






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 							=						'base_with_context'

def      _lowerCamelCase	(						__A    :							Union[str, Any]		,		__A    :							Union[str, Any] )      ->					Union[str, Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding'''] ) )
    _UpperCAmelCase				:      Tuple			  =				nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding'''] )		,		requires_grad=__A )
    for lyr_num, lyr in enumerate(model.encoders ):
        _UpperCAmelCase				:      Any			  =				weights[f'''layers_{lyr_num}''']
        _UpperCAmelCase				:      str			  =				nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale'''] ) )

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

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

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

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

def      _lowerCamelCase	(						__A    :							str		,		__A    :							Tuple )      ->					Optional[Any]:
    _UpperCAmelCase				:      Tuple			  =				nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T ) )

    _UpperCAmelCase				:      Any			  =				nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding'''] )		,		requires_grad=__A )

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

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

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

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

    return model

def      _lowerCamelCase	(						__A    :							List[str]		,		__A    :							List[str] )      ->					Optional[int]:
    _UpperCAmelCase				:      Dict			  =				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T ) )
    _UpperCAmelCase				:      Any			  =				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T ) )

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

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

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

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

        _UpperCAmelCase				:      Union[str, Any]			  =				ly_weight['''self_attention''']
        _UpperCAmelCase				:      Dict			  =				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
        _UpperCAmelCase				:      int			  =				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
        _UpperCAmelCase				:      List[str]			  =				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
        _UpperCAmelCase				:      Tuple			  =				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )

        _UpperCAmelCase				:      Any			  =				ly_weight['''MultiHeadDotProductAttention_0''']
        _UpperCAmelCase				:      str			  =				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
        _UpperCAmelCase				:      Any			  =				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
        _UpperCAmelCase				:      int			  =				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
        _UpperCAmelCase				:      Optional[int]			  =				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
        _UpperCAmelCase				:      Union[str, Any]			  =				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				:      Optional[Any]			  =				nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T ) )
        _UpperCAmelCase				:      List[Any]			  =				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
        _UpperCAmelCase				:      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				:      Dict			  =				nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale'''] ) )

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

    return model

def      _lowerCamelCase	(						__A    :							Optional[Any] )      ->					int:
    _UpperCAmelCase				:      Any			  =				checkpoints.load_tax_checkpoint(args.checkpoint_path )
    _UpperCAmelCase				:      List[Any]			  =				jnp.tree_util.tree_map(onp.array		,		__A )

    _UpperCAmelCase				:      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				:      str			  =				os.path.join(args.checkpoint_path		,		'''..'''		,		'''config.gin''' )
    _UpperCAmelCase				:      int			  =				inference.parse_training_gin_file(__A		,		__A )
    _UpperCAmelCase				:      Dict			  =				inference.InferenceModel(args.checkpoint_path		,		__A )

    _UpperCAmelCase				:      int			  =				DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		,		variance_type='''fixed_large''' )

    _UpperCAmelCase				:      Optional[int]			  =				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				:      int			  =				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				:      Optional[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				:      Optional[int]			  =				load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']		,		__A )
    _UpperCAmelCase				:      Tuple			  =				load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']		,		__A )
    _UpperCAmelCase				:      Tuple			  =				load_decoder(ta_checkpoint['''target''']['''decoder''']		,		__A )

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

    _UpperCAmelCase				:      Optional[Any]			  =				SpectrogramDiffusionPipeline(
        notes_encoder=__A		,		continuous_encoder=__A		,		decoder=__A		,		scheduler=__A		,		melgan=__A		,		)
    if args.save:
        pipe.save_pretrained(args.output_path )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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 							=						parser.parse_args()

  main(args)












import argparse
import os
import re


SCREAMING_SNAKE_CASE 							=						'src/transformers/models/auto'


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

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							bool = False )      ->					int:
    with open(__A		,		'''r'''		,		encoding='''utf-8''' ) as f:
        _UpperCAmelCase				:      Union[str, Any]			  =				f.read()

    _UpperCAmelCase				:      Any			  =				content.split('''\n''' )
    _UpperCAmelCase				:      Any			  =				[]
    _UpperCAmelCase				:      Tuple			  =				0
    while line_idx < len(__A ):
        if _re_intro_mapping.search(lines[line_idx] ) is not None:
            _UpperCAmelCase				:      Union[str, Any]			  =				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

            _UpperCAmelCase				:      str			  =				[]
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    _UpperCAmelCase				:      List[str]			  =				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
            _UpperCAmelCase				:      Tuple			  =				sorted(__A		,		key=lambda __A : _re_identifier.search(__A ).groups()[0] )
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx] )
            line_idx += 1

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

def      _lowerCamelCase	(						__A    :							bool = False )      ->					List[str]:
    _UpperCAmelCase				:      List[str]			  =				[os.path.join(__A		,		__A ) for f in os.listdir(__A ) if f.endswith('''.py''' )]
    _UpperCAmelCase				:      List[Any]			  =				[sort_auto_mapping(__A		,		overwrite=__A ) for fname in fnames]

    if not overwrite and any(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				[f for f, d in zip(__A		,		__A ) if d]
        raise ValueError(
            f'''The following files have auto mappings that need sorting: {', '.join(__A )}. Run `make style` to fix'''
            ''' this.''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  sort_all_auto_mappings(not args.check_only)




