
'''simple docstring'''
import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    NezhaForMaskedLM,
		    NezhaForMultipleChoice,
		    NezhaForNextSentencePrediction,
		    NezhaForPreTraining,
		    NezhaForQuestionAnswering,
		    NezhaForSequenceClassification,
		    NezhaForTokenClassification,
		    NezhaModel,
		)
		from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_=13					,     UpperCamelCase_=7					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=99					,     UpperCamelCase_=32					,     UpperCamelCase_=5					,     UpperCamelCase_=4					,     UpperCamelCase_=37					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.1					,     UpperCamelCase_=128					,     UpperCamelCase_=32					,     UpperCamelCase_=16					,     UpperCamelCase_=2					,     UpperCamelCase_=0.02					,     UpperCamelCase_=3					,     UpperCamelCase_=4					,     UpperCamelCase_=None					,     ):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				parent
							UpperCamelCase__     :List[str]							      =				batch_size
							UpperCamelCase__     :Tuple							      =				seq_length
							UpperCamelCase__     :Tuple							      =				is_training
							UpperCamelCase__     :Union[str, Any]							      =				use_input_mask
							UpperCamelCase__     :List[str]							      =				use_token_type_ids
							UpperCamelCase__     :Optional[int]							      =				use_labels
							UpperCamelCase__     :int							      =				vocab_size
							UpperCamelCase__     :Any							      =				hidden_size
							UpperCamelCase__     :Any							      =				num_hidden_layers
							UpperCamelCase__     :Any							      =				num_attention_heads
							UpperCamelCase__     :Dict							      =				intermediate_size
							UpperCamelCase__     :int							      =				hidden_act
							UpperCamelCase__     :Optional[Any]							      =				hidden_dropout_prob
							UpperCamelCase__     :Optional[int]							      =				attention_probs_dropout_prob
							UpperCamelCase__     :Any							      =				max_position_embeddings
							UpperCamelCase__     :Any							      =				type_vocab_size
							UpperCamelCase__     :Any							      =				type_sequence_label_size
							UpperCamelCase__     :Union[str, Any]							      =				initializer_range
							UpperCamelCase__     :Dict							      =				num_labels
							UpperCamelCase__     :List[Any]							      =				num_choices
							UpperCamelCase__     :Any							      =				scope




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size							)

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

							UpperCamelCase__     :Union[str, Any]							      =				None
							if self.use_token_type_ids:
												UpperCamelCase__     :int							      =				ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size							)

							UpperCamelCase__     :Tuple							      =				None
							UpperCamelCase__     :Optional[int]							      =				None
							UpperCamelCase__     :Dict							      =				None
							if self.use_labels:
												UpperCamelCase__     :Any							      =				ids_tensor([self.batch_size]					,     self.type_sequence_label_size							)
												UpperCamelCase__     :Dict							      =				ids_tensor([self.batch_size, self.seq_length]					,     self.num_labels							)
												UpperCamelCase__     :Optional[Any]							      =				ids_tensor([self.batch_size]					,     self.num_choices							)

							UpperCamelCase__     :int							      =				self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return NezhaConfig(
							    vocab_size=self.vocab_size					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     max_position_embeddings=self.max_position_embeddings					,     type_vocab_size=self.type_vocab_size					,     is_decoder=UpperCamelCase_					,     initializer_range=self.initializer_range					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							(
							    UpperCamelCase__       
							)     :Optional[int]							      =				self.prepare_config_and_inputs()

							UpperCamelCase__     :Dict							      =				True
							UpperCamelCase__     :Optional[int]							      =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
							UpperCamelCase__     :str							      =				ids_tensor([self.batch_size, self.seq_length]					,     vocab_size=2							)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				NezhaModel(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Any							      =				model(UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_							)
							UpperCamelCase__     :str							      =				model(UpperCamelCase_					,     token_type_ids=UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_							)
							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				True
							UpperCamelCase__     :int							      =				NezhaModel(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[str]							      =				model(
							    UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     encoder_hidden_states=UpperCamelCase_					,     encoder_attention_mask=UpperCamelCase_					,     )
							UpperCamelCase__     :Tuple							      =				model(
							    UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     encoder_hidden_states=UpperCamelCase_					,     )
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_							)
							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				NezhaForMaskedLM(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Tuple							      =				model(UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				NezhaForNextSentencePrediction(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Union[str, Any]							      =				model(
							    UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     labels=UpperCamelCase_					,     )
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, 2)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				NezhaForPreTraining(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Dict							      =				model(
							    UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     labels=UpperCamelCase_					,     next_sentence_label=UpperCamelCase_					,     )
							self.parent.assertEqual(result.prediction_logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)							)
							self.parent.assertEqual(result.seq_relationship_logits.shape					,     (self.batch_size, 2)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				NezhaForQuestionAnswering(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :int							      =				model(
							    UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     start_positions=UpperCamelCase_					,     end_positions=UpperCamelCase_					,     )
							self.parent.assertEqual(result.start_logits.shape					,     (self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape					,     (self.batch_size, self.seq_length)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.num_labels
							UpperCamelCase__     :Dict							      =				NezhaForSequenceClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :str							      =				model(UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				self.num_labels
							UpperCamelCase__     :List[Any]							      =				NezhaForTokenClassification(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.num_labels)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.num_choices
							UpperCamelCase__     :List[str]							      =				NezhaForMultipleChoice(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[Any]							      =				input_ids.unsqueeze(1							).expand(-1					,     self.num_choices					,     -1							).contiguous()
							UpperCamelCase__     :List[str]							      =				token_type_ids.unsqueeze(1							).expand(-1					,     self.num_choices					,     -1							).contiguous()
							UpperCamelCase__     :Dict							      =				input_mask.unsqueeze(1							).expand(-1					,     self.num_choices					,     -1							).contiguous()
							UpperCamelCase__     :Union[str, Any]							      =				model(
							    UpperCamelCase_					,     attention_mask=UpperCamelCase_					,     token_type_ids=UpperCamelCase_					,     labels=UpperCamelCase_					,     )
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_choices)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				self.prepare_config_and_inputs()
							(
							    UpperCamelCase__       
							)     :Union[str, Any]							      =				config_and_inputs
							UpperCamelCase__     :Any							      =				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict



