

"""simple docstring"""






import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    with open(lowerCAmelCase__   ) as metadata_file:
        _lowerCAmelCase       :   List[Any]  =    json.load(lowerCAmelCase__   )
    _lowerCAmelCase       :   List[str]  =    LukeConfig(use_entity_aware_attention=lowerCAmelCase__ ,   **metadata['model_config']   )

    # Load in the weights from the checkpoint_path
    _lowerCAmelCase       :   List[str]  =    torch.load(lowerCAmelCase__ ,   map_location='cpu'   )['module']

    # Load the entity vocab file
    _lowerCAmelCase       :   Tuple  =    load_original_entity_vocab(lowerCAmelCase__   )
    # add an entry for [MASK2]
    _lowerCAmelCase       :   Union[str, Any]  =    max(entity_vocab.values()   ) + 1
    config.entity_vocab_size += 1

    _lowerCAmelCase       :   int  =    XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']   )

    # Add special tokens to the token vocabulary for downstream tasks
    _lowerCAmelCase       :   Optional[Any]  =    AddedToken('<ent>' ,   lstrip=lowerCAmelCase__ ,   rstrip=lowerCAmelCase__   )
    _lowerCAmelCase       :   List[str]  =    AddedToken('<ent2>' ,   lstrip=lowerCAmelCase__ ,   rstrip=lowerCAmelCase__   )
    tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]}   )
    config.vocab_size += 2

    print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""   )
    tokenizer.save_pretrained(lowerCAmelCase__   )
    with open(os.path.join(lowerCAmelCase__ ,   'tokenizer_config.json'   ) ,   'r'   ) as f:
        _lowerCAmelCase       :   Union[str, Any]  =    json.load(lowerCAmelCase__   )
    _lowerCAmelCase       :   Optional[int]  =    'MLukeTokenizer'
    with open(os.path.join(lowerCAmelCase__ ,   'tokenizer_config.json'   ) ,   'w'   ) as f:
        json.dump(lowerCAmelCase__ ,   lowerCAmelCase__   )

    with open(os.path.join(lowerCAmelCase__ ,   MLukeTokenizer.vocab_files_names['entity_vocab_file']   ) ,   'w'   ) as f:
        json.dump(lowerCAmelCase__ ,   lowerCAmelCase__   )

    _lowerCAmelCase       :   Any  =    MLukeTokenizer.from_pretrained(lowerCAmelCase__   )

    # Initialize the embeddings of the special tokens
    _lowerCAmelCase       :   Any  =    tokenizer.convert_tokens_to_ids(['@']   )[0]
    _lowerCAmelCase       :   Optional[Any]  =    tokenizer.convert_tokens_to_ids(['#']   )[0]

    _lowerCAmelCase       :   int  =    state_dict['embeddings.word_embeddings.weight']
    _lowerCAmelCase       :   Any  =    word_emb[ent_init_index].unsqueeze(0   )
    _lowerCAmelCase       :   Union[str, Any]  =    word_emb[enta_init_index].unsqueeze(0   )
    _lowerCAmelCase       :   Optional[Any]  =    torch.cat([word_emb, ent_emb, enta_emb]   )
    # add special tokens for 'entity_predictions.bias'
    for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
        _lowerCAmelCase       :   str  =    state_dict[bias_name]
        _lowerCAmelCase       :   Dict  =    decoder_bias[ent_init_index].unsqueeze(0   )
        _lowerCAmelCase       :   Any  =    decoder_bias[enta_init_index].unsqueeze(0   )
        _lowerCAmelCase       :   Tuple  =    torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]   )

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers   ):
        for matrix_name in ["query.weight", "query.bias"]:
            _lowerCAmelCase       :   Dict  =    f"""encoder.layer.{layer_index}.attention.self."""
            _lowerCAmelCase       :   Tuple  =    state_dict[prefix + matrix_name]
            _lowerCAmelCase       :   Tuple  =    state_dict[prefix + matrix_name]
            _lowerCAmelCase       :   List[str]  =    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    _lowerCAmelCase       :   str  =    state_dict['entity_embeddings.entity_embeddings.weight']
    _lowerCAmelCase       :   Tuple  =    entity_emb[entity_vocab['[MASK]']].unsqueeze(0   )
    _lowerCAmelCase       :   Union[str, Any]  =    torch.cat([entity_emb, entity_mask_emb]   )
    # add [MASK2] for 'entity_predictions.bias'
    _lowerCAmelCase       :   List[Any]  =    state_dict['entity_predictions.bias']
    _lowerCAmelCase       :   int  =    entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0   )
    _lowerCAmelCase       :   Dict  =    torch.cat([entity_prediction_bias, entity_mask_bias]   )

