

'''simple docstring'''






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			(PNDMScheduler,)
  SCREAMING_SNAKE_CASE    :  Any							=			(('num_inference_steps', 5_0),)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,**lowercase__   :     Union[str, Any]			):
   __lowercase							  = {
       '''num_train_timesteps''': 1_0_0_0,
       '''beta_start''': 0.0_0_0_1,
       '''beta_end''': 0.0_2,
       '''beta_schedule''': '''linear''',
   }

   config.update(**lowercase__			)
   return config






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Any=0					,**lowercase__   :     Union[str, Any]			):
   __lowercase							  = dict(self.forward_default_kwargs			)
   __lowercase							  = kwargs.pop('''num_inference_steps'''					,lowercase__			)
   __lowercase							  = self.dummy_sample
   __lowercase							  = 0.1 * sample
   __lowercase							  = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
    __lowercase							  = self.get_scheduler_config(**lowercase__			)
    __lowercase							  = scheduler_class(**lowercase__			)
    scheduler.set_timesteps(lowercase__			)
    # copy over dummy past residuals
    __lowercase							  = dummy_past_residuals[:]

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

    __lowercase							  = scheduler.step_prk(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample
    __lowercase							  = new_scheduler.step_prk(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample

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

    __lowercase							  = scheduler.step_plms(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample
    __lowercase							  = new_scheduler.step_plms(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     Optional[int]=0					,**lowercase__   :     str			):
   __lowercase							  = dict(self.forward_default_kwargs			)
   __lowercase							  = kwargs.pop('''num_inference_steps'''					,lowercase__			)
   __lowercase							  = self.dummy_sample
   __lowercase							  = 0.1 * sample
   __lowercase							  = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
    __lowercase							  = self.get_scheduler_config()
    __lowercase							  = scheduler_class(**lowercase__			)
    scheduler.set_timesteps(lowercase__			)

    # copy over dummy past residuals (must be after setting timesteps)
    __lowercase							  = dummy_past_residuals[:]

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

     # copy over dummy past residual (must be after setting timesteps)
     __lowercase							  = dummy_past_residuals[:]

    __lowercase							  = scheduler.step_prk(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample
    __lowercase							  = new_scheduler.step_prk(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample

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

    __lowercase							  = scheduler.step_plms(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample
    __lowercase							  = new_scheduler.step_plms(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,**lowercase__   :     Optional[Any]			):
   __lowercase							  = self.scheduler_classes[0]
   __lowercase							  = self.get_scheduler_config(**lowercase__			)
   __lowercase							  = scheduler_class(**lowercase__			)

   __lowercase							  = 1_0
   __lowercase							  = self.dummy_model()
   __lowercase							  = self.dummy_sample_deter
   scheduler.set_timesteps(lowercase__			)

   for i, t in enumerate(scheduler.prk_timesteps			):
    __lowercase							  = model(lowercase__					,lowercase__			)
    __lowercase							  = scheduler.step_prk(lowercase__					,lowercase__					,lowercase__			).prev_sample

   for i, t in enumerate(scheduler.plms_timesteps			):
    __lowercase							  = model(lowercase__					,lowercase__			)
    __lowercase							  = scheduler.step_plms(lowercase__					,lowercase__					,lowercase__			).prev_sample

   return sample






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = dict(self.forward_default_kwargs			)

   __lowercase							  = kwargs.pop('''num_inference_steps'''					,lowercase__			)

   for scheduler_class in self.scheduler_classes:
    __lowercase							  = self.get_scheduler_config()
    __lowercase							  = scheduler_class(**lowercase__			)

    __lowercase							  = self.dummy_sample
    __lowercase							  = 0.1 * sample

    if num_inference_steps is not None and hasattr(lowercase__					,'''set_timesteps'''			):
     scheduler.set_timesteps(lowercase__			)
    elif num_inference_steps is not None and not hasattr(lowercase__					,'''set_timesteps'''			):
     __lowercase							  = num_inference_steps

    # copy over dummy past residuals (must be done after set_timesteps)
    __lowercase							  = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
    __lowercase							  = dummy_past_residuals[:]

