'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:	int    =logging.get_logger(__name__)




class 			__A (					UpperCamelCase__      ):
    a__			:		List[Any]			  =    ["""pixel_values"""]



    def __init__(self       :       Any  ,     __a       :       bool = True  ,     __a       :       Optional[Dict[str, int]] = None  ,     __a       :       PILImageResampling = PILImageResampling.BILINEAR  ,     __a       :       bool = True  ,     __a       :       Dict[str, int] = None  ,     __a       :       bool = True  ,     __a       :       Union[int, float] = 1 / 255  ,     __a       :       bool = True  ,     __a       :       Optional[Union[float, List[float]]] = None  ,     __a       :       Optional[Union[float, List[float]]] = None  ,     **__a       :       List[str]  ,     ):
         super().__init__(**a_					)
         UpperCAmelCase_			       =  size if size is not None else {'shortest_edge': 256}
         UpperCAmelCase_			       =  get_size_dict(a_  ,     default_to_square=a_					)
         UpperCAmelCase_			       =  crop_size if crop_size is not None else {'height': 224, 'width': 224}
         UpperCAmelCase_			       =  get_size_dict(a_					)
         UpperCAmelCase_			       =  do_resize
         UpperCAmelCase_			       =  size
         UpperCAmelCase_			       =  resample
         UpperCAmelCase_			       =  do_center_crop
         UpperCAmelCase_			       =  crop_size
         UpperCAmelCase_			       =  do_rescale
         UpperCAmelCase_			       =  rescale_factor
         UpperCAmelCase_			       =  do_normalize
         UpperCAmelCase_			       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_			       =  image_std if image_std is not None else IMAGENET_STANDARD_STD



    def    _lowercase		(self       :       Any  ,     __a       :       np.ndarray  ,     __a       :       Dict[str, int]  ,     __a       :       PILImageResampling = PILImageResampling.BICUBIC  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       List[Any]  ,     ):
         UpperCAmelCase_			       =  get_size_dict(a_  ,     default_to_square=a_					)
         if "shortest_edge" not in size:
              raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
         UpperCAmelCase_			       =  get_resize_output_image_size(a_  ,     size=size["shortest_edge"]  ,     default_to_square=a_					)
         return resize(a_  ,     size=a_  ,     resample=a_  ,     data_format=a_  ,     **a_					)



    def    _lowercase		(self       :       List[str]  ,     __a       :       np.ndarray  ,     __a       :       Dict[str, int]  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       List[Any]  ,     ):
         UpperCAmelCase_			       =  get_size_dict(a_					)
         return center_crop(a_  ,     size=(size["height"], size["width"])  ,     data_format=a_  ,     **a_					)



    def    _lowercase		(self       :       int  ,     __a       :       np.ndarray  ,     __a       :       float  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       Tuple					):
         return rescale(a_  ,     scale=a_  ,     data_format=a_  ,     **a_					)



    def    _lowercase		(self       :       str  ,     __a       :       np.ndarray  ,     __a       :       Union[float, List[float]]  ,     __a       :       Union[float, List[float]]  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       Any  ,     ):
         return normalize(a_  ,     mean=a_  ,     std=a_  ,     data_format=a_  ,     **a_					)






    def    _lowercase		(self       :       int  ,     __a       :       ImageInput  ,     __a       :       Optional[bool] = None  ,     __a       :       Dict[str, int] = None  ,     __a       :       PILImageResampling = None  ,     __a       :       bool = None  ,     __a       :       Dict[str, int] = None  ,     __a       :       Optional[bool] = None  ,     __a       :       Optional[float] = None  ,     __a       :       Optional[bool] = None  ,     __a       :       Optional[Union[float, List[float]]] = None  ,     __a       :       Optional[Union[float, List[float]]] = None  ,     __a       :       Optional[Union[str, TensorType]] = None  ,     __a       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,     **__a       :       str  ,     ):
         UpperCAmelCase_			       =  do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_			       =  size if size is not None else self.size
         UpperCAmelCase_			       =  get_size_dict(a_  ,     default_to_square=a_					)
         UpperCAmelCase_			       =  resample if resample is not None else self.resample
         UpperCAmelCase_			       =  do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_			       =  crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_			       =  get_size_dict(a_					)
         UpperCAmelCase_			       =  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_			       =  do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_			       =  image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_			       =  image_std if image_std is not None else self.image_std

         UpperCAmelCase_			       =  make_list_of_images(a_					)

         if not valid_images(a_					):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."					)

         if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."					)

         if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."					)

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."					)

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."					)

         # All transformations expect numpy arrays.
         UpperCAmelCase_			       =  [to_numpy_array(a_					) for image in images]

         if do_resize:
              UpperCAmelCase_			       =  [self.resize(image=a_  ,     size=a_  ,     resample=a_					) for image in images]

         if do_center_crop:
              UpperCAmelCase_			       =  [self.center_crop(image=a_  ,     size=a_					) for image in images]

         if do_rescale:
              UpperCAmelCase_			       =  [self.rescale(image=a_  ,     scale=a_					) for image in images]

         if do_normalize:
              UpperCAmelCase_			       =  [self.normalize(image=a_  ,     mean=a_  ,     std=a_					) for image in images]

         UpperCAmelCase_			       =  [to_channel_dimension_format(a_  ,     a_					) for image in images]

         UpperCAmelCase_			       =  {'pixel_values': images}
         return BatchFeature(data=a_  ,     tensor_type=a_					)









'''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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					str:





			"""simple docstring"""







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

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

						snake_case:    List[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

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

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







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





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

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

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

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

						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Tuple      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)
						snake_case:    Any      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

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

			return model







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
			snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

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

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

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

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

						snake_case:    str      							=ly_weight['self_attention']
						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

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

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

			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']					)					)

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

			return model







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







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

			snake_case:    str      							=[
			    '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:    List[Any]      							=os.path.join(args.checkpoint_path				,							'..'				,							'config.gin'					)
			snake_case:    Optional[Any]      							=inference.parse_training_gin_file(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[str]      							=inference.InferenceModel(args.checkpoint_path				,							__UpperCAmelCase					)

			snake_case:    List[Any]      							=DDPMScheduler(beta_schedule='squaredcos_cap_v2'				,							variance_type='fixed_large'					)

			snake_case:    Optional[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'				,							)

			snake_case:    Optional[Any]      							=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:    List[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				,							)

			snake_case:    Optional[Any]      							=load_notes_encoder(ta_checkpoint['target']['token_encoder']				,							__UpperCAmelCase					)
			snake_case:    Optional[Any]      							=load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']				,							__UpperCAmelCase					)
			snake_case:    Union[str, Any]      							=load_decoder(ta_checkpoint['target']['decoder']				,							__UpperCAmelCase					)

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

			snake_case:    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)




