


'''simple docstring'''


from typing import Optional, Union

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







class        __magic_name__  (					__SCREAMING_SNAKE_CASE   ):

     """simple docstring"""






     lowerCamelCase__						=							'MarkupLMFeatureExtractor'
     lowerCamelCase__						=							('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
     lowerCamelCase__						=							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 ,    ):


          '''simple docstring'''





          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(UpperCamelCase__		)
               __A  :					List[str]						     =       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(UpperCamelCase__ ,    UpperCamelCase__		):
                    __A  :					Dict						     =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

          return encoded_inputs


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return self.tokenizer.batch_decode(*UpperCamelCase__ ,    **UpperCamelCase__		)


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return self.tokenizer.decode(*UpperCamelCase__ ,    **UpperCamelCase__		)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       self.tokenizer.model_input_names
          return tokenizer_input_names









'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class        __magic_name__  (					unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							(ScoreSdeVeScheduler,)
     lowerCamelCase__						=							()


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       4
          __A  :					Union[str, Any]						     =       3
          __A  :					Optional[int]						     =       8
          __A  :					Dict						     =       8

          __A  :					Tuple						     =       torch.rand((batch_size, num_channels, height, width)		)

          return sample


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Tuple						     =       4
          __A  :					Dict						     =       3
          __A  :					str						     =       8
          __A  :					List[Any]						     =       8

          __A  :					Dict						     =       batch_size * num_channels * height * width
          __A  :					List[str]						     =       torch.arange(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       sample.reshape(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[Any]						     =       sample / num_elems
          __A  :					Dict						     =       sample.permute(3 ,    0 ,    1 ,    2		)

          return sample


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          def model(lowerCamelCase ,    lowerCamelCase ,    *lowerCamelCase		):
               return sample * t / (t + 1)

          return model


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1E-5,
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       dict(self.forward_default_kwargs		)

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

               __A  :					Any						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Tuple						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					List[str]						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[str]						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					Dict						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[Any]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       dict(self.forward_default_kwargs		)
          kwargs.update(lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					Optional[Any]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               __A  :					str						     =       self.get_scheduler_config()
               __A  :					Optional[Any]						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					Dict						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					str						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					List[str]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01] ,    [1, 100, 1000]		):
               self.check_over_configs(sigma_min=lowerCamelCase ,    sigma_max=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

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

          __A  :					Union[str, Any]						     =       3

          __A  :					Union[str, Any]						     =       self.dummy_model()
          __A  :					Union[str, Any]						     =       self.dummy_sample_deter

          scheduler.set_sigmas(lowerCamelCase		)
          scheduler.set_timesteps(lowerCamelCase		)
          __A  :					Optional[int]						     =       torch.manual_seed(0		)

          for i, t in enumerate(scheduler.timesteps		):
               __A  :					int						     =       scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps		):
                    with torch.no_grad():
                         __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)
                    __A  :					Optional[int]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		).prev_sample

               with torch.no_grad():
                    __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		)
               __A     ,__A  :					int						     =       output.prev_sample, output.prev_sample_mean

          __A  :					Union[str, Any]						     =       torch.sum(torch.abs(lowerCamelCase		)		)
          __A  :					str						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert np.isclose(result_sum.item() ,    143_7275_8528.0		)
          assert np.isclose(result_mean.item() ,    1871_4530.0		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					List[Any]						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)

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

               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCamelCase ,    "set_timesteps"		):
                    scheduler.set_timesteps(lowerCamelCase		)
               elif num_inference_steps is not None and not hasattr(lowerCamelCase ,    "set_timesteps"		):
                    __A  :					Any						     =       num_inference_steps

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    0 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       scheduler.step_pred(lowerCamelCase ,    1 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