    _lowerCAmelCase       :   Dict  =    LukeForMaskedLM(config=lowerCAmelCase__   ).eval()

    state_dict.pop('entity_predictions.decoder.weight'   )
    state_dict.pop('lm_head.decoder.weight'   )
    state_dict.pop('lm_head.decoder.bias'   )
    _lowerCAmelCase       :   Tuple  =    OrderedDict()
    for key, value in state_dict.items():
        if not (key.startswith('lm_head'   ) or key.startswith('entity_predictions'   )):
            _lowerCAmelCase       :   List[str]  =    state_dict[key]
        else:
            _lowerCAmelCase       :   str  =    state_dict[key]

    _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    model.load_state_dict(lowerCAmelCase__ ,   strict=lowerCAmelCase__   )

    if set(lowerCAmelCase__   ) != {"luke.embeddings.position_ids"}:
        raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""   )
    if set(lowerCAmelCase__   ) != {
        "lm_head.decoder.weight",
        "lm_head.decoder.bias",
        "entity_predictions.decoder.weight",
    }:
        raise ValueError(f"""Unexpected missing_keys: {missing_keys}"""   )

    model.tie_weights()
    assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
    assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

    # Check outputs
    _lowerCAmelCase       :   int  =    MLukeTokenizer.from_pretrained(lowerCAmelCase__ ,   task='entity_classification'   )

    _lowerCAmelCase       :   int  =    'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
    _lowerCAmelCase       :   List[str]  =    (0, 9)
    _lowerCAmelCase       :   int  =    tokenizer(lowerCAmelCase__ ,   entity_spans=[span] ,   return_tensors='pt'   )

    _lowerCAmelCase       :   List[Any]  =    model(**lowerCAmelCase__   )

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        _lowerCAmelCase       :   List[str]  =    torch.Size((1, 33, 768)   )
        _lowerCAmelCase       :   List[Any]  =    torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]   )

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""   )
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,   lowerCAmelCase__ ,   atol=1e-4   ):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        _lowerCAmelCase       :   str  =    torch.Size((1, 1, 768)   )
        _lowerCAmelCase       :   int  =    torch.tensor([[-0.1482, 0.0609, 0.0322]]   )

    if not (outputs.entity_last_hidden_state.shape == expected_shape):
        raise ValueError(
            f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
            f""" {expected_shape}"""   )
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,   lowerCAmelCase__ ,   atol=1e-4   ):
        raise ValueError

    # Verify masked word/entity prediction
    _lowerCAmelCase       :   Optional[Any]  =    MLukeTokenizer.from_pretrained(lowerCAmelCase__   )
    _lowerCAmelCase       :   List[str]  =    'Tokyo is the capital of <mask>.'
    _lowerCAmelCase       :   Dict  =    (24, 30)
    _lowerCAmelCase       :   Dict  =    tokenizer(lowerCAmelCase__ ,   entity_spans=[span] ,   return_tensors='pt'   )

    _lowerCAmelCase       :   Dict  =    model(**lowerCAmelCase__   )

    _lowerCAmelCase       :   Dict  =    encoding['input_ids'][0].tolist()
    _lowerCAmelCase       :   str  =    input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'   )   )
    _lowerCAmelCase       :   int  =    outputs.logits[0][mask_position_id].argmax(dim=-1   )
    assert "Japan" == tokenizer.decode(lowerCAmelCase__   )

    _lowerCAmelCase       :   List[str]  =    outputs.entity_logits[0][0].argmax().item()
    _lowerCAmelCase       :   Dict  =    [
        entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
    ]
    assert [e for e in multilingual_predicted_entities if e.startswith('en:'   )][0] == "en:Japan"

    # Finally, save our PyTorch model and tokenizer
    print('Saving PyTorch model to {}'.format(lowerCAmelCase__   )   )
    model.save_pretrained(lowerCAmelCase__   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    ['[MASK]', '[PAD]', '[UNK]']

    _lowerCAmelCase       :   str  =    [json.loads(lowerCAmelCase__   ) for line in open(lowerCAmelCase__   )]

    _lowerCAmelCase       :   Optional[int]  =    {}
    for entry in data:
        _lowerCAmelCase       :   Dict  =    entry['id']
        for entity_name, language in entry["entities"]:
            if entity_name in SPECIAL_TOKENS:
                _lowerCAmelCase       :   Dict  =    entity_id
                break
            _lowerCAmelCase       :   Dict  =    f"""{language}:{entity_name}"""
            _lowerCAmelCase       :   Union[str, Any]  =    entity_id
    return new_mapping


if __name__ == "__main__":
    _lowerCAmelCase						=		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
    parser.add_argument(
        """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
    )
    parser.add_argument(
        """--entity_vocab_path""",
        default=None,
        type=str,
        help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
    )
    parser.add_argument(
        """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
    )
    _lowerCAmelCase						=		parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )








