

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      (UniPCMultistepScheduler,)
   lowerCamelCase__				=      (("num_inference_steps", 2_5),)





   def      _snake_case		(       self					:			Optional[int]     , **__lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   {
           "num_train_timesteps": 1000,
           "beta_start": 0.0_001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "solver_order": 2,
           "solver_type": "bh2",
       }

       config.update(**__lowerCamelCase			)
       return config





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			Optional[Any]=0     , **__lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   dict(self.forward_default_kwargs			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("num_inference_steps"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.dummy_sample
       SCREAMING_SNAKE_CASE										=   0.1 * sample
       SCREAMING_SNAKE_CASE										=   [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
           SCREAMING_SNAKE_CASE										=   self.get_scheduler_config(**__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   scheduler_class(**__lowerCamelCase			)
           scheduler.set_timesteps(__lowerCamelCase			)
           # copy over dummy past residuals
           SCREAMING_SNAKE_CASE										=   dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   scheduler_class.from_pretrained(__lowerCamelCase			)
               new_scheduler.set_timesteps(__lowerCamelCase			)
               # copy over dummy past residuals
               SCREAMING_SNAKE_CASE										=   dummy_past_residuals[: new_scheduler.config.solver_order]

           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   sample, sample
           for t in range(__lowerCamelCase     , time_step + scheduler.config.solver_order + 1			):
               SCREAMING_SNAKE_CASE										=   scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			).prev_sample
               SCREAMING_SNAKE_CASE										=   new_scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			).prev_sample

               assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Tuple=0     , **__lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   dict(self.forward_default_kwargs			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("num_inference_steps"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.dummy_sample
       SCREAMING_SNAKE_CASE										=   0.1 * sample
       SCREAMING_SNAKE_CASE										=   [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
           SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
           SCREAMING_SNAKE_CASE										=   scheduler_class(**__lowerCamelCase			)
           scheduler.set_timesteps(__lowerCamelCase			)

           # copy over dummy past residuals (must be after setting timesteps)
           SCREAMING_SNAKE_CASE										=   dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   scheduler_class.from_pretrained(__lowerCamelCase			)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(__lowerCamelCase			)

               # copy over dummy past residual (must be after setting timesteps)
               SCREAMING_SNAKE_CASE										=   dummy_past_residuals[: new_scheduler.config.solver_order]

           SCREAMING_SNAKE_CASE										=   scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			).prev_sample
           SCREAMING_SNAKE_CASE										=   new_scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			).prev_sample

           assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str=None     , **__lowerCamelCase					:			str			):
       if scheduler is None:
           SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE										=   self.get_scheduler_config(**__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   scheduler_class(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE										=   self.get_scheduler_config(**__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   scheduler_class(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   10
       SCREAMING_SNAKE_CASE										=   self.dummy_model()
       SCREAMING_SNAKE_CASE										=   self.dummy_sample_deter
       scheduler.set_timesteps(__lowerCamelCase			)

       for i, t in enumerate(scheduler.timesteps			):
           SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			).prev_sample

       return sample





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   dict(self.forward_default_kwargs			)

       SCREAMING_SNAKE_CASE										=   kwargs.pop("num_inference_steps"     , __lowerCamelCase			)

       for scheduler_class in self.scheduler_classes:
           SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
           SCREAMING_SNAKE_CASE										=   scheduler_class(**__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   self.dummy_sample
           SCREAMING_SNAKE_CASE										=   0.1 * sample

           if num_inference_steps is not None and hasattr(__lowerCamelCase     , "set_timesteps"			):
               scheduler.set_timesteps(__lowerCamelCase			)
           elif num_inference_steps is not None and not hasattr(__lowerCamelCase     , "set_timesteps"			):
               SCREAMING_SNAKE_CASE										=   num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           SCREAMING_SNAKE_CASE										=   [residual + 0.2, residual + 0.15, residual + 0.10]
           SCREAMING_SNAKE_CASE										=   dummy_past_residuals[: scheduler.config.solver_order]

           SCREAMING_SNAKE_CASE										=   scheduler.timesteps[5]
           SCREAMING_SNAKE_CASE										=   scheduler.timesteps[6]

           SCREAMING_SNAKE_CASE										=   scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			).prev_sample
           SCREAMING_SNAKE_CASE										=   scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			).prev_sample

