




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    :		Dict			   =       "base_with_context"

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	Optional[Any]					):


      '''simple docstring'''



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

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

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

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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	List[str]					):


      '''simple docstring'''



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

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

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

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

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

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

      return model

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	Optional[Any]					):


      '''simple docstring'''



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

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

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

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

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

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

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

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

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

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

      return model

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict					):


      '''simple docstring'''



      lowerCamelCase_							=   checkpoints.load_tax_checkpoint(args.checkpoint_path					)
      lowerCamelCase_							=   jnp.tree_util.tree_map(onp.array     ,							lowercase					)

      lowerCamelCase_							=   [
          '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()',
      ]

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

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

      lowerCamelCase_							=   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'     ,							)

      lowerCamelCase_							=   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'     ,							)

      lowerCamelCase_							=   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     ,							)

      lowerCamelCase_							=   load_notes_encoder(ta_checkpoint['target']['token_encoder']     ,							lowercase					)
      lowerCamelCase_							=   load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']     ,							lowercase					)
      lowerCamelCase_							=   load_decoder(ta_checkpoint['target']['decoder']     ,							lowercase					)

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

      lowerCamelCase_							=   SpectrogramDiffusionPipeline(
          notes_encoder=lowercase     ,							continuous_encoder=lowercase     ,							decoder=lowercase     ,							scheduler=lowercase     ,							melgan=lowercase     ,							)
      if args.save:
            pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
 lowerCamelCase    :		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.",
 )
 lowerCamelCase    :		Tuple			   =       parser.parse_args()

 main(args)









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCamelCase    :		Any			   =       r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''rag'''
 UpperCamelCase						       =						True

 def __init__(    self :  Optional[Any]						,							A_ :  Optional[Any]=None						,							A_ :  Any=True						,							A_ :  Dict=None						,							A_ :  Optional[int]=None						,							A_ :  str=None						,							A_ :  int=None						,							A_ :  List[Any]=None						,							A_ :  List[str]=" / "						,							A_ :  Tuple=" // "						,							A_ :  Union[str, Any]=5						,							A_ :  Optional[Any]=300						,							A_ :  int=768						,							A_ :  Dict=8						,							A_ :  int="wiki_dpr"						,							A_ :  int="train"						,							A_ :  List[str]="compressed"						,							A_ :  Tuple=None						,							A_ :  Optional[Any]=None						,							A_ :  Optional[int]=False						,							A_ :  str=False						,							A_ :  Optional[Any]=0.0						,							A_ :  Union[str, Any]=True						,							A_ :  List[Any]=False						,							A_ :  Union[str, Any]=False						,							A_ :  Dict=False						,							A_ :  str=True						,							A_ :  List[str]=None						,							**A_ :  Optional[Any]						,							)       ->     str:

       """simple docstring"""






       super().__init__(
           bos_token_id=A_						,							pad_token_id=A_						,							eos_token_id=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							is_encoder_decoder=A_						,							prefix=A_						,							vocab_size=A_						,							**A_						,							)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       lowerCamelCase_							=   kwargs.pop('question_encoder'  )
       lowerCamelCase_							=   question_encoder_config.pop('model_type'  )
       lowerCamelCase_							=   kwargs.pop('generator'  )
       lowerCamelCase_							=   decoder_config.pop('model_type'  )

       from ..auto.configuration_auto import AutoConfig

       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )
       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )

       lowerCamelCase_							=   reduce_loss
       lowerCamelCase_							=   label_smoothing
       lowerCamelCase_							=   exclude_bos_score
       lowerCamelCase_							=   do_marginalize

       lowerCamelCase_							=   title_sep
       lowerCamelCase_							=   doc_sep
       lowerCamelCase_							=   n_docs
       lowerCamelCase_							=   max_combined_length

       lowerCamelCase_							=   dataset
       lowerCamelCase_							=   dataset_split
       lowerCamelCase_							=   index_name

       lowerCamelCase_							=   retrieval_vector_size
       lowerCamelCase_							=   retrieval_batch_size
       lowerCamelCase_							=   passages_path
       lowerCamelCase_							=   index_path
       lowerCamelCase_							=   use_dummy_dataset

       lowerCamelCase_							=   output_retrieved

       lowerCamelCase_							=   do_deduplication

       lowerCamelCase_							=   use_cache

       if self.forced_eos_token_id is None:
             lowerCamelCase_							=   getattr(self.generator						,							'forced_eos_token_id'						,							A_  )

 @classmethod
 def     a__			(    cls :  str						,							A_ :  PretrainedConfig						,							A_ :  PretrainedConfig						,							**A_ :  List[str]  )       ->     PretrainedConfig:

       """simple docstring"""






       return cls(question_encoder=question_encoder_config.to_dict()						,							generator=generator_config.to_dict()						,							**A_  )





 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   copy.deepcopy(self.__dict__  )
       lowerCamelCase_							=   self.question_encoder.to_dict()
       lowerCamelCase_							=   self.generator.to_dict()
       lowerCamelCase_							=   self.__class__.model_type
       return output



