





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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			):






				"""simple docstring"""


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

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

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

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

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			):






				"""simple docstring"""


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

				snake_case_						:   Optional[int]           =  nn.Parameter(
				    torch.FloatTensor(weights['''Embed_0''']['''embedding''']			)			,    requires_grad=A_			)

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

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

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

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

				return model






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			):






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T			)			)
				snake_case_						:   Any           =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T			)			)

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

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

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

								snake_case_						:   List[str]           =  nn.Parameter(
								    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T			)			)

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

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

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

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

				snake_case_						:   List[Any]           =  nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T			)			)

				return model






def 					lowerCamelCase_						(    _UpperCamelCase			):






				"""simple docstring"""


				snake_case_						:   int           =  checkpoints.load_tax_checkpoint(args.checkpoint_path			)
				snake_case_						:   Dict           =  jnp.tree_util.tree_map(onp.array			,    A_			)

				snake_case_						:   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()''',
				]

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

				snake_case_						:   int           =  DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''			,    variance_type='''fixed_large'''			)

				snake_case_						:   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'''			,    )

				snake_case_						:   Optional[int]           =  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'''			,    )

				snake_case_						:   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			,    )

				snake_case_						:   List[Any]           =  load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']			,    A_			)
				snake_case_						:   List[str]           =  load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']			,    A_			)
				snake_case_						:   str           =  load_decoder(ta_checkpoint['''target''']['''decoder''']			,    A_			)

				snake_case_						:   Any           =  OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''			)

				snake_case_						:   Any           =  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__":
	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)







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''image_processor''', '''tokenizer''']
			lowerCamelCase_					:					List[Any]	      =				'''BlipImageProcessor'''
			lowerCamelCase_					:					Union[str, Any]	      =				'''AutoTokenizer'''






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )

							# add QFormer tokenizer
							snake_case_						:   Optional[Any]           =  qformer_tokenizer






			def __call__(self ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = 0 ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = True ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



							if images is None and text is None:
											raise ValueError('''You have to specify at least images or text.'''   )

							snake_case_						:   Tuple           =  BatchFeature()

							if text is not None:
											snake_case_						:   Tuple           =  self.tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											encoding.update(__magic_name__   )
											snake_case_						:   Optional[Any]           =  self.qformer_tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											snake_case_						:   Optional[int]           =  qformer_text_encoding.pop('''input_ids'''   )
											snake_case_						:   Tuple           =  qformer_text_encoding.pop('''attention_mask'''   )

							if images is not None:
											snake_case_						:   Any           =  self.image_processor(__magic_name__ ,							return_tensors=__magic_name__   )
											encoding.update(__magic_name__   )

							return encoding






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							return self.tokenizer.batch_decode(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							return self.tokenizer.decode(*__magic_name__ ,							**__magic_name__   )






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer.model_input_names
							snake_case_						:   List[Any]           =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							if os.path.isfile(__magic_name__   ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(__magic_name__ ,							exist_ok=__magic_name__   )
							snake_case_						:   Any           =  os.path.join(__magic_name__ ,							'''qformer_tokenizer'''   )
							self.qformer_tokenizer.save_pretrained(__magic_name__   )
							return super().save_pretrained(__magic_name__ ,							**__magic_name__   )






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  AutoTokenizer.from_pretrained(__magic_name__ ,							subfolder='''qformer_tokenizer'''   )
							snake_case_						:   str           =  cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__   )
							args.append(__magic_name__   )
							return cls(*__magic_name__   )
