


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Optional[int]       					=			'char'
 __lowercase			:							Any       					=			'bpe'
 __lowercase			:							int       					=			'wp'





__lowerCAmelCase			=							(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class   __a      (   __UpperCamelCase				):
 __lowercase			:							List[Any]       					=			['image_processor', 'char_tokenizer']
 __lowercase			:							Union[str, Any]       					=			'ViTImageProcessor'
 __lowercase			:							int       					=			'MgpstrTokenizer'
 def __init__(       self    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=None    ,			**lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.'    ,			lowerCAmelCase__    ,			)
               lowercase__:       Dict        =       kwargs.pop('feature_extractor'			)

        lowercase__:       Optional[int]        =       image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.'			)
        if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.'			)

        lowercase__:       Any        =       tokenizer
        lowercase__:       Tuple        =       AutoTokenizer.from_pretrained('gpt2'			)
        lowercase__:       Dict        =       AutoTokenizer.from_pretrained('bert-base-uncased'			)

        super().__init__(lowerCAmelCase__    ,			lowerCAmelCase__			)
 def __call__(       self    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=None    ,			**lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        if images is None and text is None:
               raise ValueError('You need to specify either an `images` or `text` input to process.'			)

        if images is not None:
               lowercase__:       Dict        =       self.image_processor(lowerCAmelCase__    ,			return_tensors=lowerCAmelCase__    ,			**lowerCAmelCase__			)
        if text is not None:
               lowercase__:       List[str]        =       self.char_tokenizer(lowerCAmelCase__    ,			return_tensors=lowerCAmelCase__    ,			**lowerCAmelCase__			)

        if text is None:
               return inputs
        elif images is None:
               return encodings
        else:
               lowercase__:       List[Any]        =       encodings['input_ids']
               return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        lowercase__      ,						lowercase__      ,						lowercase__:       Optional[Any]        =       sequences
        lowercase__:       int        =       char_preds.size(0			)

        lowercase__      ,						lowercase__:       Optional[int]        =       self._decode_helper(lowerCAmelCase__    ,			'char'			)
        lowercase__      ,						lowercase__:       int        =       self._decode_helper(lowerCAmelCase__    ,			'bpe'			)
        lowercase__      ,						lowercase__:       Optional[Any]        =       self._decode_helper(lowerCAmelCase__    ,			'wp'			)

        lowercase__:       Union[str, Any]        =       []
        lowercase__:       List[str]        =       []
        for i in range(lowerCAmelCase__			):
               lowercase__:       int        =       [char_scores[i], bpe_scores[i], wp_scores[i]]
               lowercase__:       List[str]        =       [char_strs[i], bpe_strs[i], wp_strs[i]]
               lowercase__:       Any        =       scores.index(max(lowerCAmelCase__			)			)
               final_strs.append(strs[max_score_index]			)
               final_scores.append(scores[max_score_index]			)

        lowercase__:       List[Any]        =       {}
        lowercase__:       Dict        =       final_strs
        lowercase__:       int        =       final_scores
        lowercase__:       str        =       char_strs
        lowercase__:       str        =       bpe_strs
        lowercase__:       List[Any]        =       wp_strs
        return out
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        if format == DecodeType.CHARACTER:
               lowercase__:       List[Any]        =       self.char_decode
               lowercase__:       str        =       1
               lowercase__:       List[str]        =       '[s]'
        elif format == DecodeType.BPE:
               lowercase__:       List[Any]        =       self.bpe_decode
               lowercase__:       int        =       2
               lowercase__:       List[str]        =       '#'
        elif format == DecodeType.WORDPIECE:
               lowercase__:       str        =       self.wp_decode
               lowercase__:       Dict        =       102
               lowercase__:       List[Any]        =       '[SEP]'
        else:
               raise ValueError(F'Format {format} is not supported.'			)

        lowercase__      ,						lowercase__:       Any        =       [], []
        lowercase__:       List[str]        =       pred_logits.size(0			)
        lowercase__:       List[Any]        =       pred_logits.size(1			)
        lowercase__      ,						lowercase__:       Tuple        =       pred_logits.topk(1    ,			dim=-1    ,			largest=lowerCAmelCase__    ,			sorted=lowerCAmelCase__			)
        lowercase__:       Dict        =       preds_index.view(-1    ,			lowerCAmelCase__			)[:, 1:]
        lowercase__:       Optional[Any]        =       decoder(lowerCAmelCase__			)
        lowercase__      ,						lowercase__:       str        =       torch.nn.functional.softmax(lowerCAmelCase__    ,			dim=2			).max(dim=2			)
        lowercase__:       str        =       preds_max_prob[:, 1:]

        for index in range(lowerCAmelCase__			):
               lowercase__:       str        =       preds_str[index].find(lowerCAmelCase__			)
               lowercase__:       Dict        =       preds_str[index][:pred_eos]
               lowercase__:       Union[str, Any]        =       preds_index[index].cpu().tolist()
               lowercase__:       Optional[Any]        =       pred_index.index(lowerCAmelCase__			) if eos_token in pred_index else -1
               lowercase__:       Any        =       preds_max_prob[index][: pred_eos_index + 1]
               lowercase__:       int        =       pred_max_prob.cumprod(dim=0			)[-1] if pred_max_prob.nelement() != 0 else 0.0
               dec_strs.append(lowerCAmelCase__			)
               conf_scores.append(lowerCAmelCase__			)

        return dec_strs, conf_scores
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Any        =       [seq.replace(' '    ,			''			) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__			)]
        return decode_strs
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        return self.bpe_tokenizer.batch_decode(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[str]        =       [seq.replace(' '    ,			''			) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__			)]
        return decode_strs






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			=							'''base_with_context'''




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

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

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

              lowercase__:       Optional[Any]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

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




def        snake_case_					(  snake_case      ,							snake_case					)  ->    List[str]:
       lowercase__:       str        =       nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

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

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

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

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

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

       return model




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       int        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
       lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

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

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

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

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

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

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

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

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

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

       return model




def        snake_case_					(  snake_case					)  ->    Any:
       lowercase__:       int        =       checkpoints.load_tax_checkpoint(args.checkpoint_path					)
       lowercase__:       Tuple        =       jnp.tree_util.tree_map(onp.array      ,							snake_case					)

       lowercase__:       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()',
       ]

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

       lowercase__:       Dict        =       DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,							variance_type='fixed_large'					)

       lowercase__:       List[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'      ,							)

       lowercase__:       Dict        =       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'      ,							)

       lowercase__:       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      ,							)

       lowercase__:       Dict        =       load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,							snake_case					)
       lowercase__:       int        =       load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,							snake_case					)
       lowercase__:       Optional[int]        =       load_decoder(ta_checkpoint['target']['decoder']      ,							snake_case					)

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

       lowercase__:       List[Any]        =       SpectrogramDiffusionPipeline(
           notes_encoder=snake_case      ,							continuous_encoder=snake_case      ,							decoder=snake_case      ,							scheduler=snake_case      ,							melgan=snake_case      ,							)
       if args.save:
              pipe.save_pretrained(args.output_path					)


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

 main(args)


