import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   (PNDMScheduler,)
 lowercase				     =   (('num_inference_steps', 5_0),)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      **snake_case	:							Union[str, Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					{
      'num_train_timesteps': 1_0_0_0,
      'beta_start': 0.0001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**snake_case			)
  return config

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Dict=0   ,      **snake_case	:							int			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					dict(self.forward_default_kwargs			)
  UpperCamelCase_					:						int =					kwargs.pop('num_inference_steps'   ,      snake_case			)
  UpperCamelCase_					:						Dict =					self.dummy_sample
  UpperCamelCase_					:						Union[str, Any] =					0.1 * sample
  UpperCamelCase_					:						List[Any] =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

  for scheduler_class in self.scheduler_classes:
   UpperCamelCase_					:						Union[str, Any] =					self.get_scheduler_config(**snake_case			)
   UpperCamelCase_					:						Any =					scheduler_class(**snake_case			)
   scheduler.set_timesteps(snake_case			)
   # copy over dummy past residuals
   UpperCamelCase_					:						int =					dummy_past_residuals[:]

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

   UpperCamelCase_					:						Optional[Any] =					scheduler.step_prk(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample
   UpperCamelCase_					:						Union[str, Any] =					new_scheduler.step_prk(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample

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

   UpperCamelCase_					:						Tuple =					scheduler.step_plms(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample
   UpperCamelCase_					:						Tuple =					new_scheduler.step_plms(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Dict:

  """simple docstring"""





  pass

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Optional[Any]=0   ,      **snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					dict(self.forward_default_kwargs			)
  UpperCamelCase_					:						Union[str, Any] =					kwargs.pop('num_inference_steps'   ,      snake_case			)
  UpperCamelCase_					:						Any =					self.dummy_sample
  UpperCamelCase_					:						str =					0.1 * sample
  UpperCamelCase_					:						List[str] =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

  for scheduler_class in self.scheduler_classes:
   UpperCamelCase_					:						Optional[Any] =					self.get_scheduler_config()
   UpperCamelCase_					:						str =					scheduler_class(**snake_case			)
   scheduler.set_timesteps(snake_case			)

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

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

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

   UpperCamelCase_					:						str =					scheduler.step_prk(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample
   UpperCamelCase_					:						Optional[Any] =					new_scheduler.step_prk(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample

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

   UpperCamelCase_					:						str =					scheduler.step_plms(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample
   UpperCamelCase_					:						Optional[Any] =					new_scheduler.step_plms(snake_case   ,      snake_case   ,      snake_case   ,      **snake_case			).prev_sample

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      **snake_case	:							Optional[Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						str =					self.scheduler_classes[0]
  UpperCamelCase_					:						Any =					self.get_scheduler_config(**snake_case			)
  UpperCamelCase_					:						Dict =					scheduler_class(**snake_case			)

  UpperCamelCase_					:						Tuple =					1_0
  UpperCamelCase_					:						str =					self.dummy_model()
  UpperCamelCase_					:						Tuple =					self.dummy_sample_deter
  scheduler.set_timesteps(snake_case			)

  for i, t in enumerate(scheduler.prk_timesteps			):
   UpperCamelCase_					:						Optional[Any] =					model(snake_case   ,      snake_case			)
   UpperCamelCase_					:						Tuple =					scheduler.step_prk(snake_case   ,      snake_case   ,      snake_case			).prev_sample

  for i, t in enumerate(scheduler.plms_timesteps			):
   UpperCamelCase_					:						str =					model(snake_case   ,      snake_case			)
   UpperCamelCase_					:						Optional[int] =					scheduler.step_plms(snake_case   ,      snake_case   ,      snake_case			).prev_sample

  return sample

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					dict(self.forward_default_kwargs			)

  UpperCamelCase_					:						List[Any] =					kwargs.pop('num_inference_steps'   ,      snake_case			)

  for scheduler_class in self.scheduler_classes:
   UpperCamelCase_					:						List[str] =					self.get_scheduler_config()
   UpperCamelCase_					:						List[str] =					scheduler_class(**snake_case			)

   UpperCamelCase_					:						int =					self.dummy_sample
   UpperCamelCase_					:						Optional[int] =					0.1 * sample

   if num_inference_steps is not None and hasattr(snake_case   ,      'set_timesteps'			):
    scheduler.set_timesteps(snake_case			)
   elif num_inference_steps is not None and not hasattr(snake_case   ,      'set_timesteps'			):
    UpperCamelCase_					:						Tuple =					num_inference_steps

   # copy over dummy past residuals (must be done after set_timesteps)
   UpperCamelCase_					:						Union[str, Any] =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
   UpperCamelCase_					:						Tuple =					dummy_past_residuals[:]

   UpperCamelCase_					:						Optional[int] =					scheduler.step_prk(snake_case   ,      0   ,      snake_case   ,      **snake_case			).prev_sample
   UpperCamelCase_					:						Dict =					scheduler.step_prk(snake_case   ,      1   ,      snake_case   ,      **snake_case			).prev_sample

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

   UpperCamelCase_					:						Union[str, Any] =					scheduler.step_plms(snake_case   ,      0   ,      snake_case   ,      **snake_case			).prev_sample
   UpperCamelCase_					:						Optional[Any] =					scheduler.step_plms(snake_case   ,      1   ,      snake_case   ,      **snake_case			).prev_sample

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						int:

