destination width/height should be > 0'			)

			lowercase__					:      str      =		img
			lowercase__					:      Tuple      =		img.shape[1]
			lowercase__					:      str      =		img.shape[0]
			lowercase__					:      Tuple      =		dst_width
			lowercase__					:      Any      =		dst_height

			lowercase__					:      Tuple      =		self.src_w / self.dst_w
			lowercase__					:      Union[str, Any]      =		self.src_h / self.dst_h

			lowercase__					:      List[Any]      =		(
			    np.ones((self.dst_h, self.dst_w, 3),					np.uinta			) * 255
			)

	def    snake_case__(						self:						Dict			):
			for i in range(self.dst_h			):
					for j in range(self.dst_w			):
							lowercase__					:      Tuple      =		self.img[self.get_y(lowerCamelCase_			)][self.get_x(lowerCamelCase_			)]

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						int			):
			return int(self.ratio_x * x			)





	def    snake_case__(						self:						int,					lowerCamelCase_:						int			):
			return int(self.ratio_y * y			)


if __name__ == "__main__":
			__UpperCamelCase,	__UpperCamelCase:	List[Any] = 8_0_0, 6_0_0
			__UpperCamelCase:	Optional[Any] = imread("""image_data/lena.jpg""", 1)
			__UpperCamelCase:	Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
			)
			waitKey(0)
			destroyAllWindows()


