"""simple docstring"""





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  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						PriorTransformer
				a			=						"hidden_states"





				@property
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
											_SCREAMING_SNAKE_CASE		:						Any			  =    8
											_SCREAMING_SNAKE_CASE		:						Dict			  =    7

											_SCREAMING_SNAKE_CASE		:						str			  =    floats_tensor((batch_size, embedding_dim)).to(_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    floats_tensor((batch_size, embedding_dim)).to(_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    floats_tensor((batch_size, num_embeddings, embedding_dim)).to(_A)

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





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							Optional[int]=0):



											"""simple docstring"""







											torch.manual_seed(_A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    4
											_SCREAMING_SNAKE_CASE		:						str			  =    8
											_SCREAMING_SNAKE_CASE		:						Dict			  =    7

											_SCREAMING_SNAKE_CASE		:						Any			  =    torch.randn((batch_size, embedding_dim)).to(_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.randn((batch_size, embedding_dim)).to(_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.randn((batch_size, num_embeddings, embedding_dim)).to(_A)

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





				@property
				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											return (4, 8)





				@property
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											return (4, 8)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    {
											    """num_attention_heads""": 2,
											    """attention_head_dim""": 4,
											    """num_layers""": 2,
											    """embedding_dim""": 8,
											    """num_embeddings""": 7,
											    """additional_embeddings""": 4,
											}
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.dummy_input
											return init_dict, inputs_dict





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







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

											model.to(_A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(**self.dummy_input)[0]

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





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Dict			  =    self.prepare_init_args_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model_class(**_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    inspect.signature(model.forward)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_SCREAMING_SNAKE_CASE		:						int			  =    [*signature.parameters.keys()]

											_SCREAMING_SNAKE_CASE		:						Dict			  =    ["""hidden_states""", """timestep"""]
											self.assertListEqual(arg_names[:2]      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""")
											_SCREAMING_SNAKE_CASE		:						str			  =    model.to(_A)

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

											_SCREAMING_SNAKE_CASE		:						str			  =    self.get_dummy_seed_input()

											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						str			  =    model(**_A)[0]

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    output[0, :5].flatten().cpu()
											print(_A)

											# 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.
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239])
											self.assertTrue(torch_all_close(_A      ,    _A      ,    rtol=1e-2))




@slow
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Dict=1      ,    _A							:							List[str]=7_6_8      ,    _A							:							List[Any]=7_7      ,    _A							:							List[Any]=0):



											"""simple docstring"""







											torch.manual_seed(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    embedding_dim
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    num_embeddings

											_SCREAMING_SNAKE_CASE		:						int			  =    torch.randn((batch_size, embedding_dim)).to(_A)

											_SCREAMING_SNAKE_CASE		:						int			  =    torch.randn((batch_size, embedding_dim)).to(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.randn((batch_size, num_embeddings, embedding_dim)).to(_A)

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





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







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





				@parameterized.expand(
				    [
				        # fmt: off
				        [1_3, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
				        [3_7, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
				        # fmt: on
				    ])
				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							List[str]      ,    _A							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""      ,    subfolder="""prior""")
											model.to(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.get_dummy_seed_input(seed=_A)

											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						int			  =    model(**_A)[0]

											assert list(sample.shape) == [1, 7_6_8]

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    sample[0, :8].flatten().cpu()
											print(_A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.tensor(_A)

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



"""simple docstring"""





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




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											if dst_width < 0 or dst_height < 0:
																		raise ValueError("""Destination width/height should be > 0""")

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

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											for i in range(self.dst_h):
																		for j in range(self.dst_w):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    self.img[self.get_y(_A)][self.get_x(_A)]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


if __name__ == "__main__":
			lowerCAmelCase_ ,					lowerCAmelCase_     =	800, 600
			lowerCAmelCase_     =	imread('''image_data/lena.jpg''', 1)
			lowerCAmelCase_     =	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()


