

"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__A						       =      logging.get_logger(__name__)





class    snake_case     :
				SCREAMING_SNAKE_CASE_       :				Dict       				=  42
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  None

				@staticmethod
				def 							lowercase_					(     )->       str:



						'''simple docstring'''


						raise NotImplementedError

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Optional[int]					,  **UpperCamelCase__							:    Union[str, Any])->       Optional[Any]:



						'''simple docstring'''


						raise NotImplementedError

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    Dict)->       int:



						'''simple docstring'''


						raise NotImplementedError

				def 							lowercase_					(     self							:    List[str])->       str:



						'''simple docstring'''


						if not self.is_available():
								raise RuntimeError(
								    f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.")




				@classmethod
				def 							lowercase_					(     cls							:    str)->       Dict:



						'''simple docstring'''


						return f"`pip install {cls.pip_package or cls.name}`"





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  """optuna"""

				@staticmethod
				def 							lowercase_					(     )->       Optional[Any]:



						'''simple docstring'''


						return is_optuna_available()

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Dict					,  **UpperCamelCase__							:    Any)->       List[Any]:



						'''simple docstring'''


						return run_hp_search_optuna(_a					,  _a					,  _a					,  **_a)




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Any)->       List[Any]:



						'''simple docstring'''


						return default_hp_space_optuna(_a)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  """ray"""
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  """\'ray[tune]\'"""

				@staticmethod
				def 							lowercase_					(     )->       Tuple:



						'''simple docstring'''


						return is_ray_available()

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    List[Any]					,  **UpperCamelCase__							:    Any)->       str:



						'''simple docstring'''


						return run_hp_search_ray(_a					,  _a					,  _a					,  **_a)




				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Union[str, Any])->       int:



						'''simple docstring'''


						return default_hp_space_ray(_a)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  """sigopt"""

				@staticmethod
				def 							lowercase_					(     )->       str:



						'''simple docstring'''


						return is_sigopt_available()

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Union[str, Any]					,  **UpperCamelCase__							:    Optional[Any])->       str:



						'''simple docstring'''


						return run_hp_search_sigopt(_a					,  _a					,  _a					,  **_a)




				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Tuple)->       List[str]:



						'''simple docstring'''


						return default_hp_space_sigopt(_a)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  """wandb"""

				@staticmethod
				def 							lowercase_					(     )->       int:



						'''simple docstring'''


						return is_wandb_available()

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  **UpperCamelCase__							:    Dict)->       List[Any]:



						'''simple docstring'''


						return run_hp_search_wandb(_a					,  _a					,  _a					,  **_a)




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Dict)->       Tuple:



						'''simple docstring'''


						return default_hp_space_wandb(_a)


__A						       =      {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}






def    a__   (   )     ->     str:
		__lowerCAmelCase:	List[Any]   				=      [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
		if len(_snake_case			) > 0:
				__lowerCAmelCase:	Dict   				=      available_backends[0].name
				if len(_snake_case			) > 1:
						logger.info(
						    F"{len(_snake_case			)} hyperparameter search backends available. Using {name} as the default."			)
				return name
		raise RuntimeError(
		    "No hyperparameter search backend available.\n"
		    + "\n".join(
		        F" - To install {backend.name} run {backend.pip_install()}"
		        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()			)			)





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

				@property
				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      4
						__lowerCAmelCase:	int   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	str   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    str=0)->       str:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      4
						__lowerCAmelCase:	Dict   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	List[str]   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				@property
				def 							lowercase_					(     self							:    Dict)->       List[Any]:



						'''simple docstring'''


						return (4, 8)

				@property
				def 							lowercase_					(     self							:    Optional[int])->       int:



						'''simple docstring'''


						return (4, 8)

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    List[Any])->       int:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      PriorTransformer.from_pretrained(
						    "hf-internal-testing/prior-dummy"					,  output_loading_info=UpperCamelCase__)
						self.assertIsNotNone(UpperCamelCase__)
						self.assertEqual(len(loading_info["missing_keys"])					,  0)

						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      model(**self.dummy_input)[0]

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

				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      self.prepare_init_args_and_inputs_for_common()

						__lowerCAmelCase:	Tuple   				=      self.model_class(**UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__lowerCAmelCase:	List[Any]   				=      [*signature.parameters.keys()]

						__lowerCAmelCase:	Any   				=      ["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]					,  UpperCamelCase__)




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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
						__lowerCAmelCase:	Union[str, Any]   				=      model.to(UpperCamelCase__)

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

						__lowerCAmelCase:	str   				=      self.get_dummy_seed_input()

						with torch.no_grad():
								__lowerCAmelCase:	Dict   				=      model(**UpperCamelCase__)[0]

						__lowerCAmelCase:	Dict   				=      output[0, :5].flatten().cpu()
						print(UpperCamelCase__)

						# 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:	List[str]   				=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
						self.assertTrue(torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  rtol=1e-2))





@slow
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict=1					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    int=7_7					,  UpperCamelCase__							:    Any=0)->       Union[str, Any]:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Any   				=      embedding_dim
						__lowerCAmelCase:	Dict   				=      num_embeddings

						__lowerCAmelCase:	Dict   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    List[Any])->       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 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    int)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"					,  subfolder="prior")
						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.get_dummy_seed_input(seed=UpperCamelCase__)

						with torch.no_grad():
								__lowerCAmelCase:	Optional[Any]   				=      model(**UpperCamelCase__)[0]

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

						__lowerCAmelCase:	Dict   				=      sample[0, :8].flatten().cpu()
						print(UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor(UpperCamelCase__)

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


