


"""simple docstring"""


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


A	:  int            =     logging.get_logger(__name__)

A	:  Tuple            =     {
    "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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[Any]							 ="""gpt_neox"""

							def __init__(	self       ,  __a=5_04_32       ,  __a=61_44       ,  __a=44       ,  __a=64       ,  __a=2_45_76       ,  __a="gelu"       ,  __a=0.2_5       ,  __a=1_00_00       ,  __a=0.0       ,  __a=0.0       ,  __a=0.1       ,  __a=20_48       ,  __a=0.0_2       ,  __a=1e-5       ,  __a=True       ,  __a=0       ,  __a=2       ,  __a=False       ,  __a=True       ,  __a=None       ,  **__a       ,  ):
										super().__init__(bos_token_id=__a       ,  eos_token_id=__a       ,  **__a      )
										__lowerCAmelCase							       =     vocab_size
										__lowerCAmelCase							       =     max_position_embeddings
										__lowerCAmelCase							       =     hidden_size
										__lowerCAmelCase							       =     num_hidden_layers
										__lowerCAmelCase							       =     num_attention_heads
										__lowerCAmelCase							       =     intermediate_size
										__lowerCAmelCase							       =     hidden_act
										__lowerCAmelCase							       =     rotary_pct
										__lowerCAmelCase							       =     rotary_emb_base
										__lowerCAmelCase							       =     attention_dropout
										__lowerCAmelCase							       =     hidden_dropout
										__lowerCAmelCase							       =     classifier_dropout
										__lowerCAmelCase							       =     initializer_range
										__lowerCAmelCase							       =     layer_norm_eps
										__lowerCAmelCase							       =     use_cache
										__lowerCAmelCase							       =     tie_word_embeddings
										__lowerCAmelCase							       =     use_parallel_residual
										__lowerCAmelCase							       =     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      snake_case							(	self      ):
										if self.rope_scaling is None:
													return

										if not isinstance(self.rope_scaling       ,  __a      ) 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}"      )
										__lowerCAmelCase							       =     self.rope_scaling.get("type"       ,  __a      )
										__lowerCAmelCase							       =     self.rope_scaling.get("factor"       ,  __a      )
										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(__a       ,  __a      ) 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 KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =(KDPMaDiscreteScheduler,)
							__UpperCAmelCase		:       Optional[Any]							 =1_0

							def      snake_case							(	self       ,  **__a      ):
										__lowerCAmelCase							       =     {
										    "num_train_timesteps": 11_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__a      )
										return config

							def      snake_case							(	self      ):
										for timesteps in [10, 50, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=__a      )

							def      snake_case							(	self      ):
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
													self.check_over_configs(beta_start=__a       ,  beta_end=__a      )

							def      snake_case							(	self      ):
										for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__a      )

							def      snake_case							(	self      ):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config(prediction_type="v_prediction"      )
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.6_9_3_4e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 6.1_1_1_2e-1_0      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3

							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3



							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps       ,  device=__a      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter.to(__a      ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if str(__a      ).startswith("cpu"      ):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




