

import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
    import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)

lowerCAmelCase     :   List[Any]						     =  {'vocab_file': 'vocab.txt'}

lowerCAmelCase     :   Union[str, Any]						     =  {
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

lowerCAmelCase     :   Optional[int]						     =  {
    'openbmb/cpm-ant-10b': 10_24,
}

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     collections.OrderedDict()
      with open(a					,   'r'					,   encoding='utf-8'  ) as reader:
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     reader.readlines()
      for index, token in enumerate(a  ):
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     token.rstrip('\n'  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     index
      return vocab
class  _A		(  __magic_name__):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE="<unk>" ,					_SCREAMING_SNAKE_CASE=200					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     vocab
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     unk_token
        SCREAMING_SNAKE_CASE_				:  Tuple           =     max_input_chars_per_word



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     list(_SCREAMING_SNAKE_CASE					)
        if len(_SCREAMING_SNAKE_CASE					) > self.max_input_chars_per_word:
              return [self.unk_token]

        SCREAMING_SNAKE_CASE_				:  Dict           =     0
        SCREAMING_SNAKE_CASE_				:  Dict           =     []
        while start < len(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     len(_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  int           =     None
              while start < end:
                    SCREAMING_SNAKE_CASE_				:  str           =     ''.join(chars[start:end]					)
                    if substr in self.vocab:
                          SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     substr
                          break
                    end -= 1
              if cur_substr is None:
                    sub_tokens.append(self.unk_token					)
                    start += 1
              else:
                    sub_tokens.append(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  int           =     end

        return sub_tokens

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :      int       =  PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :      int       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  ['''input_ids''', '''attention_mask''']
  SCREAMING_SNAKE_CASE    :      int       =  False

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE="<d>" ,					_SCREAMING_SNAKE_CASE="</d>" ,					_SCREAMING_SNAKE_CASE="<s>" ,					_SCREAMING_SNAKE_CASE="</s>" ,					_SCREAMING_SNAKE_CASE="<pad>" ,					_SCREAMING_SNAKE_CASE="<unk>" ,					_SCREAMING_SNAKE_CASE="</n>" ,					_SCREAMING_SNAKE_CASE="</_>" ,					_SCREAMING_SNAKE_CASE="left" ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        requires_backends(self ,					['jieba']					)
        super().__init__(
            bod_token=_SCREAMING_SNAKE_CASE ,					eod_token=_SCREAMING_SNAKE_CASE ,					bos_token=_SCREAMING_SNAKE_CASE ,					eos_token=_SCREAMING_SNAKE_CASE ,					pad_token=_SCREAMING_SNAKE_CASE ,					unk_token=_SCREAMING_SNAKE_CASE ,					line_token=_SCREAMING_SNAKE_CASE ,					space_token=_SCREAMING_SNAKE_CASE ,					padding_side=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE ,					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     bod_token
        SCREAMING_SNAKE_CASE_				:  List[str]           =     eod_token
        SCREAMING_SNAKE_CASE_				:  Tuple           =     load_vocab(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.encoder[space_token]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.encoder[line_token]

        del self.encoder[space_token]
        del self.encoder[line_token]

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     collections.OrderedDict(sorted(self.encoder.items() ,					key=lambda _SCREAMING_SNAKE_CASE					: x[1]					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {v: k for k, v in self.encoder.items()}

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     WordpieceTokenizer(vocab=self.encoder ,					unk_token=self.unk_token					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.encoder[self.bod_token]

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.encoder[self.eod_token]

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.encoder["\n"]

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return len(self.encoder					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return dict(self.encoder ,					**self.added_tokens_encoder					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     []
        for x in jieba.cut(_SCREAMING_SNAKE_CASE ,					cut_all=_SCREAMING_SNAKE_CASE					):
              output_tokens.extend(self.wordpiece_tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)					)
        return output_tokens

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     [i for i in token_ids if i >= 0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     [
            x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
        ]
        return super()._decode(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return token in self.encoder

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return "".join(_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return self.encoder.get(_SCREAMING_SNAKE_CASE ,					self.encoder.get(self.unk_token					)					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return self.decoder.get(_SCREAMING_SNAKE_CASE ,					self.unk_token					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        if os.path.isdir(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  Any           =     os.path.join(
                  _SCREAMING_SNAKE_CASE ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
        else:
              SCREAMING_SNAKE_CASE_				:  int           =     (filename_prefix + '-' if filename_prefix else '') + save_directory
        SCREAMING_SNAKE_CASE_				:  Any           =     0
        if " " in self.encoder:
              SCREAMING_SNAKE_CASE_				:  str           =     self.encoder[' ']
              del self.encoder[" "]
        if "\n" in self.encoder:
              SCREAMING_SNAKE_CASE_				:  Any           =     self.encoder['\n']
              del self.encoder["\n"]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     collections.OrderedDict(sorted(self.encoder.items() ,					key=lambda _SCREAMING_SNAKE_CASE					: x[1]					)					)
        with open(_SCREAMING_SNAKE_CASE ,					'w' ,					encoding='utf-8'					) as writer:
              for token, token_index in self.encoder.items():
                    if index != token_index:
                          logger.warning(
                              f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                              ' Please check that the vocabulary is not corrupted!'					)
                          SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     token_index
                    writer.write(token + '\n'					)
                    index += 1
        return (vocab_file,)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        if token_ids_a is None:
              return [self.bos_token_id] + token_ids_a
        return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = False					):





        """simple docstring"""







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

        if token_ids_a is not None:
              return [1] + ([0] * len(_SCREAMING_SNAKE_CASE					)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE					))
        return [1] + ([0] * len(_SCREAMING_SNAKE_CASE					))







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  (PNDMScheduler,)
  SCREAMING_SNAKE_CASE    :      Dict       =  (('''num_inference_steps''', 50),)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_SCREAMING_SNAKE_CASE					)
        return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Any           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Any           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  int           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

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

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

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

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  List[str]           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     10
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.prk_timesteps					):
              SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps					):
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        return sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  int           =     0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_inference_steps

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

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.get_scheduler_config(steps_offset=1					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
        scheduler.set_timesteps(10					)
        assert torch.equal(
            scheduler.timesteps ,					torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					) ,					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for beta_start, beta_end in zip([0.0001, 0.001] ,					[0.002, 0.02]					):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE ,					beta_end=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t in [1, 5, 10]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     27

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  int           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample

              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                    SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  str           =     self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.step_plms(self.dummy_sample ,					1 ,					self.dummy_sample					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 198.1318					) < 1e-2
        assert abs(result_mean.item() - 0.2580					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.full_loop(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 67.3986					) < 1e-2
        assert abs(result_mean.item() - 0.0878					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 230.0399					) < 1e-2
        assert abs(result_mean.item() - 0.2995					) < 1e-3



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 186.9482					) < 1e-2
        assert abs(result_mean.item() - 0.2434					) < 1e-3






