


"""simple docstring"""



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase       (   UpperCamelCase_			):
   __UpperCAmelCase				: int													=						(DDPMParallelScheduler,)







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

       '''simple docstring'''




       snake_case					:   Dict         =					{
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''variance_type''': '''fixed_small''',
           '''clip_sample''': True,
       }

       config.update(**UpperCamelCase__		)
       return config







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







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

       '''simple docstring'''




       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,			[0.002, 0.02, 0.2, 2]		):
           self.check_over_configs(beta_start=UpperCamelCase__		,			beta_end=UpperCamelCase__		)







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

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=UpperCamelCase__		)







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

       '''simple docstring'''




       self.check_over_configs(thresholding=UpperCamelCase__		)
       for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(
                   thresholding=UpperCamelCase__		,			prediction_type=UpperCamelCase__		,			sample_max_value=UpperCamelCase__		,			)







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

       '''simple docstring'''




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







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

       '''simple docstring'''




       for t in [0, 500, 999]:
           self.check_over_forward(time_step=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   Dict         =					self.scheduler_classes[0]
       snake_case					:   Optional[Any]         =					self.get_scheduler_config()
       snake_case					:   Tuple         =					scheduler_class(**UpperCamelCase__		)

       assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00979		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1e-5







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

       '''simple docstring'''




       snake_case					:   str         =					self.scheduler_classes[0]
       snake_case					:   Tuple         =					self.get_scheduler_config()
       snake_case					:   Union[str, Any]         =					scheduler_class(**UpperCamelCase__		)

       snake_case					:   Dict         =					len(UpperCamelCase__		)

       snake_case					:   Any         =					self.dummy_model()
       snake_case					:   Tuple         =					self.dummy_sample_deter
       snake_case					:   List[Any]         =					self.dummy_sample_deter + 0.1
       snake_case					:   Optional[int]         =					self.dummy_sample_deter - 0.1

       snake_case					:   Tuple         =					samplea.shape[0]
       snake_case					:   Tuple         =					torch.stack([samplea, samplea, samplea]		,			dim=0		)
       snake_case					:   Dict         =					torch.arange(UpperCamelCase__		)[0:3, None].repeat(1		,			UpperCamelCase__		)

       snake_case					:   Tuple         =					model(samples.flatten(0		,			1		)		,			timesteps.flatten(0		,			1		)		)
       snake_case					:   List[Any]         =					scheduler.batch_step_no_noise(UpperCamelCase__		,			timesteps.flatten(0		,			1		)		,			samples.flatten(0		,			1		)		)

       snake_case					:   List[str]         =					torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case					:   Any         =					torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 1153.1833		) < 1e-2
       assert abs(result_mean.item() - 0.5005		) < 1e-3







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




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

       snake_case					:   List[str]         =					len(UpperCamelCase__		)

       snake_case					:   List[Any]         =					self.dummy_model()
       snake_case					:   List[Any]         =					self.dummy_sample_deter
       snake_case					:   Tuple         =					torch.manual_seed(0		)

       for t in reversed(range(UpperCamelCase__		)		):
           # 1. predict noise residual
           snake_case					:   Tuple         =					model(UpperCamelCase__		,			UpperCamelCase__		)

           # 2. predict previous mean of sample x_t-1
           snake_case					:   Optional[int]         =					scheduler.step(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			generator=UpperCamelCase__		).prev_sample

           snake_case					:   Tuple         =					pred_prev_sample

       snake_case					:   Optional[int]         =					torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case					:   Any         =					torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 258.9606		) < 1e-2
       assert abs(result_mean.item() - 0.3372		) < 1e-3







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   str         =					self.scheduler_classes[0]
       snake_case					:   Any         =					self.get_scheduler_config(prediction_type="v_prediction"		)
       snake_case					:   Dict         =					scheduler_class(**UpperCamelCase__		)

       snake_case					:   List[str]         =					len(UpperCamelCase__		)

       snake_case					:   List[Any]         =					self.dummy_model()
       snake_case					:   Optional[Any]         =					self.dummy_sample_deter
       snake_case					:   Union[str, Any]         =					torch.manual_seed(0		)

       for t in reversed(range(UpperCamelCase__		)		):
           # 1. predict noise residual
           snake_case					:   int         =					model(UpperCamelCase__		,			UpperCamelCase__		)

           # 2. predict previous mean of sample x_t-1
           snake_case					:   Optional[Any]         =					scheduler.step(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			generator=UpperCamelCase__		).prev_sample

           snake_case					:   Any         =					pred_prev_sample

       snake_case					:   str         =					torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case					:   Tuple         =					torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 202.0296		) < 1e-2
       assert abs(result_mean.item() - 0.2631		) < 1e-3







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




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

       snake_case					:   int         =					[100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					scheduler.timesteps

       for i, timestep in enumerate(UpperCamelCase__		):
           if i == len(UpperCamelCase__		) - 1:
               snake_case					:   Optional[int]         =					-1
           else:
               snake_case					:   str         =					timesteps[i + 1]

           snake_case					:   int         =					scheduler.previous_timestep(UpperCamelCase__		)
           snake_case					:   List[Any]         =					prev_t.item()

           self.assertEqual(UpperCamelCase__		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   Tuple         =					self.scheduler_classes[0]
       snake_case					:   List[Any]         =					self.get_scheduler_config()
       snake_case					:   Optional[Any]         =					scheduler_class(**UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					[100, 87, 50, 51, 0]

       with self.assertRaises(UpperCamelCase__		,			msg="`custom_timesteps` must be in descending order."		):
           scheduler.set_timesteps(timesteps=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   str         =					self.scheduler_classes[0]
       snake_case					:   Any         =					self.get_scheduler_config()
       snake_case					:   Union[str, Any]         =					scheduler_class(**UpperCamelCase__		)

       snake_case					:   List[Any]         =					[100, 87, 50, 1, 0]
       snake_case					:   Dict         =					len(UpperCamelCase__		)

       with self.assertRaises(UpperCamelCase__		,			msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
           scheduler.set_timesteps(num_inference_steps=UpperCamelCase__		,			timesteps=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.scheduler_classes[0]
       snake_case					:   List[str]         =					self.get_scheduler_config()
       snake_case					:   int         =					scheduler_class(**UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					[scheduler.config.num_train_timesteps]

       with self.assertRaises(
           UpperCamelCase__		,			msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,			):
           scheduler.set_timesteps(timesteps=UpperCamelCase__		)









"""simple docstring"""



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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """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 _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''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__		,			)			->	Any:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)
       snake_case					:   List[str]         =					vocab_size
       snake_case					:   Optional[int]         =					hidden_size
       snake_case					:   List[str]         =					num_hidden_layers
       snake_case					:   Union[str, Any]         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Tuple         =					intermediate_size
       snake_case					:   int         =					hidden_dropout_prob
       snake_case					:   Tuple         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Optional[Any]         =					type_vocab_size
       snake_case					:   Any         =					initializer_range
       snake_case					:   Union[str, Any]         =					layer_norm_eps
       snake_case					:   List[str]         =					position_embedding_type
       snake_case					:   Optional[Any]         =					use_cache
       snake_case					:   str         =					classifier_dropout
       # additional properties
       snake_case					:   Optional[int]         =					max_depth
       snake_case					:   Optional[int]         =					max_xpath_tag_unit_embeddings
       snake_case					:   str         =					max_xpath_subs_unit_embeddings
       snake_case					:   int         =					tag_pad_id
       snake_case					:   Dict         =					subs_pad_id
       snake_case					:   List[str]         =					xpath_unit_hidden_size





