

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class        lowercase		(			SCREAMING_SNAKE_CASE__   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							(PNDMScheduler,)
 __SCREAMING_SNAKE_CASE   				=							(("""num_inference_steps""", 50),)




 def     UpperCamelCase__				(self			,			**__a					)					->   Any:






  """simple docstring"""





  UpperCAmelCase__     							=							{
      '''num_train_timesteps''': 1000,
      '''beta_start''': 0.00_01,
      '''beta_end''': 0.02,
      '''beta_schedule''': '''linear''',
  }

  config.update(**A_					)
  return config




 def     UpperCamelCase__				(self			,			__a=0			,			**__a					)					->   Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							dict(self.forward_default_kwargs					)
  UpperCAmelCase__     							=							kwargs.pop('num_inference_steps'			,			A_					)
  UpperCAmelCase__     							=							self.dummy_sample
  UpperCAmelCase__     							=							0.1 * sample
  UpperCAmelCase__     							=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

   UpperCAmelCase__     							=							scheduler.step_prk(A_			,			A_			,			A_			,			**A_					).prev_sample
   UpperCAmelCase__     							=							new_scheduler.step_prk(A_			,			A_			,			A_			,			**A_					).prev_sample

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

   UpperCAmelCase__     							=							scheduler.step_plms(A_			,			A_			,			A_			,			**A_					).prev_sample
   UpperCAmelCase__     							=							new_scheduler.step_plms(A_			,			A_			,			A_			,			**A_					).prev_sample

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




 def     UpperCamelCase__				(self					)					->   Tuple:






  """simple docstring"""





  pass




 def     UpperCamelCase__				(self			,			__a=0			,			**__a					)					->   Tuple:






  """simple docstring"""





  UpperCAmelCase__     							=							dict(self.forward_default_kwargs					)
  UpperCAmelCase__     							=							kwargs.pop('num_inference_steps'			,			A_					)
  UpperCAmelCase__     							=							self.dummy_sample
  UpperCAmelCase__     							=							0.1 * sample
  UpperCAmelCase__     							=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

  for scheduler_class in self.scheduler_classes:
   UpperCAmelCase__     							=							self.get_scheduler_config()
   UpperCAmelCase__     							=							scheduler_class(**A_					)
   scheduler.set_timesteps(A_					)

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

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

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

   UpperCAmelCase__     							=							scheduler.step_prk(A_			,			A_			,			A_			,			**A_					).prev_sample
   UpperCAmelCase__     							=							new_scheduler.step_prk(A_			,			A_			,			A_			,			**A_					).prev_sample

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

   UpperCAmelCase__     							=							scheduler.step_plms(A_			,			A_			,			A_			,			**A_					).prev_sample
   UpperCAmelCase__     							=							new_scheduler.step_plms(A_			,			A_			,			A_			,			**A_					).prev_sample

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




 def     UpperCamelCase__				(self			,			**__a					)					->   List[str]:






  """simple docstring"""





  UpperCAmelCase__     							=							self.scheduler_classes[0]
  UpperCAmelCase__     							=							self.get_scheduler_config(**A_					)
  UpperCAmelCase__     							=							scheduler_class(**A_					)

  UpperCAmelCase__     							=							10
  UpperCAmelCase__     							=							self.dummy_model()
  UpperCAmelCase__     							=							self.dummy_sample_deter
  scheduler.set_timesteps(A_					)

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

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

  return sample




 def     UpperCamelCase__				(self					)					->   List[Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							dict(self.forward_default_kwargs					)

  UpperCAmelCase__     							=							kwargs.pop('num_inference_steps'			,			A_					)

  for scheduler_class in self.scheduler_classes:
   UpperCAmelCase__     							=							self.get_scheduler_config()
   UpperCAmelCase__     							=							scheduler_class(**A_					)

   UpperCAmelCase__     							=							self.dummy_sample
   UpperCAmelCase__     							=							0.1 * sample

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

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

   UpperCAmelCase__     							=							scheduler.step_prk(A_			,			0			,			A_			,			**A_					).prev_sample
   UpperCAmelCase__     							=							scheduler.step_prk(A_			,			1			,			A_			,			**A_					).prev_sample

