






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


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		"""base_with_context"""

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']			)			)
				__magic_name__		:Dict   =   nn.Parameter(
				    torch.FloatTensor(weights['''Embed_0''']['''embedding''']			),	requires_grad=snake_case			)
				for lyr_num, lyr in enumerate(model.encoders			):
								__magic_name__		:Optional[int]   =   weights[f'''layers_{lyr_num}''']
								__magic_name__		:Dict   =   nn.Parameter(
								    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']			)			)

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

								__magic_name__		:int   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']			)			)

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

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

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T			)			)

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

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

								__magic_name__		:Optional[int]   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T			)			)
								__magic_name__		:List[str]   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T			)			)
								__magic_name__		:Union[str, Any]   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T			)			)
								__magic_name__		:Dict   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T			)			)
								__magic_name__		:Optional[Any]   =   nn.Parameter(
								    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']			)			)

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

				__magic_name__		:int   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']			)			)

				return model

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T			)			)
				__magic_name__		:str   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T			)			)

				__magic_name__		:List[str]   =   nn.Parameter(
				    torch.FloatTensor(weights['''Embed_0''']['''embedding''']			),	requires_grad=snake_case			)

				__magic_name__		:Optional[Any]   =   nn.Parameter(
				    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T			)			)

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

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

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

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

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

				__magic_name__		:Union[str, Any]   =   nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']			)			)

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

				return model

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   checkpoints.load_tax_checkpoint(args.checkpoint_path			)
				__magic_name__		:Tuple   =   jnp.tree_util.tree_map(onp.array,	snake_case			)

				__magic_name__		:Optional[int]   =   [
				    '''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()''',
				]

				__magic_name__		:Dict   =   os.path.join(args.checkpoint_path,	'''..''',	'''config.gin'''			)
				__magic_name__		:Dict   =   inference.parse_training_gin_file(snake_case,	snake_case			)
				__magic_name__		:Optional[int]   =   inference.InferenceModel(args.checkpoint_path,	snake_case			)

				__magic_name__		:List[str]   =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2''',	variance_type='''fixed_large'''			)

				__magic_name__		:List[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''',	)

				__magic_name__		: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''',	)

				__magic_name__		:Dict   =   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,	)

				__magic_name__		:int   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''],	snake_case			)
				__magic_name__		:Any   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],	snake_case			)
				__magic_name__		:int   =   load_decoder(ta_checkpoint['''target''']['''decoder'''],	snake_case			)

				__magic_name__		:int   =   OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''			)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      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.""",
					)
					SCREAMING_SNAKE_CASE__   :      Dict				=		parser.parse_args()

					main(args)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase					      =   {
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   ['Pix2StructImageProcessor']


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   [
							    'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Pix2StructPreTrainedModel',
							    'Pix2StructForConditionalGeneration',
							    'Pix2StructVisionModel',
							    'Pix2StructTextModel',
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_UpperCamelCase					      =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




