"""simple docstring"""


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


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

lowerCAmelCase_:					int     =					{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}






class    a__    (   _a	):
   snake_case_							=      "markuplm"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=2,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=0,				_UpperCAmelCase=2,				_UpperCAmelCase=256,				_UpperCAmelCase=1024,				_UpperCAmelCase=216,				_UpperCAmelCase=1001,				_UpperCAmelCase=32,				_UpperCAmelCase=50,				_UpperCAmelCase="absolute",				_UpperCAmelCase=True,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            pad_token_id=_UpperCAmelCase,				bos_token_id=_UpperCAmelCase,				eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							position_embedding_type
        lowercase__				 =							use_cache
        lowercase__				 =							classifier_dropout
        # additional properties
        lowercase__				 =							max_depth
        lowercase__				 =							max_xpath_tag_unit_embeddings
        lowercase__				 =							max_xpath_subs_unit_embeddings
        lowercase__				 =							tag_pad_id
        lowercase__				 =							subs_pad_id
        lowercase__				 =							xpath_unit_hidden_size





"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    a__    (   _a	):
   snake_case_							=      (IPNDMScheduler,)
   snake_case_							=      (("num_inference_steps", 50),)


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							{"num_train_timesteps": 1000}
        config.update(**_UpperCAmelCase  )
        return config


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



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

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

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



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

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

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

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

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

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							self.scheduler_classes[0]
        lowercase__				 =							self.get_scheduler_config(**_UpperCAmelCase  )
        lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

        lowercase__				 =							10
        lowercase__				 =							self.dummy_model()
        lowercase__				 =							self.dummy_sample_deter
        scheduler.set_timesteps(_UpperCAmelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        return sample


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )

        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )

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

             lowercase__				 =							self.dummy_sample
             lowercase__				 =							0.1 * sample

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

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

             lowercase__				 =							scheduler.timesteps[5]
             lowercase__				 =							scheduler.timesteps[6]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop()
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_mean.item() - 254_0529  ) < 10




