





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  int			   =   KandinskyVaaControlnetImgaImgPipeline
       _UpperCAmelCase				:  List[Any]			   =   ["image_embeds", "negative_image_embeds", "image", "hint"]
       _UpperCAmelCase				:  Dict			   =   ["image_embeds", "negative_image_embeds", "image", "hint"]
       _UpperCAmelCase				:  Any			   =   [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       _UpperCAmelCase				:  Union[str, Any]			   =   False







       @property
       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          return 3_2







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
          return 3_2







       @property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Union[str, Any]:
          return self.time_input_dim







       @property
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Tuple:
          return self.time_input_dim * 4







       @property
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          return 1_0_0







       @property
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          torch.manual_seed(0      )

          lowerCamelCase__       :						Optional[int]        =							{
              '''in_channels''': 8,
              # Out channels is double in channels because predicts mean and variance
              '''out_channels''': 8,
              '''addition_embed_type''': '''image_hint''',
              '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
              '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
              '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
              '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
              '''layers_per_block''': 1,
              '''encoder_hid_dim''': self.text_embedder_hidden_size,
              '''encoder_hid_dim_type''': '''image_proj''',
              '''cross_attention_dim''': self.cross_attention_dim,
              '''attention_head_dim''': 4,
              '''resnet_time_scale_shift''': '''scale_shift''',
              '''class_embed_type''': None,
          }

          lowerCamelCase__       :						List[str]        =							UNetaDConditionModel(**A      )
          return model







       @property
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          return {
              "block_out_channels": [3_2, 3_2, 6_4, 6_4],
              "down_block_types": [
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "AttnDownEncoderBlock2D",
              ],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 1_2,
              "out_channels": 3,
              "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
              "vq_embed_dim": 4,
          }







       @property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Union[str, Any]:
          torch.manual_seed(0      )
          lowerCamelCase__       :						Union[str, Any]        =							VQModel(**self.dummy_movq_kwargs      )
          return model







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          lowerCamelCase__       :						List[str]        =							self.dummy_unet
          lowerCamelCase__       :						Tuple        =							self.dummy_movq

          lowerCamelCase__       :						int        =							{
              '''num_train_timesteps''': 1_0_0_0,
              '''beta_schedule''': '''linear''',
              '''beta_start''': 0.0_00_85,
              '''beta_end''': 0.0_12,
              '''clip_sample''': False,
              '''set_alpha_to_one''': False,
              '''steps_offset''': 0,
              '''prediction_type''': '''epsilon''',
              '''thresholding''': False,
          }

          lowerCamelCase__       :						Any        =							DDIMScheduler(**A      )

          lowerCamelCase__       :						List[str]        =							{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     List[Any]		, A					:     List[str]=0      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[str]        =							floats_tensor((1, self.text_embedder_hidden_size)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						str        =							floats_tensor((1, self.text_embedder_hidden_size)		, rng=random.Random(seed + 1      )      ).to(
              A      )
          # create init_image
          lowerCamelCase__       :						Tuple        =							floats_tensor((1, 3, 6_4, 6_4)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						int        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
          lowerCamelCase__       :						str        =							Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
          # create hint
          lowerCamelCase__       :						Dict        =							floats_tensor((1, 3, 6_4, 6_4)		, rng=random.Random(A      )      ).to(A      )

          if str(A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						List[Any]        =							torch.manual_seed(A      )
          else:
             lowerCamelCase__       :						Any        =							torch.Generator(device=A      ).manual_seed(A      )
          lowerCamelCase__       :						Tuple        =							{
              '''image''': init_image,
              '''image_embeds''': image_embeds,
              '''negative_image_embeds''': negative_image_embeds,
              '''hint''': hint,
              '''generator''': generator,
              '''height''': 6_4,
              '''width''': 6_4,
              '''num_inference_steps''': 1_0,
              '''guidance_scale''': 7.0,
              '''strength''': 0.2,
              '''output_type''': '''np''',
          }
          return inputs







