


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						np.ndarray:


						'''simple docstring'''






						__snake_case				=		cva.getAffineTransform(_lowerCAmelCase   ,						_lowerCAmelCase			)
						return cva.warpAffine(_lowerCAmelCase   ,						_lowerCAmelCase   ,						(rows, cols)			)


if __name__ == "__main__":
	# read original image
	A				:       Optional[Any]      =  cva.imread(
	    str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
	)
	# turn image in gray scale value
	A				:       str      =  cva.cvtColor(image, cva.COLOR_BGR2GRAY)
	# get image shape
	A    ,	A				:       Optional[Any]      =  gray_img.shape

	# set different points to rotate image
	A				:       List[Any]      =  np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A				:       List[Any]      =  np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A				:       Optional[int]      =  np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A				:       str      =  np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

	# add all rotated images in a list
	A				:       List[Any]      =  [
	    gray_img,
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	]

	# plot different image rotations
	A				:       int      =  plt.figure(1)
	A				:       Union[str, Any]      =  ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
	for i, image in enumerate(images):
		plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
		plt.title(titles[i])
		plt.axis('off')
		plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
	plt.show()









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      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					Any    					=						PriorTransformer
					snake_case_		:					List[str]    					=						"""hidden_states"""


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		floats_tensor((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		floats_tensor((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0    )      ->						Any:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE    )
											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

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


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

											return (4, 8)


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




											'''simple docstring'''

											return (4, 8)


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Dict:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

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

											model.to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		model(**self.dummy_input    )[0]

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		self.prepare_init_args_and_inputs_for_common()

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

											__snake_case				=		["hidden_states", "timestep"]
											self.assertListEqual(arg_names[:2]  ,     SCREAMING_SNAKE_CASE    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						List[str]:




											'''simple docstring'''

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

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

											__snake_case				=		self.get_dummy_seed_input()

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE    )[0]

											__snake_case				=		output[0, :5].flatten().cpu()
											print(SCREAMING_SNAKE_CASE    )

											# 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.
											__snake_case				=		torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]    )
											self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     rtol=1e-2    )    )






@slow
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		int=1  ,     SCREAMING_SNAKE_CASE						:		str=7_6_8  ,     SCREAMING_SNAKE_CASE						:		Dict=7_7  ,     SCREAMING_SNAKE_CASE						:		int=0    )      ->						List[str]:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE    )
											__snake_case				=		batch_size
											__snake_case				=		embedding_dim
											__snake_case				=		num_embeddings

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Union[str, Any]:




											'''simple docstring'''

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






					@parameterized.expand(
					    [
					        # fmt: off
					        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
					        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
					        # fmt: on
					    ]    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						List[str]:




											'''simple docstring'''

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

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE    )[0]

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

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

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





