



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 					__lowerCAmelCase :
				__lowerCamelCase =				42
				__lowerCamelCase =				None




				@staticmethod
				def 							snake_case   (							):



									"""simple docstring"""





									raise NotImplementedError




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		):



									"""simple docstring"""





									raise NotImplementedError




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									raise NotImplementedError




				def 							snake_case   (							self		):



									"""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 							snake_case   (							cls		):



									"""simple docstring"""





									return F'`pip install {cls.pip_package or cls.name}`'





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''optuna'''




				@staticmethod
				def 							snake_case   (							):



									"""simple docstring"""





									return is_optuna_available()




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		):



									"""simple docstring"""





									return run_hp_search_optuna(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									return default_hp_space_optuna(_snake_case		)





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''ray'''
				__lowerCamelCase =				'''\'ray[tune]\''''




				@staticmethod
				def 							snake_case   (							):



									"""simple docstring"""





									return is_ray_available()




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		):



									"""simple docstring"""





									return run_hp_search_ray(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									return default_hp_space_ray(_snake_case		)





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''sigopt'''




				@staticmethod
				def 							snake_case   (							):



									"""simple docstring"""





									return is_sigopt_available()




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		):



									"""simple docstring"""





									return run_hp_search_sigopt(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									return default_hp_space_sigopt(_snake_case		)





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''wandb'''




				@staticmethod
				def 							snake_case   (							):



									"""simple docstring"""





									return is_wandb_available()




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		):



									"""simple docstring"""





									return run_hp_search_wandb(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									return default_hp_space_wandb(_snake_case		)

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


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(snake_case			) > 0:
										_lowerCAmelCase						=    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'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
							import tensorflow as tf

							from transformers import TFBlipTextModel
							from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A=12		,		A=7		,		A=True		,		A=True		,		A=True		,		A=99		,		A=32		,		A=32		,		A=2		,		A=4		,		A=37		,		A=0.1		,		A=0.1		,		A=512		,		A=0.0_2		,		A=0		,		A=None		,		)			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		parent
								UpperCAmelCase     :		str							     =		batch_size
								UpperCAmelCase     :		Union[str, Any]							     =		seq_length
								UpperCAmelCase     :		Optional[Any]							     =		is_training
								UpperCAmelCase     :		int							     =		use_input_mask
								UpperCAmelCase     :		List[Any]							     =		use_labels
								UpperCAmelCase     :		Dict							     =		vocab_size
								UpperCAmelCase     :		str							     =		hidden_size
								UpperCAmelCase     :		List[Any]							     =		projection_dim
								UpperCAmelCase     :		Tuple							     =		num_hidden_layers
								UpperCAmelCase     :		Dict							     =		num_attention_heads
								UpperCAmelCase     :		Optional[Any]							     =		intermediate_size
								UpperCAmelCase     :		Any							     =		dropout
								UpperCAmelCase     :		List[Any]							     =		attention_dropout
								UpperCAmelCase     :		Optional[Any]							     =		max_position_embeddings
								UpperCAmelCase     :		Tuple							     =		initializer_range
								UpperCAmelCase     :		Optional[Any]							     =		scope
								UpperCAmelCase     :		Union[str, Any]							     =		bos_token_id





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Union[str, Any]							     =		None
								if self.use_input_mask:
															UpperCAmelCase     :		str							     =		random_attention_mask([self.batch_size, self.seq_length]    )

								if input_mask is not None:
															UpperCAmelCase     :		Tuple							     =		input_mask.numpy()
															UpperCAmelCase     ,     UpperCAmelCase     :		int							     =		input_mask.shape
															UpperCAmelCase     :		Optional[int]							     =		np.random.randint(1		,		seq_length - 1		,		size=(batch_size,)    )
															for batch_idx, start_index in enumerate(A    ):
																						UpperCAmelCase     :		Tuple							     =		1
																						UpperCAmelCase     :		Optional[Any]							     =		0

								UpperCAmelCase     :		int							     =		self.get_config()

								return config, input_ids, tf.convert_to_tensor(A    )





	def  _lowercase(  self    )			->			int:
								return BlipTextConfig(
								    vocab_size=self.vocab_size		,		hidden_size=self.hidden_size		,		projection_dim=self.projection_dim		,		num_hidden_layers=self.num_hidden_layers		,		num_attention_heads=self.num_attention_heads		,		intermediate_size=self.intermediate_size		,		dropout=self.dropout		,		attention_dropout=self.attention_dropout		,		max_position_embeddings=self.max_position_embeddings		,		initializer_range=self.initializer_range		,		bos_token_id=self.bos_token_id		,		)





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		TFBlipTextModel(config=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		model(A		,		attention_mask=A		,		training=A    )
								UpperCAmelCase     :		int							     =		model(A		,		training=A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )
								self.parent.assertEqual(result.pooler_output.shape		,		(self.batch_size, self.hidden_size)    )






	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		self.prepare_config_and_inputs()
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		config_and_inputs
								UpperCAmelCase     :		Optional[Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_tf
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = (TFBlipTextModel,) if is_tf_available() else ()
	lowercase          = False
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Union[str, Any]							     =		BlipTextModelTester(self    )
								UpperCAmelCase     :		List[str]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=37    )





	def  _lowercase(  self    )			->			Tuple:
								self.config_tester.run_common_tests()





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A    )





	def  _lowercase(  self    )			->			List[str]:
								pass





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	@unittest.skip(reason="""Blip does not use inputs_embeds"""    )
	def  _lowercase(  self    )			->			Union[str, Any]:
								pass





	@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
	def  _lowercase(  self    )			->			Optional[int]:
								pass





	@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
	def  _lowercase(  self    )			->			Dict:
								pass





	@slow
	def  _lowercase(  self    )			->			Dict:
								for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase     :		Any							     =		TFBlipTextModel.from_pretrained(A    )
															self.assertIsNotNone(A    )






	def  _lowercase(  self		,		A=True    )			->			str:
								super().test_pt_tf_model_equivalence(allow_missing_keys=A    )

