


'''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


__UpperCAmelCase      :str			    =       """base_with_context"""




def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Optional[int]  ):





     '''simple docstring'''






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

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

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

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

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




def      _a     (						_lowercase :     str      , _lowercase :     List[Any]  ):





     '''simple docstring'''






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

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

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

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

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

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

     return model




def      _a     (						_lowercase :     Union[str, Any]      , _lowercase :     Optional[Any]  ):





     '''simple docstring'''






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

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

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

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

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

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

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

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

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

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

     return model




def      _a     (						_lowercase :     Optional[int]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      checkpoints.load_tax_checkpoint(args.checkpoint_path  )
     __UpperCAmelCase     :			List[Any]  							=      jnp.tree_util.tree_map(onp.array      , _lowercase  )

     __UpperCAmelCase     :			List[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()''',
     ]

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

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

     __UpperCAmelCase     :			Tuple  							=      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     :			Union[str, 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'''      , )

     __UpperCAmelCase     :			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      , )

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

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

     __UpperCAmelCase     :			Optional[int]  							=      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__":
      __UpperCAmelCase      :Optional[int]			    =       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.",
      )
      __UpperCAmelCase      :List[str]			    =       parser.parse_args()

      main(args)



'''simple docstring'''



import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      :Any			    =       logging.get_logger(__name__)

__UpperCAmelCase      :Dict			    =       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]




def      _a     (						_lowercase :     Tuple  ):





     '''simple docstring'''






     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __UpperCAmelCase     :			Any  							=      k.replace(_lowercase      , _lowercase  )

     if k.startswith('''encoder'''  ):
          __UpperCAmelCase     :			str  							=      k.replace('''.attn'''      , '''.self_attn'''  )
          __UpperCAmelCase     :			Any  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[str]  							=      k.replace('''norm2'''      , '''final_layer_norm'''  )
     elif k.startswith('''decoder'''  ):
          __UpperCAmelCase     :			int  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			Union[str, Any]  							=      k.replace('''norm2'''      , '''encoder_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[Any]  							=      k.replace('''norm3'''      , '''final_layer_norm'''  )
     return k




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      [
         '''model.encoder.layernorm_embedding.weight''',
         '''model.encoder.layernorm_embedding.bias''',
         '''model.decoder.layernorm_embedding.weight''',
         '''model.decoder.layernorm_embedding.bias''',
     ]
     for k in keys:
          __UpperCAmelCase     :			Any  							=      sd.pop(_lowercase  )
          __UpperCAmelCase     :			Optional[int]  							=      k.replace('''layernorm_embedding'''      , '''layer_norm'''  )
          assert new_k not in sd
          __UpperCAmelCase     :			List[str]  							=      v


__UpperCAmelCase      :str			    =       ["START"]




@torch.no_grad()
def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Optional[int]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      torch.load(_lowercase      , map_location='''cpu'''  )
     __UpperCAmelCase     :			List[str]  							=      model['''model''']
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotConfig.from_json_file(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotForConditionalGeneration(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      m.model.state_dict().keys()
     __UpperCAmelCase     :			int  							=      []
     __UpperCAmelCase     :			List[str]  							=      {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __UpperCAmelCase     :			int  							=      rename_state_dict_key(_lowercase  )
          if new_k not in valid_keys:
               failures.append([k, new_k]  )
          else:
               __UpperCAmelCase     :			Union[str, Any]  							=      v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(_lowercase  )
     m.model.load_state_dict(_lowercase      , strict=_lowercase  )
     m.half()
     m.save_pretrained(_lowercase  )


if __name__ == "__main__":
      __UpperCAmelCase      :Optional[int]			    =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
      parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
      parser.add_argument(
          "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
      )
      __UpperCAmelCase      :Tuple			    =       parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)