




"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Dict     				=     ShapEImgaImgPipeline
   _lowerCamelCase    :Union[str, Any]     				=     ["image"]
   _lowerCamelCase    :Tuple     				=     ["image"]
   _lowerCamelCase    :List[Any]     				=     [
       "num_images_per_prompt",
       "num_inference_steps",
       "generator",
       "latents",
       "guidance_scale",
       "frame_size",
       "output_type",
       "return_dict",
   ]
   _lowerCamelCase    :int     				=     False






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         return 32






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						str:

         """simple docstring"""
         return 32






   @property
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[Any]:

         """simple docstring"""
         return self.time_input_dim * 4






   @property
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         return 8






   @property
   def 	_lowerCAmelCase   (		self       :		str   )			->						Union[str, Any]:

         """simple docstring"""
         torch.manual_seed(0   )
         lowerCAmelCase__      :			str   =	CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size   ,     image_size=64   ,     projection_dim=self.text_embedder_hidden_size   ,     intermediate_size=37   ,     num_attention_heads=4   ,     num_channels=3   ,     num_hidden_layers=5   ,     patch_size=1   ,     )

         lowerCAmelCase__      :			Any   =	CLIPVisionModel(UpperCamelCase   )
         return model






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	CLIPImageProcessor(
             crop_size=2_24   ,     do_center_crop=UpperCamelCase   ,     do_normalize=UpperCamelCase   ,     do_resize=UpperCamelCase   ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]   ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]   ,     resample=3   ,     size=2_24   ,     )

         return image_processor






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Optional[int]:

         """simple docstring"""
         torch.manual_seed(0   )

         lowerCAmelCase__      :			Dict   =	{
             """num_attention_heads""": 2,
             """attention_head_dim""": 16,
             """embedding_dim""": self.time_input_dim,
             """num_embeddings""": 32,
             """embedding_proj_dim""": self.text_embedder_hidden_size,
             """time_embed_dim""": self.time_embed_dim,
             """num_layers""": 1,
             """clip_embed_dim""": self.time_input_dim * 2,
             """additional_embeddings""": 0,
             """time_embed_act_fn""": """gelu""",
             """norm_in_type""": """layer""",
             """embedding_proj_norm_type""": """layer""",
             """encoder_hid_proj_type""": None,
             """added_emb_type""": None,
         }

         lowerCAmelCase__      :			Any   =	PriorTransformer(**UpperCamelCase   )
         return model






   @property
   def 	_lowerCAmelCase   (		self       :		str   )			->						Optional[Any]:

         """simple docstring"""
         torch.manual_seed(0   )

         lowerCAmelCase__      :			Dict   =	{
             """param_shapes""": (
                 (self.renderer_dim, 93),
                 (self.renderer_dim, 8),
                 (self.renderer_dim, 8),
                 (self.renderer_dim, 8),
             ),
             """d_latent""": self.time_input_dim,
             """d_hidden""": self.renderer_dim,
             """n_output""": 12,
             """background""": (
                 0.1,
                 0.1,
                 0.1,
             ),
         }
         lowerCAmelCase__      :			str   =	ShapERenderer(**UpperCamelCase   )
         return model






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.dummy_prior
         lowerCAmelCase__      :			Tuple   =	self.dummy_image_encoder
         lowerCAmelCase__      :			Optional[int]   =	self.dummy_image_processor
         lowerCAmelCase__      :			Optional[int]   =	self.dummy_renderer

         lowerCAmelCase__      :			Tuple   =	HeunDiscreteScheduler(
             beta_schedule="""exp"""   ,     num_train_timesteps=10_24   ,     prediction_type="""sample"""   ,     use_karras_sigmas=UpperCamelCase   ,     clip_sample=UpperCamelCase   ,     clip_sample_range=1.0   ,     )
         lowerCAmelCase__      :			Union[str, Any]   =	{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """image_processor""": image_processor,
             """renderer""": renderer,
             """scheduler""": scheduler,
         }

         return components






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Optional[int]=0   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	floats_tensor((1, 3, 64, 64)   ,     rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )

         if str(UpperCamelCase   ).startswith("""mps"""   ):
               lowerCAmelCase__      :			int   =	torch.manual_seed(UpperCamelCase   )
         else:
               lowerCAmelCase__      :			Union[str, Any]   =	torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{
             """image""": input_image,
             """generator""": generator,
             """num_inference_steps""": 1,
             """frame_size""": 32,
             """output_type""": """np""",
         }
         return inputs






   def 	_lowerCAmelCase   (		self       :		str   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	"""cpu"""

         lowerCAmelCase__      :			Tuple   =	self.get_dummy_components()

         lowerCAmelCase__      :			List[Any]   =	self.pipeline_class(**UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	pipe.to(UpperCamelCase   )

         pipe.set_progress_bar_config(disable=UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	pipe(**self.get_dummy_inputs(UpperCamelCase   )   )
         lowerCAmelCase__      :			Union[str, Any]   =	output.images[0]
         lowerCAmelCase__      :			Optional[Any]   =	image[0, -3:, -3:, -1]

         assert image.shape == (20, 32, 32, 3)

         lowerCAmelCase__      :			List[Any]   =	np.array(
             [
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
             ]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2






   def 	_lowerCAmelCase   (		self       :		str   )			->						Tuple:

         """simple docstring"""
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	torch_device == """cpu"""
         lowerCAmelCase__      :			List[Any]   =	True
         self._test_inference_batch_single_identical(
             batch_size=2   ,     test_max_difference=UpperCamelCase   ,     relax_max_difference=UpperCamelCase   ,     )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.get_dummy_components()
         lowerCAmelCase__      :			Any   =	self.pipeline_class(**UpperCamelCase   )
         lowerCAmelCase__      :			str   =	pipe.to(UpperCamelCase   )
         pipe.set_progress_bar_config(disable=UpperCamelCase   )

         lowerCAmelCase__      :			List[Any]   =	1
         lowerCAmelCase__      :			Optional[Any]   =	2

         lowerCAmelCase__      :			List[str]   =	self.get_dummy_inputs(UpperCamelCase   )

         for key in inputs.keys():
               if key in self.batch_params:
                     lowerCAmelCase__      :			Any   =	batch_size * [inputs[key]]

         lowerCAmelCase__      :			str   =	pipe(**UpperCamelCase   ,     num_images_per_prompt=UpperCamelCase   )[0]

         assert images.shape[0] == batch_size * num_images_per_prompt






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






   def 	_lowerCAmelCase   (		self       :		int   )			->						int:

         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""   )
         lowerCAmelCase__      :			Dict   =	load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/shap_e/test_shap_e_img2img_out.npy"""   )
         lowerCAmelCase__      :			List[Any]   =	ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""   )
         lowerCAmelCase__      :			str   =	pipe.to(UpperCamelCase   )
         pipe.set_progress_bar_config(disable=UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	torch.Generator(device=UpperCamelCase   ).manual_seed(0   )

         lowerCAmelCase__      :			str   =	pipe(
             UpperCamelCase   ,     generator=UpperCamelCase   ,     guidance_scale=3.0   ,     num_inference_steps=64   ,     frame_size=64   ,     output_type="""np"""   ,     ).images[0]

         assert images.shape == (20, 64, 64, 3)

         assert_mean_pixel_difference(UpperCamelCase   ,     UpperCamelCase   )











"""simple docstring"""


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													=      """base_with_context"""



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Dict:
      lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]				)				)
      lowerCAmelCase__      :			int   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)
      for lyr_num, lyr in enumerate(model.encoders				):
            lowerCAmelCase__      :			str   =	weights[f"""layers_{lyr_num}"""]
            lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			str   =	ly_weight["""attention"""]
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[Any]   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	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_mlp_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			List[Any]   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)

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

            lowerCAmelCase__      :			int   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Any   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Any   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	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[int]   =	nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]				)				)

      lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]				)				)

      return model



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T				)				)
      lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			List[str]   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)

      lowerCAmelCase__      :			Dict   =	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__      :			Tuple   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T				)				)

            lowerCAmelCase__      :			Tuple   =	ly_weight["""self_attention"""]
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)

            lowerCAmelCase__      :			List[Any]   =	ly_weight["""MultiHeadDotProductAttention_0"""]
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Any   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	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__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]				)				)

      lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T				)				)

      return model



def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			Optional[int]   =	checkpoints.load_tax_checkpoint(args.checkpoint_path				)
      lowerCAmelCase__      :			Optional[int]   =	jnp.tree_util.tree_map(onp.array    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			Union[str, 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()""",
      ]

      lowerCAmelCase__      :			Dict   =	os.path.join(args.checkpoint_path    ,		""".."""    ,		"""config.gin"""				)
      lowerCAmelCase__      :			Tuple   =	inference.parse_training_gin_file(__UpperCAmelCase    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			Any   =	inference.InferenceModel(args.checkpoint_path    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			List[Any]   =	DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    ,		variance_type="""fixed_large"""				)

      lowerCAmelCase__      :			List[Any]   =	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__      :			List[str]   =	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[Any]   =	load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	load_decoder(ta_checkpoint["""target"""]["""decoder"""]    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			Any   =	OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""				)

      lowerCAmelCase__      :			Optional[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													=      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													=      parser.parse_args()

  main(args)