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          lowerCamelCase__       :						Union[str, Any]        =							'''cpu'''

          lowerCamelCase__       :						Tuple        =							self.get_dummy_components()

          lowerCamelCase__       :						Dict        =							self.pipeline_class(**A      )
          lowerCamelCase__       :						Dict        =							pipe.to(A      )

          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Optional[Any]        =							pipe(**self.get_dummy_inputs(A      )      )
          lowerCamelCase__       :						Tuple        =							output.images

          lowerCamelCase__       :						Tuple        =							pipe(
              **self.get_dummy_inputs(A      )		, return_dict=A		, )[0]

          lowerCamelCase__       :						Union[str, Any]        =							image[0, -3:, -3:, -1]
          lowerCamelCase__       :						List[Any]        =							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 6_4, 6_4, 3)

          lowerCamelCase__       :						Union[str, Any]        =							np.array(
              [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]      )
          assert (
              np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
          ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
          ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     str      )       ->List[str]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Dict        =							load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''      )

          lowerCamelCase__       :						Optional[int]        =							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
          lowerCamelCase__       :						int        =							init_image.resize((5_1_2, 5_1_2)      )

          lowerCamelCase__       :						str        =							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinskyv22/hint_image_cat.png'''      )
          lowerCamelCase__       :						Optional[int]        =							torch.from_numpy(np.array(A      )      ).float() / 2_55.0
          lowerCamelCase__       :						Any        =							hint.permute(2		, 0		, 1      ).unsqueeze(0      )

          lowerCamelCase__       :						str        =							'''A robot, 4k photo'''

          lowerCamelCase__       :						Union[str, Any]        =							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-prior'''		, torch_dtype=torch.floataa      )
          pipe_prior.to(A      )

          lowerCamelCase__       :						Optional[Any]        =							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-controlnet-depth'''		, torch_dtype=torch.floataa      )
          lowerCamelCase__       :						Optional[Any]        =							pipeline.to(A      )

          pipeline.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Optional[Any]        =							torch.Generator(device='''cpu'''      ).manual_seed(0      )

          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[Any]        =							pipe_prior(
              A		, image=A		, strength=0.85		, generator=A		, negative_prompt=''''''		, ).to_tuple()

          lowerCamelCase__       :						List[Any]        =							pipeline(
              image=A		, image_embeds=A		, negative_image_embeds=A		, hint=A		, generator=A		, num_inference_steps=1_0_0		, height=5_1_2		, width=5_1_2		, strength=0.5		, output_type='''np'''		, )

          lowerCamelCase__       :						Tuple        =							output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)

          assert_mean_pixel_difference(A		, A      )










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


_A :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
   lowerCamelCase__       :						List[str]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)
   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Any        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

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

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

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

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

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

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

   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

   lowerCamelCase__       :						str        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(
       torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T					)					)

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

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

      lowerCamelCase__       :						Optional[Any]        =							ly_weight['''self_attention''']
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

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

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

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

   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T					)					)

   return model



def 	_a						(   UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							[
       '''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()''',
   ]

   lowerCamelCase__       :						List[Any]        =							os.path.join(args.checkpoint_path      ,  '''..'''      ,  '''config.gin'''					)
   lowerCamelCase__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

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

   lowerCamelCase__       :						str        =							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'''      ,  )

   lowerCamelCase__       :						int        =							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'''      ,  )

   lowerCamelCase__       :						Optional[int]        =							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      ,  )

   lowerCamelCase__       :						Optional[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

   lowerCamelCase__       :						List[Any]        =							SpectrogramDiffusionPipeline(
       notes_encoder=UpperCAmelCase      ,  continuous_encoder=UpperCAmelCase      ,  decoder=UpperCAmelCase      ,  scheduler=UpperCAmelCase      ,  melgan=UpperCAmelCase      ,  )
   if args.save:
      pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
       _A :   int  			=  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.',
       )
       _A :   Tuple  			=  parser.parse_args()

       main(args)



