





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


__lowercase       :Any       =  "base_with_context"







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


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

												SCREAMING_SNAKE_CASE__     :				List[str]   =					ly_weight["attention"]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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__     :				Union[str, 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__     :				Tuple   =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				Optional[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(weights["encoder_norm"]["scale"]     )     )
						return model







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T     )     )

						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					nn.Parameter(
						    torch.FloatTensor(weights["Embed_0"]["embedding"]     )			,   requires_grad=_lowerCamelCase     )

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

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

												SCREAMING_SNAKE_CASE__     :				Tuple   =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				List[str]   =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				Tuple   =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				List[str]   =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]     )     )

						SCREAMING_SNAKE_CASE__     :				List[str]   =					nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]     )     )

						return model







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T     )     )
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T     )     )

						SCREAMING_SNAKE_CASE__     :				int   =					nn.Parameter(
						    torch.FloatTensor(weights["Embed_0"]["embedding"]     )			,   requires_grad=_lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					nn.Parameter(
						    torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T     )     )

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

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

												SCREAMING_SNAKE_CASE__     :				List[Any]   =					ly_weight["self_attention"]
												SCREAMING_SNAKE_CASE__     :				Tuple   =					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__     :				Optional[int]   =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				List[str]   =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T     )     )

												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ly_weight["MultiHeadDotProductAttention_0"]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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__     :				str   =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				List[str]   =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					nn.Parameter(
												    torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]     )     )

												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]     )     )
												SCREAMING_SNAKE_CASE__     :				str   =					nn.Parameter(
												    torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T     )     )
												SCREAMING_SNAKE_CASE__     :				Union[str, 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__     :				Union[str, Any]   =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T     )     )

						SCREAMING_SNAKE_CASE__     :				Tuple   =					nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]     )     )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T     )     )

						return model







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					checkpoints.load_tax_checkpoint(args.checkpoint_path     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					jnp.tree_util.tree_map(onp.array			,   _lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					[
						    "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__     :				int   =					os.path.join(args.checkpoint_path			,   ".."			,   "config.gin"     )
						SCREAMING_SNAKE_CASE__     :				int   =					inference.parse_training_gin_file(_lowerCamelCase			,   _lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					inference.InferenceModel(args.checkpoint_path			,   _lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					DDPMScheduler(beta_schedule="squaredcos_cap_v2"			,   variance_type="fixed_large"     )

						SCREAMING_SNAKE_CASE__     :				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__     :				Dict   =					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__     :				Optional[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__     :				int   =					load_notes_encoder(ta_checkpoint["target"]["token_encoder"]			,   _lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				str   =					load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]			,   _lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				int   =					load_decoder(ta_checkpoint["target"]["decoder"]			,   _lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				List[Any]   =					OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"     )

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


if __name__ == "__main__":
				__lowercase       :Union[str, 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.",
				)
				__lowercase       :str       =  parser.parse_args()

				main(args)






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard



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






							"""simple docstring"""

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

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe.prepare_image_inputs([canny_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe.prepare_image_inputs([pose_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2