@require_torch
class      lowercase			(							A__			,	A__			,	A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (
		    (
		        NezhaModel,
		        NezhaForMaskedLM,
		        NezhaForMultipleChoice,
		        NezhaForNextSentencePrediction,
		        NezhaForPreTraining,
		        NezhaForQuestionAnswering,
		        NezhaForSequenceClassification,
		        NezhaForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_a									=       (
		    {
		        'feature-extraction': NezhaModel,
		        'fill-mask': NezhaForMaskedLM,
		        'question-answering': NezhaForQuestionAnswering,
		        'text-classification': NezhaForSequenceClassification,
		        'token-classification': NezhaForTokenClassification,
		        'zero-shot': NezhaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_a									=       True




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=False							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				super()._prepare_for_class(UpperCamelCase_					,     UpperCamelCase_					,     return_labels=UpperCamelCase_							)

							if return_labels:
												if model_class in get_values(UpperCamelCase_							):
																	UpperCamelCase__     :List[str]							      =				torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)					,     dtype=torch.long					,     device=UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				torch.zeros(
																	    self.model_tester.batch_size					,     dtype=torch.long					,     device=UpperCamelCase_							)
							return inputs_dict




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				NezhaModelTester(self							)
							UpperCamelCase__     :List[Any]							      =				ConfigTester(self					,     config_class=UpperCamelCase_					,     hidden_size=37							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.config_tester.run_common_tests()




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							(
							    UpperCamelCase__       
							)     :Any							      =				self.model_tester.prepare_config_and_inputs_for_decoder()

							UpperCamelCase__     :Union[str, Any]							      =				None

							self.model_tester.create_and_check_model_as_decoder(
							    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_next_sequence_prediction(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__     :str							      =				NezhaModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)




		@slow
		@require_torch_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
												# NezhaForMultipleChoice behaves incorrectly in JIT environments.
												if model_class == NezhaForMultipleChoice:
																	return

												UpperCamelCase__     :List[Any]							      =				True
												UpperCamelCase__     :Union[str, Any]							      =				model_class(config=UpperCamelCase_							)

												UpperCamelCase__     :Dict							      =				self._prepare_for_class(UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :Tuple							      =				torch.jit.trace(
												    UpperCamelCase_					,     (inputs_dict['''input_ids'''].to('''cpu'''							), inputs_dict['''attention_mask'''].to('''cpu'''							))							)

												with tempfile.TemporaryDirectory() as tmp:
																	torch.jit.save(UpperCamelCase_					,     os.path.join(UpperCamelCase_					,     '''bert.pt'''							)							)
																	UpperCamelCase__     :Any							      =				torch.jit.load(os.path.join(UpperCamelCase_					,     '''bert.pt'''							)					,     map_location=UpperCamelCase_							)
																	loaded(inputs_dict['''input_ids'''].to(UpperCamelCase_							)					,     inputs_dict['''attention_mask'''].to(UpperCamelCase_							)							)



@require_torch
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''							)
							UpperCamelCase__     :List[str]							      =				torch.tensor([[0, 1, 2, 3, 4, 5]]							)
							UpperCamelCase__     :Dict							      =				torch.tensor([[0, 1, 1, 1, 1, 1]]							)
							with torch.no_grad():
												UpperCamelCase__     :Any							      =				model(UpperCamelCase_					,     attention_mask=UpperCamelCase_							)[0]
							UpperCamelCase__     :int							      =				torch.Size((1, 6, 768)							)
							self.assertEqual(output.shape					,     UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]							)

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,     UpperCamelCase_					,     atol=1e-4							)							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''							)
							UpperCamelCase__     :str							      =				torch.tensor([[0, 1, 2, 3, 4, 5]]							)
							UpperCamelCase__     :Tuple							      =				torch.tensor([[1, 1, 1, 1, 1, 1]]							)
							with torch.no_grad():
												UpperCamelCase__     :Tuple							      =				model(UpperCamelCase_					,     attention_mask=UpperCamelCase_							)[0]
							UpperCamelCase__     :int							      =				torch.Size((1, 6, 21128)							)
							self.assertEqual(output.shape					,     UpperCamelCase_							)
							UpperCamelCase__     :int							      =				torch.tensor(
							    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]							)

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,     UpperCamelCase_					,     atol=1e-4							)							)



'''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


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       None




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	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 					lowerCAmelCase__       (	cls							):

							'''simple docstring'''


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



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'optuna'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_optuna_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_optuna(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_optuna(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'ray'
		_a									=       '\'ray[tune]\''




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_ray_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_ray(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_ray(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'sigopt'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_sigopt_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_sigopt(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_sigopt(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'wandb'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_wandb_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_wandb(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_wandb(UpperCamelCase_							)



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


def     a   (    )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(__a     ) > 0:
										UpperCamelCase__     :Tuple							      =				available_backends[0].name
										if len(__a     ) > 1:
															logger.info(
															    f'''{len(__a     )} 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()     )     )