

"""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


__UpperCamelCase    : Optional[Any]				 =						'''base_with_context'''

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Dict    							=      nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
	lowerCAmelCase__      :		Any    							=      nn.Parameter(
	    torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)		,	requires_grad=A_					)
	for lyr_num, lyr in enumerate(model.encoders					):
		lowerCAmelCase__      :		Union[str, Any]    							=      weights[f'layers_{lyr_num}']
		lowerCAmelCase__      :		List[Any]    							=      nn.Parameter(
		    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

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

		lowerCAmelCase__      :		Dict    							=      nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

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

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

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		List[str]    							=      nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T					)					)

	lowerCAmelCase__      :		List[Any]    							=      nn.Parameter(
	    torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)		,	requires_grad=A_					)

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

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

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

	lowerCAmelCase__      :		Dict    							=      nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)

	return model

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		List[str]    							=      nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
	lowerCAmelCase__      :		List[str]    							=      nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

	lowerCAmelCase__      :		Union[str, Any]    							=      nn.Parameter(
	    torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)		,	requires_grad=A_					)

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

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

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

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

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

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

	lowerCAmelCase__      :		int    							=      nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

	lowerCAmelCase__      :		Tuple    							=      nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T					)					)

	return model

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      checkpoints.load_tax_checkpoint(args.checkpoint_path					)
	lowerCAmelCase__      :		Any    							=      jnp.tree_util.tree_map(onp.array		,	A_					)

	lowerCAmelCase__      :		Optional[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()''',
	]

	lowerCAmelCase__      :		Optional[int]    							=      os.path.join(args.checkpoint_path		,	'''..'''		,	'''config.gin'''					)
	lowerCAmelCase__      :		List[str]    							=      inference.parse_training_gin_file(A_		,	A_					)
	lowerCAmelCase__      :		Optional[int]    							=      inference.InferenceModel(args.checkpoint_path		,	A_					)

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

	lowerCAmelCase__      :		Tuple    							=      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'''		,	)

	lowerCAmelCase__      :		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'''		,	)

	lowerCAmelCase__      :		int    							=      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		,	)

	lowerCAmelCase__      :		Union[str, Any]    							=      load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']		,	A_					)
	lowerCAmelCase__      :		Tuple    							=      load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']		,	A_					)
	lowerCAmelCase__      :		Tuple    							=      load_decoder(ta_checkpoint['''target''']['''decoder''']		,	A_					)

	lowerCAmelCase__      :		List[Any]    							=      OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

	lowerCAmelCase__      :		str    							=      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__":
							__UpperCamelCase    : Optional[Any]				 =						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.''',
							)
							__UpperCamelCase    : Union[str, Any]				 =						parser.parse_args()

							main(args)







"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Dict				 =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : List[Any]				 =						{
    '''vocab_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-german-cased''': (
            '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCamelCase    : Union[str, Any]				 =						{
    '''distilbert-base-uncased''': 5_1_2,
    '''distilbert-base-uncased-distilled-squad''': 5_1_2,
    '''distilbert-base-cased''': 5_1_2,
    '''distilbert-base-cased-distilled-squad''': 5_1_2,
    '''distilbert-base-german-cased''': 5_1_2,
    '''distilbert-base-multilingual-cased''': 5_1_2,
}


__UpperCamelCase    : int				 =						{
    '''distilbert-base-uncased''': {'''do_lower_case''': True},
    '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
    '''distilbert-base-cased''': {'''do_lower_case''': False},
    '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
    '''distilbert-base-german-cased''': {'''do_lower_case''': False},
    '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			PRETRAINED_INIT_CONFIGURATION
					lowercase__												=			["input_ids", "attention_mask"]
					lowercase__												=			DistilBertTokenizer







					def __init__(			self     :							int  ,lowercase_     :							List[str]=None  ,lowercase_     :							int=None  ,lowercase_     :							str=True  ,lowercase_     :							Union[str, Any]="[UNK]"  ,lowercase_     :							List[str]="[SEP]"  ,lowercase_     :							List[str]="[PAD]"  ,lowercase_     :							List[str]="[CLS]"  ,lowercase_     :							List[str]="[MASK]"  ,lowercase_     :							Tuple=True  ,lowercase_     :							Tuple=None  ,**lowercase_     :							str  ,):
						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,tokenize_chinese_chars=lowercase_  ,strip_accents=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		Tuple    							=      json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
						if (
						    normalizer_state.get('''lowercase'''  ,lowercase_						) != do_lower_case
						    or normalizer_state.get('''strip_accents'''  ,lowercase_						) != strip_accents
						    or normalizer_state.get('''handle_chinese_chars'''  ,lowercase_						) != tokenize_chinese_chars
						):
							lowerCAmelCase__      :		Union[str, Any]    							=      getattr(lowercase_  ,normalizer_state.pop('''type'''						)						)
							lowerCAmelCase__      :		Optional[int]    							=      do_lower_case
							lowerCAmelCase__      :		List[str]    							=      strip_accents
							lowerCAmelCase__      :		int    							=      tokenize_chinese_chars
							lowerCAmelCase__      :		Dict    							=      normalizer_class(**lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      do_lower_case







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							Tuple  ,lowercase_     :							Optional[Any]=None						):
						lowerCAmelCase__      :		Any    							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		Dict    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [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 			__lowerCAmelCase					(			self     :							Any  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						lowerCAmelCase__      :		Any    							=      self._tokenizer.model.save(lowercase_  ,name=lowercase_						)
						return tuple(lowercase_						)