  """simple docstring"""





  for timesteps in [1_0_0, 1_0_0_0]:
   self.check_over_configs(num_train_timesteps=snake_case			)

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

  """simple docstring"""





  for steps_offset in [0, 1]:
   self.check_over_configs(steps_offset=snake_case			)

  UpperCamelCase_					:						Tuple =					self.scheduler_classes[0]
  UpperCamelCase_					:						Union[str, Any] =					self.get_scheduler_config(steps_offset=1			)
  UpperCamelCase_					:						str =					scheduler_class(**snake_case			)
  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	:							Union[str, Any]			)   ->						Optional[Any]:

  """simple docstring"""





  for beta_start, beta_end in zip([0.0001, 0.001]   ,      [0.002, 0.02]			):
   self.check_over_configs(beta_start=snake_case   ,      beta_end=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  for schedule in ["linear", "squaredcos_cap_v2"]:
   self.check_over_configs(beta_schedule=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						int:

  """simple docstring"""





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

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

  """simple docstring"""





  for t in [1, 5, 1_0]:
   self.check_over_forward(time_step=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						List[Any]:

  """simple docstring"""





  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=snake_case			)

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

  """simple docstring"""





  UpperCamelCase_					:						Any =					2_7

  for scheduler_class in self.scheduler_classes:
   UpperCamelCase_					:						Dict =					self.dummy_sample
   UpperCamelCase_					:						List[str] =					0.1 * sample

   UpperCamelCase_					:						List[Any] =					self.get_scheduler_config()
   UpperCamelCase_					:						Dict =					scheduler_class(**snake_case			)

   scheduler.set_timesteps(snake_case			)

   # 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]			):
    UpperCamelCase_					:						Dict =					scheduler.step_prk(snake_case   ,      snake_case   ,      snake_case			).prev_sample

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

  """simple docstring"""





  with self.assertRaises(snake_case			):
   UpperCamelCase_					:						Any =					self.scheduler_classes[0]
   UpperCamelCase_					:						List[str] =					self.get_scheduler_config()
   UpperCamelCase_					:						Union[str, Any] =					scheduler_class(**snake_case			)

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						str =					self.full_loop()
  UpperCamelCase_					:						Union[str, Any] =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						Tuple =					torch.mean(torch.abs(snake_case			)			)

  assert abs(result_sum.item() - 198.1318			) < 1e-2
  assert abs(result_mean.item() - 0.2580			) < 1e-3

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					self.full_loop(prediction_type='v_prediction'			)
  UpperCamelCase_					:						List[Any] =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						int =					torch.mean(torch.abs(snake_case			)			)

  assert abs(result_sum.item() - 67.3986			) < 1e-2
  assert abs(result_mean.item() - 0.0878			) < 1e-3

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.full_loop(set_alpha_to_one=snake_case   ,      beta_start=0.01			)
  UpperCamelCase_					:						Optional[int] =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						Tuple =					torch.mean(torch.abs(snake_case			)			)

  assert abs(result_sum.item() - 230.0399			) < 1e-2
  assert abs(result_mean.item() - 0.2995			) < 1e-3


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					self.full_loop(set_alpha_to_one=snake_case   ,      beta_start=0.01			)
  UpperCamelCase_					:						str =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						Dict =					torch.mean(torch.abs(snake_case			)			)

  assert abs(result_sum.item() - 186.9482			) < 1e-2
  assert abs(result_mean.item() - 0.2434			) < 1e-3




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


a_    					=       logging.get_logger(__name__)

a_    					=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   'megatron-bert'

 def __init__( self	:							List[str]   ,      snake_case	:							Tuple=2_9_0_5_6   ,      snake_case	:							Dict=1_0_2_4   ,      snake_case	:							Dict=2_4   ,      snake_case	:							Union[str, Any]=1_6   ,      snake_case	:							Optional[int]=4_0_9_6   ,      snake_case	:							Optional[int]="gelu"   ,      snake_case	:							Any=0.1   ,      snake_case	:							Tuple=0.1   ,      snake_case	:							Optional[int]=5_1_2   ,      snake_case	:							List[Any]=2   ,      snake_case	:							Tuple=0.02   ,      snake_case	:							Optional[Any]=1e-12   ,      snake_case	:							str=0   ,      snake_case	:							Optional[int]="absolute"   ,      snake_case	:							Union[str, Any]=True   ,      **snake_case	:							Any   ,      )   ->						List[Any]:

  """simple docstring"""





  super().__init__(pad_token_id=snake_case   ,      **snake_case			)

  UpperCamelCase_					:						Optional[Any] =					vocab_size
  UpperCamelCase_					:						Any =					hidden_size
  UpperCamelCase_					:						Union[str, Any] =					num_hidden_layers
  UpperCamelCase_					:						List[Any] =					num_attention_heads
  UpperCamelCase_					:						str =					hidden_act
  UpperCamelCase_					:						List[str] =					intermediate_size
  UpperCamelCase_					:						List[Any] =					hidden_dropout_prob
  UpperCamelCase_					:						Any =					attention_probs_dropout_prob
  UpperCamelCase_					:						Tuple =					max_position_embeddings
  UpperCamelCase_					:						Dict =					type_vocab_size
  UpperCamelCase_					:						Optional[int] =					initializer_range
  UpperCamelCase_					:						Optional[Any] =					layer_norm_eps
  UpperCamelCase_					:						Dict =					position_embedding_type
  UpperCamelCase_					:						List[str] =					use_cache



