



'''simple docstring'''




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			def __init__(							self     : int							,     lowerCAmelCase__     : NestedDataStructureLike[PathLike]							,     lowerCAmelCase__     : Optional[NamedSplit] = None							,     lowerCAmelCase__     : Optional[Features] = None							,     lowerCAmelCase__     : str = None							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : Optional[str] = None							,     lowerCAmelCase__     : Optional[int] = None							,     **lowerCAmelCase__     : Optional[int]							,     ):





					"""simple docstring"""






					super().__init__(
					    lowerCAmelCase__							,     split=lowerCAmelCase__							,     features=lowerCAmelCase__							,     cache_dir=lowerCAmelCase__							,     keep_in_memory=lowerCAmelCase__							,     streaming=lowerCAmelCase__							,     num_proc=lowerCAmelCase__							,     **lowerCAmelCase__							,     )
					__SCREAMING_SNAKE_CASE       :		Dict           =		field
					__SCREAMING_SNAKE_CASE       :		Any           =		path_or_paths if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else {self.split: path_or_paths}
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		Json(
					    cache_dir=lowerCAmelCase__							,     data_files=lowerCAmelCase__							,     features=lowerCAmelCase__							,     field=lowerCAmelCase__							,     **lowerCAmelCase__							,     )



			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					if self.streaming:
							__SCREAMING_SNAKE_CASE       :		Any           =		self.builder.as_streaming_dataset(split=self.split						)
					# Build regular (map-style) dataset
					else:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		None
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		None
							__SCREAMING_SNAKE_CASE       :		Dict           =		None
							__SCREAMING_SNAKE_CASE       :		int           =		None

							self.builder.download_and_prepare(
							    download_config=lowerCAmelCase__							,     download_mode=lowerCAmelCase__							,     verification_mode=lowerCAmelCase__							,     base_path=lowerCAmelCase__							,     num_proc=self.num_proc							,     )
							__SCREAMING_SNAKE_CASE       :		Tuple           =		self.builder.as_dataset(
							    split=self.split							,     verification_mode=lowerCAmelCase__							,     in_memory=self.keep_in_memory						)
					return dataset






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : Dict							,     lowerCAmelCase__     : Dataset							,     lowerCAmelCase__     : Union[PathLike, BinaryIO]							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : Optional[int] = None							,     **lowerCAmelCase__     : int							,     ):





					"""simple docstring"""






					if num_proc is not None and num_proc <= 0:
							raise ValueError(F"num_proc {num_proc} must be an integer > 0."						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		dataset
					__SCREAMING_SNAKE_CASE       :		str           =		path_or_buf
					__SCREAMING_SNAKE_CASE       :		int           =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		num_proc
					__SCREAMING_SNAKE_CASE       :		Any           =		"""utf-8"""
					__SCREAMING_SNAKE_CASE       :		Any           =		to_json_kwargs

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		self.to_json_kwargs.pop("""path_or_buf"""							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		self.to_json_kwargs.pop("""orient"""							,     """records"""						)
					__SCREAMING_SNAKE_CASE       :		int           =		self.to_json_kwargs.pop("""lines"""							,     True if orient == """records""" else False						)
					__SCREAMING_SNAKE_CASE       :		str           =		self.to_json_kwargs.pop("""index"""							,     False if orient in ["""split""", """table"""] else True						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.to_json_kwargs.pop("""compression"""							,     lowerCAmelCase__						)

					if compression not in [None, "infer", "gzip", "bz2", "xz"]:
							raise NotImplementedError(F"`datasets` currently does not support {compression} compression"						)

					if isinstance(self.path_or_buf							,     (str, bytes, os.PathLike)						):
							with fsspec.open(self.path_or_buf							,     """wb"""							,     compression=lowerCAmelCase__						) as buffer:
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self._write(file_obj=lowerCAmelCase__							,     orient=lowerCAmelCase__							,     lines=lowerCAmelCase__							,     index=lowerCAmelCase__							,     **self.to_json_kwargs						)
					else:
							if compression:
									raise NotImplementedError(
									    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
									    """ was passed. Please provide a local path instead."""						)
							__SCREAMING_SNAKE_CASE       :		Dict           =		self._write(
							    file_obj=self.path_or_buf							,     orient=lowerCAmelCase__							,     lines=lowerCAmelCase__							,     index=lowerCAmelCase__							,     **self.to_json_kwargs						)
					return written

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		args

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		query_table(
					    table=self.dataset.data							,     key=slice(lowerCAmelCase__							,     offset + self.batch_size						)							,     indices=self.dataset._indices							,     )
					__SCREAMING_SNAKE_CASE       :		Tuple           =		batch.to_pandas().to_json(
					    path_or_buf=lowerCAmelCase__							,     orient=lowerCAmelCase__							,     lines=lowerCAmelCase__							,     index=lowerCAmelCase__							,     **lowerCAmelCase__						)
					if not json_str.endswith("""\n"""						):
							json_str += "\n"
					return json_str.encode(self.encoding						)



			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : BinaryIO							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : List[Any]							,     **lowerCAmelCase__     : int							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		0

					if self.num_proc is None or self.num_proc == 1:
							for offset in logging.tqdm(
							    range(0							,     len(self.dataset						)							,     self.batch_size						)							,     unit="""ba"""							,     disable=not logging.is_progress_bar_enabled()							,     desc="""Creating json from Arrow format"""							,     ):
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self._batch_json((offset, orient, lines, index, to_json_kwargs)						)
									written += file_obj.write(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		len(self.dataset						), self.batch_size
							with multiprocessing.Pool(self.num_proc						) as pool:
									for json_str in logging.tqdm(
									    pool.imap(
									        self._batch_json							,     [(offset, orient, lines, index, to_json_kwargs) for offset in range(0							,     lowerCAmelCase__							,     lowerCAmelCase__						)]							,     )							,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							,     unit="""ba"""							,     disable=not logging.is_progress_bar_enabled()							,     desc="""Creating json from Arrow format"""							,     ):
											written += file_obj.write(lowerCAmelCase__						)

					return written




'''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__       :	List[Any]    = '''base_with_context'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Dict					):
		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)
		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]					)					)
		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   List[str]					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]					)					)

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T					)					)
		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T					)					)

		for lyr_num, lyr in enumerate(model.decoders					):
				__SCREAMING_SNAKE_CASE       :		str           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		ly_weight["""self_attention"""]
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""MultiHeadDotProductAttention_0"""]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T					)					)

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		int           =		checkpoints.load_tax_checkpoint(args.checkpoint_path					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		jnp.tree_util.tree_map(onp.array       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		int           =		[
		    """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()""",
		]

		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		os.path.join(args.checkpoint_path       ,      """.."""       ,      """config.gin"""					)
		__SCREAMING_SNAKE_CASE       :		Any           =		inference.parse_training_gin_file(_lowerCamelCase       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		inference.InferenceModel(args.checkpoint_path       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""       ,      variance_type="""fixed_large"""					)

		__SCREAMING_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"""       ,      )

		__SCREAMING_SNAKE_CASE       :		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"""       ,      )

		__SCREAMING_SNAKE_CASE       :		Tuple           =		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       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		load_decoder(ta_checkpoint["""target"""]["""decoder"""]       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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__":
						UpperCamelCase__       :	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.''',
						)
						UpperCamelCase__       :	List[str]    = parser.parse_args()

						main(args)