




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__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      PriorTransformer
				SCREAMING_SNAKE_CASE_ :							List[str]											=      """hidden_states"""





				@property
				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Optional[Any]:
							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 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]=0							)      ->  Dict:
							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 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  List[Any]:
							return (4, 8)





				@property
				def 							__UpperCAmelCase							(   self       :  str							)      ->  str:
							return (4, 8)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Optional[int]:
							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 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							a					,	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 							__UpperCAmelCase							(   self       :  Dict							)      ->  Dict:
							a					,	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 							__UpperCAmelCase							(   self       :  str							)      ->  List[Any]:
							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_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]							)
							self.assertTrue(torch_all_close(__lowerCamelCase      ,   __lowerCamelCase      ,   rtol=1e-2							)							)




@slow
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  str=1      ,   __lowerCamelCase       :  Tuple=7_68      ,   __lowerCamelCase       :  List[Any]=77      ,   __lowerCamelCase       :  Optional[Any]=0							)      ->  Optional[Any]:
							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 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  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 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Any							)      ->  Tuple:
							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							)












from typing import TYPE_CHECKING

from ....utils import _LazyModule


__lowerCAmelCase	:       int     =	{'tokenization_tapex': ['TapexTokenizer']}


if TYPE_CHECKING:
		from .tokenization_tapex import TapexTokenizer


else:
		import sys

		__lowerCAmelCase	:       Tuple     =	_LazyModule(__name__, globals()['__file__'], _import_structure)






