





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	(							__lowerCAmelCase       ,      unittest.TestCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					PriorTransformer
							lowerCAmelCase__						=					"hidden_states"




							@property
							def 							UpperCAmelCase__			(  self			:   str		):
									__snake_case:						Dict			  =						4
									__snake_case:						Any			  =						8
									__snake_case:						Optional[int]			  =						7

									__snake_case:						str			  =						floats_tensor((batch_size, embedding_dim)		).to(lowerCamelCase__		)

									__snake_case:						List[Any]			  =						floats_tensor((batch_size, embedding_dim)		).to(lowerCamelCase__		)
									__snake_case:						Any			  =						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 							UpperCAmelCase__			(  self			:   Optional[int]							,    A			:   Any=0		):
									torch.manual_seed(lowerCamelCase__		)
									__snake_case:						Tuple			  =						4
									__snake_case:						str			  =						8
									__snake_case:						Optional[int]			  =						7

									__snake_case:						List[str]			  =						torch.randn((batch_size, embedding_dim)		).to(lowerCamelCase__		)

									__snake_case:						Dict			  =						torch.randn((batch_size, embedding_dim)		).to(lowerCamelCase__		)
									__snake_case:						Optional[Any]			  =						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 							UpperCAmelCase__			(  self			:   Dict		):
									return (4, 8)




							@property
							def 							UpperCAmelCase__			(  self			:   Tuple		):
									return (4, 8)




							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									__snake_case:						Optional[Any]			  =						{
									    '''num_attention_heads''': 2,
									    '''attention_head_dim''': 4,
									    '''num_layers''': 2,
									    '''embedding_dim''': 8,
									    '''num_embeddings''': 7,
									    '''additional_embeddings''': 4,
									}
									__snake_case:						Any			  =						self.dummy_input
									return init_dict, inputs_dict




							def 							UpperCAmelCase__			(  self			:   str		):
									__snake_case:						Tuple			  =						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__		)
									__snake_case:						Union[str, Any]			  =						model(**self.dummy_input		)[0]

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




							def 							UpperCAmelCase__			(  self			:   Any		):
									__snake_case:						Dict			  =						self.prepare_init_args_and_inputs_for_common()

									__snake_case:						List[str]			  =						self.model_class(**lowerCamelCase__		)
									__snake_case:						Optional[int]			  =						inspect.signature(model.forward		)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__snake_case:						Optional[int]			  =						[*signature.parameters.keys()]

									__snake_case:						int			  =						['''hidden_states''', '''timestep''']
									self.assertListEqual(arg_names[:2]							,    lowerCamelCase__		)






							def 							UpperCAmelCase__			(  self			:   List[str]		):
									__snake_case:						str			  =						PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""		)
									__snake_case:						int			  =						model.to(lowerCamelCase__		)

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

									__snake_case:						Tuple			  =						self.get_dummy_seed_input()

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

									__snake_case:						Dict			  =						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.
									__snake_case:						Optional[Any]			  =						torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]		)
									self.assertTrue(torch_all_close(lowerCamelCase__							,    lowerCamelCase__							,    rtol=1E-2		)		)







@slow
class    __snake_case	(							unittest.TestCase      ):





							'''simple docstring'''







							def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Union[str, Any]=1							,    A			:   Dict=768							,    A			:   List[Any]=77							,    A			:   Tuple=0		):
									torch.manual_seed(lowerCamelCase__		)
									__snake_case:						Union[str, Any]			  =						batch_size
									__snake_case:						List[Any]			  =						embedding_dim
									__snake_case:						Optional[int]			  =						num_embeddings

									__snake_case:						Union[str, Any]			  =						torch.randn((batch_size, embedding_dim)		).to(lowerCamelCase__		)

									__snake_case:						Optional[int]			  =						torch.randn((batch_size, embedding_dim)		).to(lowerCamelCase__		)
									__snake_case:						Union[str, Any]			  =						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 							UpperCAmelCase__			(  self			:   Any		):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							@parameterized.expand(
							    [
							        # fmt: off
							        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
							        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
							        # fmt: on
							    ]		)
							def 							UpperCAmelCase__			(  self			:   Any							,    A			:   Tuple							,    A			:   int		):
									__snake_case:						Any			  =						PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""							,    subfolder="""prior"""		)
									model.to(lowerCamelCase__		)
									__snake_case:						str			  =						self.get_dummy_seed_input(seed=lowerCamelCase__		)

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

									assert list(sample.shape		) == [1, 768]

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

									assert torch_all_close(lowerCamelCase__							,    lowerCamelCase__							,    atol=1E-3		)













import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)