    __lowercase							  = scheduler.step_prk(lowercase__					,0					,lowercase__					,**lowercase__			).prev_sample
    __lowercase							  = scheduler.step_prk(lowercase__					,1					,lowercase__					,**lowercase__			).prev_sample

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

    __lowercase							  = scheduler.step_plms(lowercase__					,0					,lowercase__					,**lowercase__			).prev_sample
    __lowercase							  = scheduler.step_plms(lowercase__					,1					,lowercase__					,**lowercase__			).prev_sample

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   for timesteps in [1_0_0, 1_0_0_0]:
    self.check_over_configs(num_train_timesteps=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   for steps_offset in [0, 1]:
    self.check_over_configs(steps_offset=lowercase__			)

   __lowercase							  = self.scheduler_classes[0]
   __lowercase							  = self.get_scheduler_config(steps_offset=1			)
   __lowercase							  = scheduler_class(**lowercase__			)
   scheduler.set_timesteps(1_0			)
   assert torch.equal(
       scheduler.timesteps					,torch.LongTensor(
           [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]			)					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]					,[0.0_0_2, 0.0_2]			):
    self.check_over_configs(beta_start=lowercase__					,beta_end=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   for schedule in ["linear", "squaredcos_cap_v2"]:
    self.check_over_configs(beta_schedule=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(prediction_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   for t in [1, 5, 1_0]:
    self.check_over_forward(time_step=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   for t, num_inference_steps in zip([1, 5, 1_0]					,[1_0, 5_0, 1_0_0]			):
    self.check_over_forward(num_inference_steps=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
   __lowercase							  = 2_7

   for scheduler_class in self.scheduler_classes:
    __lowercase							  = self.dummy_sample
    __lowercase							  = 0.1 * sample

    __lowercase							  = self.get_scheduler_config()
    __lowercase							  = scheduler_class(**lowercase__			)

    scheduler.set_timesteps(lowercase__			)

    # before power of 3 fix, would error on first step, so we only need to do two
    for i, t in enumerate(scheduler.prk_timesteps[:2]			):
     __lowercase							  = scheduler.step_prk(lowercase__					,lowercase__					,lowercase__			).prev_sample






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   with self.assertRaises(lowercase__			):
    __lowercase							  = self.scheduler_classes[0]
    __lowercase							  = self.get_scheduler_config()
    __lowercase							  = scheduler_class(**lowercase__			)

    scheduler.step_plms(self.dummy_sample					,1					,self.dummy_sample			).prev_sample






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.full_loop()
   __lowercase							  = torch.sum(torch.abs(lowercase__			)			)
   __lowercase							  = torch.mean(torch.abs(lowercase__			)			)

   assert abs(result_sum.item() - 1_9_8.1_3_1_8			) < 1e-2
   assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.full_loop(prediction_type='''v_prediction'''			)
   __lowercase							  = torch.sum(torch.abs(lowercase__			)			)
   __lowercase							  = torch.mean(torch.abs(lowercase__			)			)

   assert abs(result_sum.item() - 6_7.3_9_8_6			) < 1e-2
   assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # We specify different beta, so that the first alpha is 0.99
   __lowercase							  = self.full_loop(set_alpha_to_one=lowercase__					,beta_start=0.0_1			)
   __lowercase							  = torch.sum(torch.abs(lowercase__			)			)
   __lowercase							  = torch.mean(torch.abs(lowercase__			)			)

   assert abs(result_sum.item() - 2_3_0.0_3_9_9			) < 1e-2
   assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # We specify different beta, so that the first alpha is 0.99
   __lowercase							  = self.full_loop(set_alpha_to_one=lowercase__					,beta_start=0.0_1			)
   __lowercase							  = torch.sum(torch.abs(lowercase__			)			)
   __lowercase							  = torch.mean(torch.abs(lowercase__			)			)

   assert abs(result_sum.item() - 1_8_6.9_4_8_2			) < 1e-2
   assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3




'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase__											=	{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__											=	[
     '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPTNeoXJapaneseForCausalLM''',
     '''GPTNeoXJapaneseLayer''',
     '''GPTNeoXJapaneseModel''',
     '''GPTNeoXJapanesePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase__											=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

