"""simple docstring"""






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCAmelCase__						:						Optional[Any]		  =			logging.get_logger(__name__)
class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			Tuple					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[Any]    ):
									UpperCAmelCase__    =							question_encoder
									UpperCAmelCase__    =							generator
									UpperCAmelCase__    =							self.question_encoder







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			Any    ):
									if os.path.isfile(lowerCamelCase__    ):
													raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''    )
									os.makedirs(lowerCamelCase__					,exist_ok=lowerCamelCase__    )
									UpperCAmelCase__    =							os.path.join(lowerCamelCase__					,'question_encoder_tokenizer'    )
									UpperCAmelCase__    =							os.path.join(lowerCamelCase__					,'generator_tokenizer'    )
									self.question_encoder.save_pretrained(lowerCamelCase__    )
									self.generator.save_pretrained(lowerCamelCase__    )







					@classmethod
					def 	__lowerCAmelCase		(       cls     :			str					,lowerCamelCase__     :			Optional[int]					,**lowerCamelCase__     :			Tuple    ):
									# dynamically import AutoTokenizer
									from ..auto.tokenization_auto import AutoTokenizer

									UpperCAmelCase__    =							kwargs.pop('config'					,lowerCamelCase__    )

									if config is None:
													UpperCAmelCase__    =							RagConfig.from_pretrained(lowerCamelCase__    )

									UpperCAmelCase__    =							AutoTokenizer.from_pretrained(
									    lowerCamelCase__					,config=config.question_encoder					,subfolder='question_encoder_tokenizer'    )
									UpperCAmelCase__    =							AutoTokenizer.from_pretrained(
									    lowerCamelCase__					,config=config.generator					,subfolder='generator_tokenizer'    )
									return cls(question_encoder=lowerCamelCase__					,generator=lowerCamelCase__    )







					def __call__(       self     :			List[Any]					,*lowerCamelCase__     :			Union[str, Any]					,**lowerCamelCase__     :			Tuple    ):
									return self.current_tokenizer(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,*lowerCamelCase__     :			Optional[Any]					,**lowerCamelCase__     :			Union[str, Any]    ):
									return self.generator.batch_decode(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,*lowerCamelCase__     :			int					,**lowerCamelCase__     :			Any    ):
									return self.generator.decode(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.question_encoder







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							self.generator







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[List[str]] = None					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			str = "longest"					,lowerCamelCase__     :			str = None					,lowerCamelCase__     :			bool = True					,**lowerCamelCase__     :			Optional[Any]					,):
									warnings.warn(
									    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
									    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
									    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
									    'details'					,lowerCamelCase__					,)
									if max_length is None:
													UpperCAmelCase__    =							self.current_tokenizer.model_max_length
									UpperCAmelCase__    =							self(
									    lowerCamelCase__					,add_special_tokens=lowerCamelCase__					,return_tensors=lowerCamelCase__					,max_length=lowerCamelCase__					,padding=lowerCamelCase__					,truncation=lowerCamelCase__					,**lowerCamelCase__					,)
									if tgt_texts is None:
													return model_inputs
									# Process tgt_texts
									if max_target_length is None:
													UpperCAmelCase__    =							self.current_tokenizer.model_max_length
									UpperCAmelCase__    =							self(
									    text_target=lowerCamelCase__					,add_special_tokens=lowerCamelCase__					,return_tensors=lowerCamelCase__					,padding=lowerCamelCase__					,max_length=lowerCamelCase__					,truncation=lowerCamelCase__					,**lowerCamelCase__					,)
									UpperCAmelCase__    =							labels['input_ids']
									return model_inputs




"""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)



