



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 A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   List[Any]:
			A__	  =     nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']       )       )
			A__	  =     nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']       )			,						requires_grad=_A       )
			for lyr_num, lyr in enumerate(model.encoders       ):
						A__	  =     weights[f'''layers_{lyr_num}''']
						A__	  =     nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']       )       )

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

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

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

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


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			A__	  =     nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T       )       )

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

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

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

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

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

			return model


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			A__	  =     nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T       )       )
			A__	  =     nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T       )       )

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

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

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

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

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

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

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

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

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

			return model


def A    (							__UpperCamelCase       )    ->   int:
			A__	  =     checkpoints.load_tax_checkpoint(args.checkpoint_path       )
			A__	  =     jnp.tree_util.tree_map(onp.array			,						_A       )

			A__	  =     [
			    '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__	  =     os.path.join(args.checkpoint_path			,						'..'			,						'config.gin'       )
			A__	  =     inference.parse_training_gin_file(_A			,						_A       )
			A__	  =     inference.InferenceModel(args.checkpoint_path			,						_A       )

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

			A__	  =     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__	  =     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__	  =     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__	  =     load_notes_encoder(ta_checkpoint['target']['token_encoder']			,						_A       )
			A__	  =     load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']			,						_A       )
			A__	  =     load_decoder(ta_checkpoint['target']['decoder']			,						_A       )

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

			A__	  =     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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_albert import AlbertTokenizer
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		None

SCREAMING_SNAKE_CASE			:						str				  =		logging.get_logger(__name__)
SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE			:						Any				  =		{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		"""▁"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      AlbertTokenizer

  def __init__(		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True  ,				__SCREAMING_SNAKE_CASE			:   Dict=False  ,				__SCREAMING_SNAKE_CASE			:   Dict="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Dict="<unk>"  ,				__SCREAMING_SNAKE_CASE			:   str="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Any="<pad>"  ,				__SCREAMING_SNAKE_CASE			:   List[str]="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]="[MASK]"  ,				**__SCREAMING_SNAKE_CASE			:   Tuple  ,				):
   # Mask token behave like a normal word, i.e. include the space before it and
   # is included in the raw text, there should be a match in a non-normalized sentence.
   __a							=	(
       AddedToken(__SCREAMING_SNAKE_CASE  ,				lstrip=__SCREAMING_SNAKE_CASE  ,				rstrip=__SCREAMING_SNAKE_CASE  ,				normalized=__SCREAMING_SNAKE_CASE							)
       if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
       else mask_token
   )

   super().__init__(
       __SCREAMING_SNAKE_CASE  ,				tokenizer_file=__SCREAMING_SNAKE_CASE  ,				do_lower_case=__SCREAMING_SNAKE_CASE  ,				remove_space=__SCREAMING_SNAKE_CASE  ,				keep_accents=__SCREAMING_SNAKE_CASE  ,				bos_token=__SCREAMING_SNAKE_CASE  ,				eos_token=__SCREAMING_SNAKE_CASE  ,				unk_token=__SCREAMING_SNAKE_CASE  ,				sep_token=__SCREAMING_SNAKE_CASE  ,				pad_token=__SCREAMING_SNAKE_CASE  ,				cls_token=__SCREAMING_SNAKE_CASE  ,				mask_token=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	do_lower_case
   __a							=	remove_space
   __a							=	keep_accents
   __a							=	vocab_file
   __a							=	False if not self.vocab_file else True

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep							) * [0]
   return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None							):
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
        "tokenizer."							)

   if not os.path.isdir(__SCREAMING_SNAKE_CASE							):
    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
    return
   __a							=	os.path.join(
       __SCREAMING_SNAKE_CASE  ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

   if os.path.abspath(self.vocab_file							) != os.path.abspath(__SCREAMING_SNAKE_CASE							):
    copyfile(self.vocab_file  ,				__SCREAMING_SNAKE_CASE							)

   return (out_vocab_file,)


