

import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


lowerCAmelCase     :   Optional[Any]						     =  'base_with_context'

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']  )  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     nn.Parameter(
          torch.FloatTensor(weights['Embed_0']['embedding']  )					,   requires_grad=a  )
      for lyr_num, lyr in enumerate(model.encoders  ):
            SCREAMING_SNAKE_CASE_				:  Tuple           =     weights[f"layers_{lyr_num}"]
            SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(
                torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']  )  )

            SCREAMING_SNAKE_CASE_				:  List[Any]           =     ly_weight['attention']
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Any           =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )

            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']  )  )

            SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Tuple           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Any           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T  )  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']  )  )
      return model

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T  )  )

      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     nn.Parameter(
          torch.FloatTensor(weights['Embed_0']['embedding']  )					,   requires_grad=a  )

      for lyr_num, lyr in enumerate(model.encoders  ):
            SCREAMING_SNAKE_CASE_				:  Any           =     weights[f"layers_{lyr_num}"]
            SCREAMING_SNAKE_CASE_				:  Tuple           =     ly_weight['attention']

            SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  str           =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     nn.Parameter(
                torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']  )  )

            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']  )  )

      SCREAMING_SNAKE_CASE_				:  List[Any]           =     nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']  )  )

      return model

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Any           =     nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T  )  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T  )  )

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     nn.Parameter(
          torch.FloatTensor(weights['Embed_0']['embedding']  )					,   requires_grad=a  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     nn.Parameter(
          torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T  )  )

      for lyr_num, lyr in enumerate(model.decoders  ):
            SCREAMING_SNAKE_CASE_				:  List[str]           =     weights[f"layers_{lyr_num}"]
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     nn.Parameter(
                torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']  )  )

            SCREAMING_SNAKE_CASE_				:  List[Any]           =     nn.Parameter(
                torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T  )  )

            SCREAMING_SNAKE_CASE_				:  Any           =     ly_weight['self_attention']
            SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Any           =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Tuple           =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  str           =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )

            SCREAMING_SNAKE_CASE_				:  str           =     ly_weight['MultiHeadDotProductAttention_0']
            SCREAMING_SNAKE_CASE_				:  Dict           =     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Any           =     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(
                torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']  )  )

            SCREAMING_SNAKE_CASE_				:  Tuple           =     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']  )  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     nn.Parameter(
                torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  int           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Tuple           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T  )  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T  )  )

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']  )  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T  )  )

      return model

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     checkpoints.load_tax_checkpoint(args.checkpoint_path  )
      SCREAMING_SNAKE_CASE_				:  Any           =     jnp.tree_util.tree_map(onp.array					,   a  )

      SCREAMING_SNAKE_CASE_				:  Any           =     [
          'from __gin__ import dynamic_registration',
          'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
          'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
          'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
      ]

      SCREAMING_SNAKE_CASE_				:  Tuple           =     os.path.join(args.checkpoint_path					,   '..'					,   'config.gin'  )
      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     inference.parse_training_gin_file(a					,   a  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     inference.InferenceModel(args.checkpoint_path					,   a  )

      SCREAMING_SNAKE_CASE_				:  int           =     DDPMScheduler(beta_schedule='squaredcos_cap_v2'					,   variance_type='fixed_large'  )

      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     SpectrogramNotesEncoder(
          max_length=synth_model.sequence_length['inputs']					,   vocab_size=synth_model.model.module.config.vocab_size					,   d_model=synth_model.model.module.config.emb_dim					,   dropout_rate=synth_model.model.module.config.dropout_rate					,   num_layers=synth_model.model.module.config.num_encoder_layers					,   num_heads=synth_model.model.module.config.num_heads					,   d_kv=synth_model.model.module.config.head_dim					,   d_ff=synth_model.model.module.config.mlp_dim					,   feed_forward_proj='gated-gelu'					,   )

      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     SpectrogramContEncoder(
          input_dims=synth_model.audio_codec.n_dims					,   targets_context_length=synth_model.sequence_length['targets_context']					,   d_model=synth_model.model.module.config.emb_dim					,   dropout_rate=synth_model.model.module.config.dropout_rate					,   num_layers=synth_model.model.module.config.num_encoder_layers					,   num_heads=synth_model.model.module.config.num_heads					,   d_kv=synth_model.model.module.config.head_dim					,   d_ff=synth_model.model.module.config.mlp_dim					,   feed_forward_proj='gated-gelu'					,   )

      SCREAMING_SNAKE_CASE_				:  Any           =     TaFilmDecoder(
          input_dims=synth_model.audio_codec.n_dims					,   targets_length=synth_model.sequence_length['targets_context']					,   max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time					,   d_model=synth_model.model.module.config.emb_dim					,   num_layers=synth_model.model.module.config.num_decoder_layers					,   num_heads=synth_model.model.module.config.num_heads					,   d_kv=synth_model.model.module.config.head_dim					,   d_ff=synth_model.model.module.config.mlp_dim					,   dropout_rate=synth_model.model.module.config.dropout_rate					,   )

      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     load_notes_encoder(ta_checkpoint['target']['token_encoder']					,   a  )
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']					,   a  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     load_decoder(ta_checkpoint['target']['decoder']					,   a  )

      SCREAMING_SNAKE_CASE_				:  List[Any]           =     OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'  )

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     SpectrogramDiffusionPipeline(
          notes_encoder=a					,   continuous_encoder=a					,   decoder=a					,   scheduler=a					,   melgan=a					,   )
      if args.save:
            pipe.save_pretrained(args.output_path  )


if __name__ == "__main__":
    lowerCAmelCase     :   Tuple						     =  argparse.ArgumentParser()

    parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
    parser.add_argument(
        '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
    )
    parser.add_argument(
        '--checkpoint_path',
        default=F'{MODEL}/checkpoint_500000',
        type=str,
        required=False,
        help='Path to the original jax model checkpoint.',
    )
    lowerCAmelCase     :   Dict						     =  parser.parse_args()

    main(args)









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