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





   def      _snake_case		(       self					:			int			):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       SCREAMING_SNAKE_CASE										=   UniPCMultistepScheduler(**self.get_scheduler_config()			)
       SCREAMING_SNAKE_CASE										=   self.full_loop(scheduler=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   torch.mean(torch.abs(__lowerCamelCase			)			)

       assert abs(result_mean.item() - 0.2_464			) < 1e-3

       SCREAMING_SNAKE_CASE										=   DPMSolverSinglestepScheduler.from_config(scheduler.config			)
       SCREAMING_SNAKE_CASE										=   DEISMultistepScheduler.from_config(scheduler.config			)
       SCREAMING_SNAKE_CASE										=   DPMSolverMultistepScheduler.from_config(scheduler.config			)
       SCREAMING_SNAKE_CASE										=   UniPCMultistepScheduler.from_config(scheduler.config			)

       SCREAMING_SNAKE_CASE										=   self.full_loop(scheduler=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   torch.mean(torch.abs(__lowerCamelCase			)			)

       assert abs(result_mean.item() - 0.2_464			) < 1e-3





   def      _snake_case		(       self					:			Any			):
       for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]			):
       self.check_over_configs(thresholding=__lowerCamelCase			)
       for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
               for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "sample"]:
                       self.check_over_configs(
                           thresholding=__lowerCamelCase     , prediction_type=__lowerCamelCase     , sample_max_value=__lowerCamelCase     , solver_order=__lowerCamelCase     , solver_type=__lowerCamelCase     , )





   def      _snake_case		(       self					:			Optional[int]			):
       for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]			):
       for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                   self.check_over_configs(
                       solver_order=__lowerCamelCase     , solver_type=__lowerCamelCase     , prediction_type=__lowerCamelCase     , )
                   SCREAMING_SNAKE_CASE										=   self.full_loop(
                       solver_order=__lowerCamelCase     , solver_type=__lowerCamelCase     , prediction_type=__lowerCamelCase     , )
                   assert not torch.isnan(__lowerCamelCase			).any(), "Samples have nan numbers"





   def      _snake_case		(       self					:			Tuple			):
       self.check_over_configs(lower_order_final=__lowerCamelCase			)
       self.check_over_configs(lower_order_final=__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]			):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=__lowerCamelCase     , time_step=0			)





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.full_loop()
       SCREAMING_SNAKE_CASE										=   torch.mean(torch.abs(__lowerCamelCase			)			)

       assert abs(result_mean.item() - 0.2_464			) < 1e-3





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.full_loop(prediction_type="v_prediction"			)
       SCREAMING_SNAKE_CASE										=   torch.mean(torch.abs(__lowerCamelCase			)			)

       assert abs(result_mean.item() - 0.1_014			) < 1e-3





   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE										=   self.get_scheduler_config(thresholding=__lowerCamelCase     , dynamic_thresholding_ratio=0			)
       SCREAMING_SNAKE_CASE										=   scheduler_class(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   10
       SCREAMING_SNAKE_CASE										=   self.dummy_model()
       SCREAMING_SNAKE_CASE										=   self.dummy_sample_deter.half()
       scheduler.set_timesteps(__lowerCamelCase			)

       for i, t in enumerate(scheduler.timesteps			):
           SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			).prev_sample

       assert sample.dtype == torch.floataa





   def      _snake_case		(       self					:			Optional[int]     , **__lowerCamelCase					:			List[str]			):
       for scheduler_class in self.scheduler_classes:
           SCREAMING_SNAKE_CASE										=   self.get_scheduler_config(**__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   scheduler_class(**__lowerCamelCase			)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
           assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps


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


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{
    '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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "gpt_neox"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]=50432     , __lowerCamelCase					:			int=6144     , __lowerCamelCase					:			Optional[Any]=44     , __lowerCamelCase					:			Tuple=64     , __lowerCamelCase					:			Optional[int]=24576     , __lowerCamelCase					:			List[str]="gelu"     , __lowerCamelCase					:			Any=0.25     , __lowerCamelCase					:			List[Any]=10000     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[Any]=2048     , __lowerCamelCase					:			Tuple=0.02     , __lowerCamelCase					:			Tuple=1e-5     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int=0     , __lowerCamelCase					:			Optional[Any]=2     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			Optional[int]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   rotary_pct
       SCREAMING_SNAKE_CASE										=   rotary_emb_base
       SCREAMING_SNAKE_CASE										=   attention_dropout
       SCREAMING_SNAKE_CASE										=   hidden_dropout
       SCREAMING_SNAKE_CASE										=   classifier_dropout
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   use_cache
       SCREAMING_SNAKE_CASE										=   tie_word_embeddings
       SCREAMING_SNAKE_CASE										=   use_parallel_residual
       SCREAMING_SNAKE_CASE										=   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					:			Union[str, Any]			):
       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}"			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("type"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   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}"			)