





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__							:      List[Any]         =    """base_with_context"""






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Optional[int]   		=	nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
   a__						:    Tuple   		=	nn.Parameter(
       torch.FloatTensor(weights["Embed_0"]["embedding"] )				,			requires_grad=lowerCamelCase )
   for lyr_num, lyr in enumerate(model.encoders ):
      a__						:    List[Any]   		=	weights[F"""layers_{lyr_num}"""]
      a__						:    str   		=	nn.Parameter(
          torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )

      a__						:    List[Any]   		=	ly_weight["attention"]
      a__						:    Any   		=	nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )

      a__						:    int   		=	nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )

      a__						:    Any   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
      a__						:    Union[str, Any]   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
      a__						:    str   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )

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






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Any   		=	nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )

   a__						:    List[str]   		=	nn.Parameter(
       torch.FloatTensor(weights["Embed_0"]["embedding"] )				,			requires_grad=lowerCamelCase )

   for lyr_num, lyr in enumerate(model.encoders ):
      a__						:    Tuple   		=	weights[F"""layers_{lyr_num}"""]
      a__						:    Dict   		=	ly_weight["attention"]

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

      a__						:    Dict   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
      a__						:    List[Any]   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
      a__						:    List[str]   		=	nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )

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

   return model






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Tuple   		=	nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
   a__						:    List[str]   		=	nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )

   a__						:    Any   		=	nn.Parameter(
       torch.FloatTensor(weights["Embed_0"]["embedding"] )				,			requires_grad=lowerCamelCase )

   a__						:    Tuple   		=	nn.Parameter(
       torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )

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

      a__						:    int   		=	nn.Parameter(
          torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )

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

      a__						:    Dict   		=	ly_weight["MultiHeadDotProductAttention_0"]
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
      a__						:    Any   		=	nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
      a__						:    List[Any]   		=	nn.Parameter(
          torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )

      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
      a__						:    List[str]   		=	nn.Parameter(
          torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
      a__						:    Optional[Any]   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
      a__						:    Union[str, Any]   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
      a__						:    List[str]   		=	nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )

   a__						:    Any   		=	nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )

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

   return model






def    _A	(   lowerCamelCase ):
   a__						:    str   		=	checkpoints.load_tax_checkpoint(args.checkpoint_path )
   a__						:    Optional[int]   		=	jnp.tree_util.tree_map(onp.array				,			lowerCamelCase )

   a__						:    Any   		=	[
       "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()",
   ]

   a__						:    str   		=	os.path.join(args.checkpoint_path				,			".."				,			"config.gin" )
   a__						:    int   		=	inference.parse_training_gin_file(lowerCamelCase				,			lowerCamelCase )
   a__						:    List[Any]   		=	inference.InferenceModel(args.checkpoint_path				,			lowerCamelCase )

   a__						:    List[str]   		=	DDPMScheduler(beta_schedule="squaredcos_cap_v2"				,			variance_type="fixed_large" )

   a__						:    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"				,			)

   a__						:    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"				,			)

   a__						:    Union[str, 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				,			)

   a__						:    str   		=	load_notes_encoder(ta_checkpoint["target"]["token_encoder"]				,			lowerCamelCase )
   a__						:    int   		=	load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]				,			lowerCamelCase )
   a__						:    int   		=	load_decoder(ta_checkpoint["target"]["decoder"]				,			lowerCamelCase )

   a__						:    Tuple   		=	OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )

   a__						:    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__":
  SCREAMING_SNAKE_CASE__							:      Tuple         =    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__							:      Optional[Any]         =    parser.parse_args()

  main(args)








import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

