"""simple docstring"""






import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput
class        snake_case (				_SCREAMING_SNAKE_CASE       ):




					"""simple docstring"""

					def __init__(       self     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							dataset
									UpperCAmelCase__    =							process
									UpperCAmelCase__    =							params







					def __len__(       self     :			Tuple    ):
									return len(self.dataset    )







					def __getitem__(       self     :			Any					,lowerCamelCase__     :			Dict    ):
									UpperCAmelCase__    =							self.dataset[i]
									UpperCAmelCase__    =							self.process(A_					,**self.params    )
									return processed






class        snake_case (				_SCREAMING_SNAKE_CASE       ):




					"""simple docstring"""

					def __init__(       self     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Union[str, Any]=None    ):
									UpperCAmelCase__    =							loader
									UpperCAmelCase__    =							infer
									UpperCAmelCase__    =							params
									if loader_batch_size == 1:
													# Let's spare some time by deactivating altogether
													UpperCAmelCase__    =							None
									UpperCAmelCase__    =							loader_batch_size

									# Internal bookkeeping
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							None







					def __len__(       self     :			Optional[int]    ):
									return len(self.loader    )







					def __iter__(       self     :			str    ):
									UpperCAmelCase__    =							iter(self.loader    )
									return self







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									if isinstance(self._loader_batch_data					,torch.Tensor    ):
													# Batch data is simple tensor, just fetch the slice
													UpperCAmelCase__    =							self._loader_batch_data[self._loader_batch_index]
									else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													UpperCAmelCase__    =							{}
													for k, element in self._loader_batch_data.items():
																	if isinstance(A_					,A_    ):
																					# Convert ModelOutput to tuple first
																					UpperCAmelCase__    =							element.to_tuple()
																					if isinstance(element[0]					,torch.Tensor    ):
																									UpperCAmelCase__    =							tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																					elif isinstance(element[0]					,np.ndarray    ):
																									UpperCAmelCase__    =							tuple(np.expand_dims(el[self._loader_batch_index]					,0    ) for el in element    )
																					continue
																	if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(A_					,A_    ):
																					# Those are stored as lists of tensors so need specific unbatching.
																					if isinstance(element[0]					,torch.Tensor    ):
																									UpperCAmelCase__    =							tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																					elif isinstance(element[0]					,np.ndarray    ):
																									UpperCAmelCase__    =							tuple(np.expand_dims(el[self._loader_batch_index]					,0    ) for el in element    )
																					continue
																	if element is None:
																					# This can happen for optional data that get passed around
																					UpperCAmelCase__    =							None
																	elif isinstance(element[self._loader_batch_index]					,torch.Tensor    ):
																					# Take correct batch data, but make it looked like batch_size=1
																					# For compatibility with other methods within transformers

																					UpperCAmelCase__    =							element[self._loader_batch_index].unsqueeze(0    )
																	elif isinstance(element[self._loader_batch_index]					,np.ndarray    ):
																					# Take correct batch data, but make it looked like batch_size=1
																					# For compatibility with other methods within transformers
																					UpperCAmelCase__    =							np.expand_dims(element[self._loader_batch_index]					,0    )
																	else:
																					# This is typically a list, so no need to `unsqueeze`.
																					UpperCAmelCase__    =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
													UpperCAmelCase__    =							self._loader_batch_data.__class__(A_    )
									self._loader_batch_index += 1
									return result







					def 	__lowerCAmelCase		(       self     :			Any    ):
									if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													# We are currently unrolling a batch so we just need to return
													# the current item within a batch
													return self.loader_batch_item()

									# We're out of items within a batch
									UpperCAmelCase__    =							next(self.iterator    )
									UpperCAmelCase__    =							self.infer(A_					,**self.params    )
									# We now have a batch of "inferred things".
									if self.loader_batch_size is not None:
													# Try to infer the size of the batch
													if isinstance(A_					,torch.Tensor    ):
																	UpperCAmelCase__    =							processed
													else:
																	UpperCAmelCase__    =							list(processed.keys()    )[0]
																	UpperCAmelCase__    =							processed[key]
													if isinstance(A_					,A_    ):
																	UpperCAmelCase__    =							len(A_    )
													else:
																	UpperCAmelCase__    =							first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																	# could be last batch so we can't unroll as many
																	# elements.
																	UpperCAmelCase__    =							observed_batch_size
													# Setting internal index to unwrap the batch
													UpperCAmelCase__    =							processed
													UpperCAmelCase__    =							0
													return self.loader_batch_item()
									else:
													# We're not unrolling batches
													return processed






class        snake_case (				_SCREAMING_SNAKE_CASE       ):




