import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							(PNDMScheduler,)
   lowerCAmelCase_														=							(("num_inference_steps", 50),)




   def      __a						(   self      :    Union[str, Any]	,     **_lowercase      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				{
         '''num_train_timesteps''': 10_00,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
     }

     config.update(**_lowercase				)
     return config




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    Any=0	,     **_lowercase      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				dict(self.forward_default_kwargs				)
     SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""num_inference_steps"""	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_sample
     SCREAMING_SNAKE_CASE__      							=				0.1 * sample
     SCREAMING_SNAKE_CASE__      							=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

       SCREAMING_SNAKE_CASE__      							=				scheduler.step_prk(_lowercase	,     _lowercase	,     _lowercase	,     **_lowercase				).prev_sample
       SCREAMING_SNAKE_CASE__      							=				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"

       SCREAMING_SNAKE_CASE__      							=				scheduler.step_plms(_lowercase	,     _lowercase	,     _lowercase	,     **_lowercase				).prev_sample
       SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    int				):



     """simple docstring"""






     pass




   def      __a						(   self      :    List[Any]	,     _lowercase      :    str=0	,     **_lowercase      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				dict(self.forward_default_kwargs				)
     SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""num_inference_steps"""	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_sample
     SCREAMING_SNAKE_CASE__      							=				0.1 * sample
     SCREAMING_SNAKE_CASE__      							=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__      							=				scheduler_class(**_lowercase				)
       scheduler.set_timesteps(_lowercase				)

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				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)
         SCREAMING_SNAKE_CASE__      							=				dummy_past_residuals[:]

       SCREAMING_SNAKE_CASE__      							=				scheduler.step_prk(_lowercase	,     _lowercase	,     _lowercase	,     **_lowercase				).prev_sample
       SCREAMING_SNAKE_CASE__      							=				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"

       SCREAMING_SNAKE_CASE__      							=				scheduler.step_plms(_lowercase	,     _lowercase	,     _lowercase	,     **_lowercase				).prev_sample
       SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    Optional[Any]	,     **_lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				scheduler_class(**_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				10
     SCREAMING_SNAKE_CASE__      							=				self.dummy_model()
     SCREAMING_SNAKE_CASE__      							=				self.dummy_sample_deter
     scheduler.set_timesteps(_lowercase				)

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

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

     return sample




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				dict(self.forward_default_kwargs				)

     SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""num_inference_steps"""	,     _lowercase				)

     for scheduler_class in self.scheduler_classes:
       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__      							=				scheduler_class(**_lowercase				)

       SCREAMING_SNAKE_CASE__      							=				self.dummy_sample
       SCREAMING_SNAKE_CASE__      							=				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"""				):
         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 + 0.1, residual + 0.05]
       SCREAMING_SNAKE_CASE__      							=				dummy_past_residuals[:]

       SCREAMING_SNAKE_CASE__      							=				scheduler.step_prk(_lowercase	,     0	,     _lowercase	,     **_lowercase				).prev_sample
       SCREAMING_SNAKE_CASE__      							=				scheduler.step_prk(_lowercase	,     1	,     _lowercase	,     **_lowercase				).prev_sample

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

       SCREAMING_SNAKE_CASE__      							=				scheduler.step_plms(_lowercase	,     0	,     _lowercase	,     **_lowercase				).prev_sample
       SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    Any				):



     """simple docstring"""






     for timesteps in [1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowercase				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config(steps_offset=1				)
     SCREAMING_SNAKE_CASE__      							=				scheduler_class(**_lowercase				)
     scheduler.set_timesteps(10				)
     assert torch.equal(
         scheduler.timesteps	,     torch.LongTensor(
             [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]				)	,     )




   def      __a						(   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=_lowercase	,     beta_end=_lowercase				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






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




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    int				):



     """simple docstring"""






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




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     for t, num_inference_steps in zip([1, 5, 10]	,     [10, 50, 1_00]				):
       self.check_over_forward(num_inference_steps=_lowercase				)




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				27

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

       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__      							=				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]				):
         SCREAMING_SNAKE_CASE__      							=				scheduler.step_prk(_lowercase	,     _lowercase	,     _lowercase				).prev_sample




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     with self.assertRaises(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__      							=				self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__      							=				scheduler_class(**_lowercase				)

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




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.full_loop()
     SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				torch.mean(torch.abs(_lowercase				)				)

     assert abs(result_sum.item() - 1_98.13_18				) < 1E-2
     assert abs(result_mean.item() - 0.25_80				) < 1E-3




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.full_loop(prediction_type="""v_prediction"""				)
     SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				torch.mean(torch.abs(_lowercase				)				)

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




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.full_loop(set_alpha_to_one=_lowercase	,     beta_start=0.01				)
     SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				torch.mean(torch.abs(_lowercase				)				)

     assert abs(result_sum.item() - 2_30.03_99				) < 1E-2
     assert abs(result_mean.item() - 0.29_95				) < 1E-3




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.full_loop(set_alpha_to_one=_lowercase	,     beta_start=0.01				)
     SCREAMING_SNAKE_CASE__      							=				torch.sum(torch.abs(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				torch.mean(torch.abs(_lowercase				)				)

     assert abs(result_sum.item() - 1_86.94_82				) < 1E-2
     assert abs(result_mean.item() - 0.24_34				) < 1E-3







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


__lowerCamelCase      :			str							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    List[str]	,     *_lowercase      :    Union[str, Any]	,     **_lowercase      :    Union[str, Any]				):



     """simple docstring"""






     warnings.warn(
         """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use DonutImageProcessor instead."""	,     _lowercase	,     )
     super().__init__(*_lowercase	,     **_lowercase				)
