"""simple docstring"""






import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__						:						Tuple		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						Tuple		  =			{
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

lowerCAmelCase__						:						str		  =			{
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

lowerCAmelCase__						:						Dict		  =			{
    'vinai/phobert-base': 256,
    'vinai/phobert-large': 256,
}



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							set()
				UpperCAmelCase__    =							word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								UpperCAmelCase__    =							char

				UpperCAmelCase__    =							set(lowerCamelCase )
				return pairs







class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				VOCAB_FILES_NAMES
					snake_case__       					=				PRETRAINED_VOCAB_FILES_MAP
					snake_case__       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







					def __init__(       self     :			str					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			List[str]="<s>"					,lowerCamelCase__     :			Optional[int]="</s>"					,lowerCamelCase__     :			Any="</s>"					,lowerCamelCase__     :			str="<s>"					,lowerCamelCase__     :			List[str]="<unk>"					,lowerCamelCase__     :			Union[str, Any]="<pad>"					,lowerCamelCase__     :			Optional[Any]="<mask>"					,**lowerCamelCase__     :			List[Any]					,):
									super().__init__(
									    bos_token=lowerCamelCase__					,eos_token=lowerCamelCase__					,unk_token=lowerCamelCase__					,sep_token=lowerCamelCase__					,cls_token=lowerCamelCase__					,pad_token=lowerCamelCase__					,mask_token=lowerCamelCase__					,**lowerCamelCase__					,)

									UpperCAmelCase__    =							vocab_file
									UpperCAmelCase__    =							merges_file

									UpperCAmelCase__    =							{}
									UpperCAmelCase__    =							0
									UpperCAmelCase__    =							1
									UpperCAmelCase__    =							2
									UpperCAmelCase__    =							3

									self.add_from_file(lowerCamelCase__    )

									UpperCAmelCase__    =							{v: k for k, v in self.encoder.items()}

									with open(lowerCamelCase__					,encoding='utf-8'    ) as merges_handle:
													UpperCAmelCase__    =							merges_handle.read().split('\n'    )[:-1]
									UpperCAmelCase__    =							[tuple(merge.split()[:-1]    ) for merge in merges]
									UpperCAmelCase__    =							dict(zip(lowerCamelCase__					,range(len(lowerCamelCase__    )    )    )    )
									UpperCAmelCase__    =							{}







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			List[int]					,lowerCamelCase__     :			Optional[List[int]] = None    ):

									if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									UpperCAmelCase__    =							[self.cls_token_id]
									UpperCAmelCase__    =							[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			List[int]					,lowerCamelCase__     :			Optional[List[int]] = None					,lowerCamelCase__     :			bool = False    ):

									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCamelCase__					,token_ids_a=lowerCamelCase__					,already_has_special_tokens=lowerCamelCase__    )

									if token_ids_a is None:
													return [1] + ([0] * len(lowerCamelCase__    )) + [1]
									return [1] + ([0] * len(lowerCamelCase__    )) + [1, 1] + ([0] * len(lowerCamelCase__    )) + [1]







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			List[int]					,lowerCamelCase__     :			Optional[List[int]] = None    ):
									UpperCAmelCase__    =							[self.sep_token_id]
									UpperCAmelCase__    =							[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 + sep + token_ids_a + sep    ) * [0]







					@property
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									return len(self.encoder    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									return dict(self.encoder					,**self.added_tokens_encoder    )







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			Any    ):
									if token in self.cache:
													return self.cache[token]
									UpperCAmelCase__    =							tuple(lowerCamelCase__    )
									UpperCAmelCase__    =							tuple(list(word[:-1]    ) + [word[-1] + '</w>']    )
									UpperCAmelCase__    =							get_pairs(lowerCamelCase__    )

									if not pairs:
													return token

									while True:
													UpperCAmelCase__    =							min(lowerCamelCase__					,key=lambda lowerCamelCase__    : self.bpe_ranks.get(lowerCamelCase__					,float('inf'    )    )    )
													if bigram not in self.bpe_ranks:
																	break
													UpperCAmelCase__   ,			UpperCAmelCase__    =							bigram
													UpperCAmelCase__    =							[]
													UpperCAmelCase__    =							0
													while i < len(lowerCamelCase__    ):
																	try:
																					UpperCAmelCase__    =							word.index(lowerCamelCase__					,lowerCamelCase__    )
																	except ValueError:
																					new_word.extend(word[i:]    )
																					break
																	else:
																					new_word.extend(word[i:j]    )
																					UpperCAmelCase__    =							j

																	if word[i] == first and i < len(lowerCamelCase__    ) - 1 and word[i + 1] == second:
																					new_word.append(first + second    )
																					i += 2
																	else:
																					new_word.append(word[i]    )
																					i += 1
													UpperCAmelCase__    =							tuple(lowerCamelCase__    )
													UpperCAmelCase__    =							new_word
													if len(lowerCamelCase__    ) == 1:
																	break
													else:
																	UpperCAmelCase__    =							get_pairs(lowerCamelCase__    )
									UpperCAmelCase__    =							'@@ '.join(lowerCamelCase__    )
									UpperCAmelCase__    =							word[:-4]
									UpperCAmelCase__    =							word
									return word







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			List[str]    ):
									UpperCAmelCase__    =							[]

									UpperCAmelCase__    =							re.findall(r'\S+\n?'					,lowerCamelCase__    )

									for token in words:
													split_tokens.extend(list(self.bpe(lowerCamelCase__    ).split(' '    )    )    )
									return split_tokens







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			List[str]    ):
									return self.encoder.get(lowerCamelCase__					,self.encoder.get(self.unk_token    )    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[Any]    ):
									return self.decoder.get(lowerCamelCase__					,self.unk_token    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							' '.join(lowerCamelCase__    ).replace('@@ '					,''    ).strip()
									return out_string







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[str] = None    ):
									if not os.path.isdir(lowerCamelCase__    ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
													return
									UpperCAmelCase__    =							os.path.join(
									    lowerCamelCase__					,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
									UpperCAmelCase__    =							os.path.join(
									    lowerCamelCase__					,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

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

									if os.path.abspath(self.merges_file    ) != os.path.abspath(lowerCamelCase__    ):
													copyfile(self.merges_file					,lowerCamelCase__    )

									return out_vocab_file, out_merge_file







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Dict    ):
									if isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													try:
																	with open(lowerCamelCase__					,'r'					,encoding='utf-8'    ) as fd:
																					self.add_from_file(lowerCamelCase__    )
													except FileNotFoundError as fnfe:
																	raise fnfe
													except UnicodeError:
																	raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''    )
													return

									UpperCAmelCase__    =							f.readlines()
									for lineTmp in lines:
													UpperCAmelCase__    =							lineTmp.strip()
													UpperCAmelCase__    =							line.rfind(' '    )
													if idx == -1:
																	raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''    )
													UpperCAmelCase__    =							line[:idx]
													UpperCAmelCase__    =							len(self.encoder    )




"""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


lowerCAmelCase__						:						str		  =			'base_with_context'



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )
				for lyr_num, lyr in enumerate(model.encoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

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

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

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

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



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

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

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

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

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

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

				return model



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )

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

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

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

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

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

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

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

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

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

				return model



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							checkpoints.load_tax_checkpoint(args.checkpoint_path )
				UpperCAmelCase__    =							jnp.tree_util.tree_map(onp.array      ,    lowerCamelCase )

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

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

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

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

				UpperCAmelCase__    =							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__":
						lowerCAmelCase__						:						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.',
						)
						lowerCAmelCase__						:						List[str]		  =			parser.parse_args()

						main(args)



