'''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


__lowerCAmelCase	:       List[Any]						       =	"base_with_context"





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   int       ):


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

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

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

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

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





def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T       )       )

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

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

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

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

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

	return model





def       lowerCAmelCase					(		UpperCamelCase__				:   int    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T       )       )
	__UpperCAmelCase							=			nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T       )       )

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

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

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

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

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

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

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

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

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

	return model





def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			checkpoints.load_tax_checkpoint(args.checkpoint_path       )
	__UpperCAmelCase							=			jnp.tree_util.tree_map(onp.array    ,					UpperCamelCase__       )

	__UpperCAmelCase							=			[
	    '''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()''',
	]

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

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

	__UpperCAmelCase							=			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'''    ,					)

	__UpperCAmelCase							=			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'''    ,					)

	__UpperCAmelCase							=			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    ,					)

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

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

	__UpperCAmelCase							=			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__":
					__lowerCAmelCase	:       List[str]						       =	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	:       str						       =	parser.parse_args()

					main(args)

'''simple docstring'''


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp






@slow
@require_flax
class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							List[str]     ,		__a				:							Optional[Any]	)							->	List[Any]:
								return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__a	) for s in shape]	)}.npy"""





							def       snake_case__						(      self				:							Dict	)							->	Union[str, Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Tuple=0     ,		__a				:							List[Any]=(4, 4, 6_4, 6_4)     ,		__a				:							Optional[Any]=False	)							->	Tuple:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return image





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]=False     ,		__a				:							Optional[Any]="CompVis/stable-diffusion-v1-4"	)							->	Any:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			'''bf16''' if fpaa else None

								__UpperCAmelCase				,			__UpperCAmelCase							=			FlaxUNetaDConditionModel.from_pretrained(
								    __a     ,		subfolder='''unet'''     ,		dtype=__a     ,		revision=__a	)
								return model, params





							def       snake_case__						(      self				:							str     ,		__a				:							int=0     ,		__a				:							Tuple=(4, 7_7, 7_6_8)     ,		__a				:							Optional[int]=False	)							->	Union[str, Any]:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return hidden_states





							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
							        [1_7, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
							        [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
							        [3, 1_0_0_0, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Tuple     ,		__a				:							Tuple     ,		__a				:							str     ,		__a				:							Optional[Any]	)							->	Any:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)






							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
							        [1_7, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
							        [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
							        [3, 1_0_0_0, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Optional[int]     ,		__a				:							Optional[Any]     ,		__a				:							Optional[Any]	)							->	Union[str, Any]:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		shape=(4, 4, 9_6, 9_6)     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		shape=(4, 7_7, 1_0_2_4)     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)