					"""simple docstring"""

					def __init__(       self     :			Union[str, Any]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			str=None    ):
									super().__init__(A_					,A_					,A_    )







					def __iter__(       self     :			str    ):
									UpperCAmelCase__    =							iter(self.loader    )
									UpperCAmelCase__    =							None
									return self







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									if self.subiterator is None:
													UpperCAmelCase__    =							self.infer(next(self.iterator    )					,**self.params    )
									try:
													# Try to return next item
													UpperCAmelCase__    =							next(self.subiterator    )
									except StopIteration:
													# When a preprocess iterator ends, we can start lookig at the next item
													# ChunkIterator will keep feeding until ALL elements of iterator
													# all have created their subiterator and have been iterating against.
													#
													# Another way to look at it, is we're basically flattening lists of lists
													# into a single list, but with generators
													UpperCAmelCase__    =							self.infer(next(self.iterator    )					,**self.params    )
													UpperCAmelCase__    =							next(self.subiterator    )
									return processed






class        snake_case (				_SCREAMING_SNAKE_CASE       ):




					"""simple docstring"""

					def __iter__(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							iter(self.loader    )
									return self







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							False
									UpperCAmelCase__    =							[]
									if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													while self._loader_batch_index < self.loader_batch_size:
																	UpperCAmelCase__    =							self.loader_batch_item()
																	UpperCAmelCase__    =							item.pop('is_last'    )
																	accumulator.append(A_    )
																	if is_last:
																					return accumulator

									while not is_last:
													UpperCAmelCase__    =							self.infer(next(self.iterator    )					,**self.params    )
													if self.loader_batch_size is not None:
																	if isinstance(A_					,torch.Tensor    ):
																					UpperCAmelCase__    =							processed
																	else:
																					UpperCAmelCase__    =							list(processed.keys()    )[0]
																					UpperCAmelCase__    =							processed[key]
																	if isinstance(A_					,A_    ):
																					UpperCAmelCase__    =							len(A_    )
																	else:
																					UpperCAmelCase__    =							first_tensor.shape[0]
																	if 0 < observed_batch_size < self.loader_batch_size:
																					# could be last batch so we can't unroll as many
																					# elements.
																					UpperCAmelCase__    =							observed_batch_size
																	UpperCAmelCase__    =							processed
																	UpperCAmelCase__    =							0
																	while self._loader_batch_index < self.loader_batch_size:
																					UpperCAmelCase__    =							self.loader_batch_item()
																					UpperCAmelCase__    =							item.pop('is_last'    )
																					accumulator.append(A_    )
																					if is_last:
																									return accumulator
													else:
																	UpperCAmelCase__    =							processed
																	UpperCAmelCase__    =							item.pop('is_last'    )
																	accumulator.append(A_    )
									return accumulator






class        snake_case (				_SCREAMING_SNAKE_CASE       ):




					"""simple docstring"""

					def __init__(       self     :			Optional[int]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							dataset
									UpperCAmelCase__    =							key







					def __len__(       self     :			Optional[int]    ):
									return len(self.dataset    )







					def __getitem__(       self     :			Union[str, Any]					,lowerCamelCase__     :			int    ):
									return self.dataset[i][self.key]






class        snake_case (				_SCREAMING_SNAKE_CASE       ):




					"""simple docstring"""

					def __init__(       self     :			Optional[int]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Dict    ):
									UpperCAmelCase__    =							dataset
									UpperCAmelCase__    =							keya
									UpperCAmelCase__    =							keya







					def __len__(       self     :			Union[str, Any]    ):
									return len(self.dataset    )







					def __getitem__(       self     :			int					,lowerCamelCase__     :			List[Any]    ):
									return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




"""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)