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

   UpperCAmelCase__     							=							scheduler.step_plms(A_			,			0			,			A_			,			**A_					).prev_sample
   UpperCAmelCase__     							=							scheduler.step_plms(A_			,			1			,			A_			,			**A_					).prev_sample

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




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






  """simple docstring"""





  for timesteps in [100, 1000]:
   self.check_over_configs(num_train_timesteps=A_					)




 def     UpperCamelCase__				(self					)					->   Optional[Any]:






  """simple docstring"""





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

  UpperCAmelCase__     							=							self.scheduler_classes[0]
  UpperCAmelCase__     							=							self.get_scheduler_config(steps_offset=1					)
  UpperCAmelCase__     							=							scheduler_class(**A_					)
  scheduler.set_timesteps(10					)
  assert torch.equal(
      scheduler.timesteps			,			torch.LongTensor(
          [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)			,			)




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  for beta_start, beta_end in zip([0.00_01, 0.0_01]			,			[0.0_02, 0.02]					):
   self.check_over_configs(beta_start=A_			,			beta_end=A_					)




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





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




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





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




 def     UpperCamelCase__				(self					)					->   str:






  """simple docstring"""





  for t in [1, 5, 10]:
   self.check_over_forward(time_step=A_					)




 def     UpperCamelCase__				(self					)					->   Tuple:






  """simple docstring"""





  for t, num_inference_steps in zip([1, 5, 10]			,			[10, 50, 100]					):
   self.check_over_forward(num_inference_steps=A_					)




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






  """simple docstring"""





  UpperCAmelCase__     							=							27

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

   UpperCAmelCase__     							=							self.get_scheduler_config()
   UpperCAmelCase__     							=							scheduler_class(**A_					)

   scheduler.set_timesteps(A_					)

   # 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__     							=							scheduler.step_prk(A_			,			A_			,			A_					).prev_sample




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  with self.assertRaises(A_					):
   UpperCAmelCase__     							=							self.scheduler_classes[0]
   UpperCAmelCase__     							=							self.get_scheduler_config()
   UpperCAmelCase__     							=							scheduler_class(**A_					)

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




 def     UpperCamelCase__				(self					)					->   List[Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							self.full_loop()
  UpperCAmelCase__     							=							torch.sum(torch.abs(A_					)					)
  UpperCAmelCase__     							=							torch.mean(torch.abs(A_					)					)

  assert abs(result_sum.item() - 198.1318					) < 1E-2
  assert abs(result_mean.item() - 0.25_80					) < 1E-3




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  UpperCAmelCase__     							=							self.full_loop(prediction_type='v_prediction'					)
  UpperCAmelCase__     							=							torch.sum(torch.abs(A_					)					)
  UpperCAmelCase__     							=							torch.mean(torch.abs(A_					)					)

  assert abs(result_sum.item() - 67.39_86					) < 1E-2
  assert abs(result_mean.item() - 0.08_78					) < 1E-3




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






  """simple docstring"""





  UpperCAmelCase__     							=							self.full_loop(set_alpha_to_one=A_			,			beta_start=0.01					)
  UpperCAmelCase__     							=							torch.sum(torch.abs(A_					)					)
  UpperCAmelCase__     							=							torch.mean(torch.abs(A_					)					)

  assert abs(result_sum.item() - 230.0399					) < 1E-2
  assert abs(result_mean.item() - 0.29_95					) < 1E-3




 def     UpperCamelCase__				(self					)					->   Tuple:






  """simple docstring"""





  UpperCAmelCase__     							=							self.full_loop(set_alpha_to_one=A_			,			beta_start=0.01					)
  UpperCAmelCase__     							=							torch.sum(torch.abs(A_					)					)
  UpperCAmelCase__     							=							torch.mean(torch.abs(A_					)					)

  assert abs(result_sum.item() - 186.9482					) < 1E-2
  assert abs(result_mean.item() - 0.24_34					) < 1E-3





"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


A__ :       List[Any]				= logging.get_logger(__name__)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					*A_			,					**A_							)  ->						None:





         """simple docstring"""




         warnings.warn(
             '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
             ''' Please use SegformerImageProcessor instead.'''			,					A_			,					)
         super().__init__(*A_			,					**A_							)
