




'''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  :				str									=  'base_with_context'


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']	)	)
					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']	),      requires_grad=__UpperCAmelCase	)
					for lyr_num, lyr in enumerate(model.encoders	):
										snake_case_									=				weights[F"layers_{lyr_num}"]
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

										snake_case_									=				ly_weight['''attention''']
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)
					return model


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']	),      requires_grad=__UpperCAmelCase	)

					for lyr_num, lyr in enumerate(model.encoders	):
										snake_case_									=				weights[F"layers_{lyr_num}"]
										snake_case_									=				ly_weight['''attention''']

										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)

					return model


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T	)	)
					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']	),      requires_grad=__UpperCAmelCase	)

					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T	)	)

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

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

										snake_case_									=				ly_weight['''self_attention''']
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

										snake_case_									=				ly_weight['''MultiHeadDotProductAttention_0''']
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T	)	)

					return model


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				checkpoints.load_tax_checkpoint(args.checkpoint_path	)
					snake_case_									=				jnp.tree_util.tree_map(onp.array,      __UpperCAmelCase	)

					snake_case_									=				[
					    '''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()''',
					]

					snake_case_									=				os.path.join(args.checkpoint_path,      '''..''',      '''config.gin'''	)
					snake_case_									=				inference.parse_training_gin_file(__UpperCAmelCase,      __UpperCAmelCase	)
					snake_case_									=				inference.InferenceModel(args.checkpoint_path,      __UpperCAmelCase	)

					snake_case_									=				DDPMScheduler(beta_schedule='''squaredcos_cap_v2''',      variance_type='''fixed_large'''	)

					snake_case_									=				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''',      )

					snake_case_									=				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''',      )

					snake_case_									=				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,      )

					snake_case_									=				load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''],      __UpperCAmelCase	)
					snake_case_									=				load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],      __UpperCAmelCase	)
					snake_case_									=				load_decoder(ta_checkpoint['''target''']['''decoder'''],      __UpperCAmelCase	)

					snake_case_									=				OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''	)

					snake_case_									=				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  :				Optional[Any]									=  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  :				Optional[Any]									=  parser.parse_args()

							main(args)


'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	Any							)  ->    int:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						UNetaDModel(
      block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,       )
  return model





 def a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase											=						self.dummy_uncond_unet
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""      ,       return_dict=SCREAMING_SNAKE_CASE__							)[0]

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]
  __lowerCAmelCase											=						image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						"""google/ddpm-cifar10-32"""

  __lowerCAmelCase											=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]							)

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