"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		(UniPCMultistepScheduler,)
  _UpperCAmelCase          =		(("num_inference_steps", 25),)

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    {
          'num_train_timesteps': 1000,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'solver_order': 2,
          'solver_type': 'bh2',
      }

      config.update(**_A      )
      return config

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   int  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Union[str, Any]  =    0.1 * sample
      _lowerCAmelCase       :   Tuple  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Optional[int]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )
          # copy over dummy past residuals
          _lowerCAmelCase       :   Tuple  =    dummy_past_residuals[: scheduler.config.solver_order]

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

          _lowerCAmelCase,					_lowerCAmelCase       :   str  =    sample, sample
          for t in range(_A    ,time_step + scheduler.config.solver_order + 1      ):
              _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
              _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   List[str]  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Dict  =    0.1 * sample
      _lowerCAmelCase       :   str  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Any  =    self.get_scheduler_config()
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )

          # copy over dummy past residuals (must be after setting timesteps)
          _lowerCAmelCase       :   List[str]  =    dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   int  =    scheduler_class.from_pretrained(_A      )
              # copy over dummy past residuals
              new_scheduler.set_timesteps(_A      )

              # copy over dummy past residual (must be after setting timesteps)
              _lowerCAmelCase       :   str  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if scheduler is None:
          _lowerCAmelCase       :   int  =    self.scheduler_classes[0]
          _lowerCAmelCase       :   List[str]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
      _lowerCAmelCase       :   int  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   List[str]  =    10
      _lowerCAmelCase       :   str  =    self.dummy_model()
      _lowerCAmelCase       :   str  =    self.dummy_sample_deter
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Any  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      return sample

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    dict(self.forward_default_kwargs      )

      _lowerCAmelCase       :   Any  =    kwargs.pop('num_inference_steps'    ,_A      )

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   str  =    self.get_scheduler_config()
          _lowerCAmelCase       :   List[str]  =    scheduler_class(**_A      )

          _lowerCAmelCase       :   Any  =    self.dummy_sample
          _lowerCAmelCase       :   Tuple  =    0.1 * sample

          if num_inference_steps is not None and hasattr(_A    ,'set_timesteps'      ):
              scheduler.set_timesteps(_A      )
          elif num_inference_steps is not None and not hasattr(_A    ,'set_timesteps'      ):
              _lowerCAmelCase       :   Optional[Any]  =    num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _lowerCAmelCase       :   Optional[Any]  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          _lowerCAmelCase       :   Optional[Any]  =    dummy_past_residuals[: scheduler.config.solver_order]

          _lowerCAmelCase       :   Any  =    scheduler.timesteps[5]
          _lowerCAmelCase       :   List[str]  =    scheduler.timesteps[6]

          _lowerCAmelCase       :   List[str]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    UniPCMultistepScheduler(**self.get_scheduler_config()      )
      _lowerCAmelCase       :   Optional[Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

      _lowerCAmelCase       :   int  =    DPMSolverSinglestepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   List[str]  =    DEISMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Tuple  =    DPMSolverMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Any  =    UniPCMultistepScheduler.from_config(scheduler.config      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   List[str]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for timesteps in [25, 50, 100, 999, 1000]:
          self.check_over_configs(num_train_timesteps=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(thresholding=_A      )
      for order in [1, 2, 3]:
          for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=_A    ,prediction_type=_A    ,sample_max_value=_A    ,solver_order=_A    ,solver_type=_A    ,)

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for solver_type in ["bh1", "bh2"]:
          for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  _lowerCAmelCase       :   List[Any]  =    self.full_loop(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  assert not torch.isnan(_A      ).any(), "Samples have nan numbers"

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(lower_order_final=_A      )
      self.check_over_configs(lower_order_final=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
          self.check_over_forward(num_inference_steps=_A    ,time_step=0      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.full_loop()
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.full_loop(prediction_type='v_prediction'      )
      _lowerCAmelCase       :   Union[str, Any]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.1_0_1_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   int  =    self.get_scheduler_config(thresholding=_A    ,dynamic_thresholding_ratio=0      )
      _lowerCAmelCase       :   Tuple  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Optional[Any]  =    10
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_model()
      _lowerCAmelCase       :   Dict  =    self.dummy_sample_deter.half()
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Tuple  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      assert sample.dtype == torch.floataa







  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   str  =    scheduler_class(**_A      )

          scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
          assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps





