


'''simple docstring'''


from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase          =     logging.get_logger(__name__)







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							['input_ids', 'attention_mask']


     def __init__(      self ,    lowerCamelCase="[PAD]" ,    lowerCamelCase="[BOS]" ,    lowerCamelCase="[EOS]" ,    lowerCamelCase="[MASK]" ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase=2048 ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else pad_token
          __A  :					Optional[int]						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else bos_token
          __A  :					List[str]						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else eos_token
          __A  :					List[str]						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else mask_token
          __A  :					int						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else cls_token
          __A  :					Dict						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else sep_token

          super().__init__(
              pad_token=lowerCamelCase ,    bos_token=lowerCamelCase ,    eos_token=lowerCamelCase ,    mask_token=lowerCamelCase ,    cls_token=lowerCamelCase ,    sep_token=lowerCamelCase ,    model_max_length=lowerCamelCase ,    **lowerCamelCase ,    )

          __A  :					str						     =       2**8  # utf is 8 bits

          # define special tokens dict
          __A  :					Dict[str, int]						     =       {
              self.pad_token: 0,
              self.bos_token: 1,
              self.eos_token: 2,
              self.mask_token: 3,
              self.cls_token: 4,
              self.sep_token: 5,
          }
          __A  :					Optional[Any]						     =       len(self.special_tokens_encoder		)
          __A  :					Dict[int, str]						     =       {v: k for k, v in self.special_tokens_encoder.items()}


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Dict						     =       self.special_tokens_encoder.copy()
          vocab.update(self.added_tokens_encoder		)
          for i in range(self._utf_vocab_size		):
               __A  :					Optional[int]						     =       chr(lowerCamelCase		)
               __A  :					Dict						     =       i + len(self.special_tokens_encoder		)
          return vocab


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return self._utf_vocab_size + self._num_special_tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = False		):


          '''simple docstring'''





          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase ,    token_ids_a=lowerCamelCase ,    already_has_special_tokens=lowerCamelCase		)

          # normal case: some special tokens
          if token_ids_a is None:
               return [1] + [0] * len(lowerCamelCase		) + [1]
          return [1] + ([0] * len(lowerCamelCase		)) + [1] + ([0] * len(lowerCamelCase		)) + [1]


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          else:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       [chr(lowerCamelCase		) for i in text.encode("utf-8"		)]
          return tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if token in self.special_tokens_encoder:
               __A  :					str						     =       self.special_tokens_encoder[token]
          elif token in self.added_tokens_encoder:
               __A  :					Optional[Any]						     =       self.added_tokens_encoder[token]
          elif len(lowerCamelCase		) != 1:
               __A  :					List[str]						     =       self.unk_token_id
          else:
               __A  :					List[str]						     =       ord(lowerCamelCase		) + self._num_special_tokens
          return token_id


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if index in self.special_tokens_decoder:
               __A  :					List[Any]						     =       self.special_tokens_decoder[index]
          elif index in self.added_tokens_decoder:
               __A  :					str						     =       self.added_tokens_decoder[index]
          else:
               __A  :					int						     =       chr(index - self._num_special_tokens		)
          return token


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       b""
          for token in tokens:
               if token in self.special_tokens_decoder:
                    __A  :					Dict						     =       self.special_tokens_decoder[token].encode("utf-8"		)
               elif token in self.added_tokens_decoder:
                    __A  :					List[str]						     =       self.special_tokens_decoder[token].encode("utf-8"		)
               elif token in self.special_tokens_encoder:
                    __A  :					Optional[Any]						     =       token.encode("utf-8"		)
               elif token in self.added_tokens_encoder:
                    __A  :					Optional[int]						     =       token.encode("utf-8"		)
               else:
                    __A  :					Optional[Any]						     =       bytes([ord(lowerCamelCase		)]		)
               bstring += tok_string
          __A  :					Optional[int]						     =       bstring.decode("utf-8" ,    errors="replace"		)
          return string


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          return ()





'''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		)

