


"""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   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       PriorTransformer
							__a					 =       '''hidden_states'''




							@property
							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      4
										__lowerCamelCase          =      8
										__lowerCamelCase          =      7

										__lowerCamelCase          =      floats_tensor((batch_size, embedding_dim)   ).to(A_   )

										__lowerCamelCase          =      floats_tensor((batch_size, embedding_dim)   ).to(A_   )
										__lowerCamelCase          =      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   __a   (     self:    Optional[int]     ,							A_:    str=0   ):
										torch.manual_seed(A_   )
										__lowerCamelCase          =      4
										__lowerCamelCase          =      8
										__lowerCamelCase          =      7

										__lowerCamelCase          =      torch.randn((batch_size, embedding_dim)   ).to(A_   )

										__lowerCamelCase          =      torch.randn((batch_size, embedding_dim)   ).to(A_   )
										__lowerCamelCase          =      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   __a   (     self:    Any   ):
										return (4, 8)




							@property
							def   __a   (     self:    Optional[int]   ):
										return (4, 8)




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										__lowerCamelCase          =      self.dummy_input
										return init_dict, inputs_dict




							def   __a   (     self:    Dict   ):
										__lowerCamelCase     ,__lowerCamelCase          =      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_   )
										__lowerCamelCase          =      model(**self.dummy_input   )[0]

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




							def   __a   (     self:    int   ):
										__lowerCamelCase     ,__lowerCamelCase          =      self.prepare_init_args_and_inputs_for_common()

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

										__lowerCamelCase          =      ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]     ,							A_   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""   )
										__lowerCamelCase          =      model.to(A_   )

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

										__lowerCamelCase          =      self.get_dummy_seed_input()

										with torch.no_grad():
													__lowerCamelCase          =      model(**A_   )[0]

										__lowerCamelCase          =      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.
										__lowerCamelCase          =      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  ):




							def   __a   (     self:    Tuple     ,							A_:    Tuple=1     ,							A_:    Dict=7_68     ,							A_:    List[str]=77     ,							A_:    List[Any]=0   ):
										torch.manual_seed(A_   )
										__lowerCamelCase          =      batch_size
										__lowerCamelCase          =      embedding_dim
										__lowerCamelCase          =      num_embeddings

										__lowerCamelCase          =      torch.randn((batch_size, embedding_dim)   ).to(A_   )

										__lowerCamelCase          =      torch.randn((batch_size, embedding_dim)   ).to(A_   )
										__lowerCamelCase          =      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   __a   (     self:    Optional[int]   ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




							@parameterized.expand(
							    [
							        # fmt: off
							        [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
							        [37, [-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   __a   (     self:    int     ,							A_:    Tuple     ,							A_:    str   ):
										__lowerCamelCase          =      PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""     ,							subfolder="""prior"""   )
										model.to(A_   )
										__lowerCamelCase          =      self.get_dummy_seed_input(seed=A_   )

										with torch.no_grad():
													__lowerCamelCase          =      model(**A_   )[0]

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

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

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








"""simple docstring"""




def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [int(lowercase__     ) for i in ip_va_address.split("""."""     ) if i.isdigit()]
			return len(lowercase__     ) == 4 and all(0 <= int(lowercase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				input().strip()
					__magic_name__						: Optional[int]    =				'valid' if is_ip_va_address_valid(ip) else 'invalid'
					print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




