


'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( UpperCamelCase_	):



     """simple docstring"""






     __magic_name__    			=    (PNDMScheduler,)
     __magic_name__    			=    (("num_inference_steps", 5_0),)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       {
                "num_train_timesteps": 1000,
                "beta_start": 0.0001,
                "beta_end": 0.02,
                "beta_schedule": "linear",
            }

            config.update(**__A   )
            return config







     def a	( self      ,	snake_case__=0      ,	**snake_case__   ):

            '''simple docstring'''

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

            for scheduler_class in self.scheduler_classes:
                   _lowerCAmelCase    :							str   			=       self.get_scheduler_config(**__A   )
                   _lowerCAmelCase    :							int   			=       scheduler_class(**__A   )
                   scheduler.set_timesteps(__A   )
                   # copy over dummy past residuals
                   _lowerCAmelCase    :							Dict   			=       dummy_past_residuals[:]

                   with tempfile.TemporaryDirectory() as tmpdirname:
                          scheduler.save_config(__A   )
                          _lowerCAmelCase    :							Tuple   			=       scheduler_class.from_pretrained(__A   )
                          new_scheduler.set_timesteps(__A   )
                          # copy over dummy past residuals
                          _lowerCAmelCase    :							List[str]   			=       dummy_past_residuals[:]

                   _lowerCAmelCase    :							int   			=       scheduler.step_prk(__A      ,	__A      ,	__A      ,	**__A   ).prev_sample
                   _lowerCAmelCase    :							List[Any]   			=       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"

                   _lowerCAmelCase    :							Tuple   			=       scheduler.step_plms(__A      ,	__A      ,	__A      ,	**__A   ).prev_sample
                   _lowerCAmelCase    :							int   			=       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 a	( self   ):

            '''simple docstring'''

            pass







     def a	( self      ,	snake_case__=0      ,	**snake_case__   ):

            '''simple docstring'''

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

            for scheduler_class in self.scheduler_classes:
                   _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
                   _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**__A   )
                   scheduler.set_timesteps(__A   )

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

                   with tempfile.TemporaryDirectory() as tmpdirname:
                          scheduler.save_config(__A   )
                          _lowerCAmelCase    :							Tuple   			=       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)
                          _lowerCAmelCase    :							Optional[Any]   			=       dummy_past_residuals[:]

                   _lowerCAmelCase    :							List[str]   			=       scheduler.step_prk(__A      ,	__A      ,	__A      ,	**__A   ).prev_sample
                   _lowerCAmelCase    :							List[str]   			=       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"

                   _lowerCAmelCase    :							Tuple   			=       scheduler.step_plms(__A      ,	__A      ,	__A      ,	**__A   ).prev_sample
                   _lowerCAmelCase    :							Union[str, Any]   			=       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 a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config(**__A   )
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**__A   )

            _lowerCAmelCase    :							Optional[int]   			=       10
            _lowerCAmelCase    :							Dict   			=       self.dummy_model()
            _lowerCAmelCase    :							Any   			=       self.dummy_sample_deter
            scheduler.set_timesteps(__A   )

            for i, t in enumerate(scheduler.prk_timesteps   ):
                   _lowerCAmelCase    :							List[str]   			=       model(__A      ,	__A   )
                   _lowerCAmelCase    :							Union[str, Any]   			=       scheduler.step_prk(__A      ,	__A      ,	__A   ).prev_sample

            for i, t in enumerate(scheduler.plms_timesteps   ):
                   _lowerCAmelCase    :							List[Any]   			=       model(__A      ,	__A   )
                   _lowerCAmelCase    :							List[Any]   			=       scheduler.step_plms(__A      ,	__A      ,	__A   ).prev_sample

            return sample







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       dict(self.forward_default_kwargs   )

            _lowerCAmelCase    :							List[Any]   			=       kwargs.pop('num_inference_steps'      ,	__A   )

            for scheduler_class in self.scheduler_classes:
                   _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
                   _lowerCAmelCase    :							Any   			=       scheduler_class(**__A   )

                   _lowerCAmelCase    :							Optional[Any]   			=       self.dummy_sample
                   _lowerCAmelCase    :							Optional[int]   			=       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'   ):
                          _lowerCAmelCase    :							Tuple   			=       num_inference_steps

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

                   _lowerCAmelCase    :							Any   			=       scheduler.step_prk(__A      ,	0      ,	__A      ,	**__A   ).prev_sample
                   _lowerCAmelCase    :							Dict   			=       scheduler.step_prk(__A      ,	1      ,	__A      ,	**__A   ).prev_sample

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

                   _lowerCAmelCase    :							Dict   			=       scheduler.step_plms(__A      ,	0      ,	__A      ,	**__A   ).prev_sample
                   _lowerCAmelCase    :							Optional[Any]   			=       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 a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config(steps_offset=1   )
            _lowerCAmelCase    :							str   			=       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 a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       27

            for scheduler_class in self.scheduler_classes:
                   _lowerCAmelCase    :							List[Any]   			=       self.dummy_sample
                   _lowerCAmelCase    :							Union[str, Any]   			=       0.1 * sample

                   _lowerCAmelCase    :							Dict   			=       self.get_scheduler_config()
                   _lowerCAmelCase    :							Dict   			=       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]   ):
                          _lowerCAmelCase    :							Optional[int]   			=       scheduler.step_prk(__A      ,	__A      ,	__A   ).prev_sample







     def a	( self   ):

            '''simple docstring'''

            with self.assertRaises(__A   ):
                   _lowerCAmelCase    :							Any   			=       self.scheduler_classes[0]
                   _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
                   _lowerCAmelCase    :							List[str]   			=       scheduler_class(**__A   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.full_loop()
            _lowerCAmelCase    :							Optional[Any]   			=       torch.sum(torch.abs(__A   )   )
            _lowerCAmelCase    :							Any   			=       torch.mean(torch.abs(__A   )   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.full_loop(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							List[Any]   			=       torch.sum(torch.abs(__A   )   )
            _lowerCAmelCase    :							Optional[Any]   			=       torch.mean(torch.abs(__A   )   )

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







     def a	( self   ):

            '''simple docstring'''

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

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       self.full_loop(set_alpha_to_one=__A      ,	beta_start=0.01   )
            _lowerCAmelCase    :							int   			=       torch.sum(torch.abs(__A   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(__A   )   )

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





'''simple docstring'''







from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

