


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = (EulerAncestralDiscreteScheduler,)
 SCREAMING_SNAKE_CASE__   = 10




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:Any											= {
           '''num_train_timesteps''': 1100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,						[0.0002, 0.002, 0.02]					):
             self.check_over_configs(beta_start=_lowerCamelCase      ,						beta_end=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_lowerCamelCase					)




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:List[Any]											= self.get_scheduler_config()
       a		:Optional[int]											= scheduler_class(**_lowerCamelCase					)

       scheduler.set_timesteps(self.num_inference_steps					)

       a		:Optional[int]											= torch.manual_seed(0					)

       a		:int											= self.dummy_model()
       a		:Union[str, Any]											= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       a		:Any											= sample.to(_lowerCamelCase					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.scale_model_input(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Tuple											= scheduler.step(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase					)
             a		:Dict											= output.prev_sample

       a		:Union[str, Any]											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:Union[str, Any]											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert abs(result_sum.item() - 152.3192					) < 1e-2
       assert abs(result_mean.item() - 0.1983					) < 1e-3




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= self.scheduler_classes[0]
       a		:Optional[int]											= self.get_scheduler_config(prediction_type='''v_prediction'''					)
       a		:Optional[Any]											= scheduler_class(**_lowerCamelCase					)

       scheduler.set_timesteps(self.num_inference_steps					)

       a		:Optional[int]											= torch.manual_seed(0					)

       a		:Dict											= self.dummy_model()
       a		:Optional[Any]											= self.dummy_sample_deter * scheduler.init_noise_sigma
       a		:int											= sample.to(_lowerCamelCase					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.scale_model_input(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:List[Any]											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:List[str]											= scheduler.step(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase					)
             a		:List[str]											= output.prev_sample

       a		:Tuple											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:Dict											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert abs(result_sum.item() - 108.4439					) < 1e-2
       assert abs(result_mean.item() - 0.1412					) < 1e-3




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= self.scheduler_classes[0]
       a		:List[Any]											= self.get_scheduler_config()
       a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

       scheduler.set_timesteps(self.num_inference_steps      ,						device=_lowerCamelCase					)
       a		:Optional[int]											= torch.manual_seed(0					)

       a		:Any											= self.dummy_model()
       a		:Any											= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       a		:Dict											= sample.to(_lowerCamelCase					)

       for t in scheduler.timesteps:
             a		:Any											= scheduler.scale_model_input(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Tuple											= scheduler.step(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase					)
             a		:Dict											= output.prev_sample

       a		:Optional[Any]											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:str											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert abs(result_sum.item() - 152.3192					) < 1e-2
       assert abs(result_mean.item() - 0.1983					) < 1e-3









from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'MarkupLMFeatureExtractor'
 SCREAMING_SNAKE_CASE__   = ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 SCREAMING_SNAKE_CASE__   = True




 def __call__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = 0      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       # first, create nodes and xpaths
       if self.parse_html:
             if html_strings is None:
                   raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''					)

             if nodes is not None or xpaths is not None or node_labels is not None:
                   raise ValueError(
                       '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''					)

             a		:int											= self.feature_extractor(_lowerCamelCase					)
             a		:Tuple											= features['''nodes''']
             a		:Dict											= features['''xpaths''']
       else:
             if html_strings is not None:
                   raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''					)
             if nodes is None or xpaths is None:
                   raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''					)

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
             if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
                   a		:Tuple											= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       a		:Optional[int]											= self.tokenizer(
           text=questions if questions is not None else nodes      ,						text_pair=nodes if questions is not None else None      ,						xpaths=_lowerCamelCase      ,						node_labels=_lowerCamelCase      ,						add_special_tokens=_lowerCamelCase      ,						padding=_lowerCamelCase      ,						truncation=_lowerCamelCase      ,						max_length=_lowerCamelCase      ,						stride=_lowerCamelCase      ,						pad_to_multiple_of=_lowerCamelCase      ,						return_token_type_ids=_lowerCamelCase      ,						return_attention_mask=_lowerCamelCase      ,						return_overflowing_tokens=_lowerCamelCase      ,						return_special_tokens_mask=_lowerCamelCase      ,						return_offsets_mapping=_lowerCamelCase      ,						return_length=_lowerCamelCase      ,						verbose=_lowerCamelCase      ,						return_tensors=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       return encoded_inputs




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.batch_decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.tokenizer.model_input_names
       return tokenizer_input_names





