

'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case			:     Union[str, Any]										=      logging.get_logger(__name__)

__snake_case			:     str										=      {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	'gpt_neox'


			def __init__(				self	:						Dict		,      lowerCAmelCase_	:						Any=5_04_32		,      lowerCAmelCase_	:						str=61_44		,      lowerCAmelCase_	:						Optional[Any]=44		,      lowerCAmelCase_	:						Tuple=64		,      lowerCAmelCase_	:						Optional[Any]=2_45_76		,      lowerCAmelCase_	:						Optional[Any]="gelu"		,      lowerCAmelCase_	:						Dict=0.25		,      lowerCAmelCase_	:						List[Any]=1_00_00		,      lowerCAmelCase_	:						List[Any]=0.0		,      lowerCAmelCase_	:						Tuple=0.0		,      lowerCAmelCase_	:						List[str]=0.1		,      lowerCAmelCase_	:						Optional[Any]=20_48		,      lowerCAmelCase_	:						List[Any]=0.02		,      lowerCAmelCase_	:						int=1e-5		,      lowerCAmelCase_	:						List[str]=True		,      lowerCAmelCase_	:						Optional[Any]=0		,      lowerCAmelCase_	:						Any=2		,      lowerCAmelCase_	:						str=False		,      lowerCAmelCase_	:						Dict=True		,      lowerCAmelCase_	:						Union[str, Any]=None		,      **lowerCAmelCase_	:						List[Any]		,      )      ->		int:





									'''simple docstring'''
									super().__init__(bos_token_id=lowerCAmelCase_		,      eos_token_id=lowerCAmelCase_		,      **lowerCAmelCase_			)
									A__					:       List[str]     =vocab_size
									A__					:       Dict     =max_position_embeddings
									A__					:       Union[str, Any]     =hidden_size
									A__					:       List[str]     =num_hidden_layers
									A__					:       int     =num_attention_heads
									A__					:       Optional[int]     =intermediate_size
									A__					:       Tuple     =hidden_act
									A__					:       List[Any]     =rotary_pct
									A__					:       int     =rotary_emb_base
									A__					:       List[str]     =attention_dropout
									A__					:       Optional[int]     =hidden_dropout
									A__					:       str     =classifier_dropout
									A__					:       Optional[Any]     =initializer_range
									A__					:       int     =layer_norm_eps
									A__					:       Dict     =use_cache
									A__					:       List[Any]     =tie_word_embeddings
									A__					:       str     =use_parallel_residual
									A__					:       Any     =rope_scaling
									self._rope_scaling_validation()

									if self.hidden_size % self.num_attention_heads != 0:
															raise ValueError(
															    """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""			)



			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									if self.rope_scaling is None:
															return

									if not isinstance(self.rope_scaling		,      lowerCAmelCase_			) or len(self.rope_scaling			) != 2:
															raise ValueError(
															    """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
															    f"got {self.rope_scaling}"			)
									A__					:       Optional[int]     =self.rope_scaling.get("""type"""		,      lowerCAmelCase_			)
									A__					:       List[Any]     =self.rope_scaling.get("""factor"""		,      lowerCAmelCase_			)
									if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
															raise ValueError(
															    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"			)
									if rope_scaling_factor is None or not isinstance(lowerCAmelCase_		,      lowerCAmelCase_			) or rope_scaling_factor <= 1.0:
															raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"			)




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	(CMStochasticIterativeScheduler,)
			__snake_case        =	10


			def lowercase__						(				self	:						List[str]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     ={
									    """num_train_timesteps""": 2_01,
									    """sigma_min""": 0.002,
									    """sigma_max""": 80.0,
									}

									config.update(**lowerCAmelCase_			)
									return config


			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =10

									A__					:       str     =self.get_scheduler_config()
									A__					:       Any     =self.scheduler_classes[0](**lowerCAmelCase_			)

									scheduler.set_timesteps(lowerCAmelCase_			)

									A__					:       List[Any]     =scheduler.timesteps[0]
									A__					:       Union[str, Any]     =scheduler.timesteps[1]

									A__					:       Optional[int]     =self.dummy_sample
									A__					:       Union[str, Any]     =0.1 * sample

									A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample
									A__					:       int     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample

									self.assertEqual(output_a.shape		,      sample.shape			)
									self.assertEqual(output_a.shape		,      output_a.shape			)


			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[int]:





									'''simple docstring'''
									for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_			)


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





									'''simple docstring'''
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Any     =scheduler_class(**lowerCAmelCase_			)

									A__					:       int     =1
									scheduler.set_timesteps(lowerCAmelCase_			)
									A__					:       Any     =scheduler.timesteps

									A__					:       Optional[int]     =torch.manual_seed(0			)

									A__					:       List[Any]     =self.dummy_model()
									A__					:       Optional[int]     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowerCAmelCase_			):
															# 1. scale model input
															A__					:       Optional[Any]     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Tuple     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Dict     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 192.7614			) < 1e-2
									assert abs(result_mean.item() - 0.2510			) < 1e-3


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[1_06, 0]
									scheduler.set_timesteps(timesteps=lowerCAmelCase_			)
									A__					:       List[Any]     =scheduler.timesteps

									A__					:       Optional[Any]     =torch.manual_seed(0			)

									A__					:       int     =self.dummy_model()
									A__					:       int     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															A__					:       Any     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       List[str]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Union[str, Any]     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       List[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 347.6357			) < 1e-2
									assert abs(result_mean.item() - 0.4527			) < 1e-3


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Union[str, Any]     =self.get_scheduler_config()
									A__					:       List[Any]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[39, 30, 12, 15, 0]

									with self.assertRaises(lowerCAmelCase_		,      msg="""`timesteps` must be in descending order."""			):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       List[str]     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Dict     =[39, 30, 12, 1, 0]
									A__					:       int     =len(lowerCAmelCase_			)

									with self.assertRaises(lowerCAmelCase_		,      msg="""Can only pass one of `num_inference_steps` or `timesteps`."""			):
															scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_		,      timesteps=lowerCAmelCase_			)



			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Any     =self.get_scheduler_config()
									A__					:       Optional[int]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       List[str]     =[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowerCAmelCase_		,      msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""		,      ):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)

