


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


A			:						Optional[Any]        =  'base_with_context'






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]	)	)
		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)       ,       requires_grad=__magic_name__	)
		for lyr_num, lyr in enumerate(model.encoders	):
				lowercase__	     =						weights[f'''layers_{lyr_num}''']
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						ly_weight["""attention"""]
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)

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






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				str	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T	)	)

		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)       ,       requires_grad=__magic_name__	)

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

				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)

		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]	)	)

		return model






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Tuple	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T	)	)
		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T	)	)

		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)       ,       requires_grad=__magic_name__	)

		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T	)	)

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

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

				lowercase__	     =						ly_weight["""self_attention"""]
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

				lowercase__	     =						ly_weight["""MultiHeadDotProductAttention_0"""]
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)

		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]	)	)

		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T	)	)

		return model






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						checkpoints.load_tax_checkpoint(args.checkpoint_path	)
		lowercase__	     =						jnp.tree_util.tree_map(onp.array       ,       __magic_name__	)

		lowercase__	     =						[
		    """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()""",
		]

		lowercase__	     =						os.path.join(args.checkpoint_path       ,       """.."""       ,       """config.gin"""	)
		lowercase__	     =						inference.parse_training_gin_file(__magic_name__       ,       __magic_name__	)
		lowercase__	     =						inference.InferenceModel(args.checkpoint_path       ,       __magic_name__	)

		lowercase__	     =						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""       ,       variance_type="""fixed_large"""	)

		lowercase__	     =						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"""       ,       )

		lowercase__	     =						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"""       ,       )

		lowercase__	     =						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       ,       )

		lowercase__	     =						load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]       ,       __magic_name__	)
		lowercase__	     =						load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]       ,       __magic_name__	)
		lowercase__	     =						load_decoder(ta_checkpoint["""target"""]["""decoder"""]       ,       __magic_name__	)

		lowercase__	     =						OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""	)

		lowercase__	     =						SpectrogramDiffusionPipeline(
		    notes_encoder=__magic_name__       ,       continuous_encoder=__magic_name__       ,       decoder=__magic_name__       ,       scheduler=__magic_name__       ,       melgan=__magic_name__       ,       )
		if args.save:
				pipe.save_pretrained(args.output_path	)


if __name__ == "__main__":
	A			:						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.',
	)
	A			:						Optional[Any]        =  parser.parse_args()

	main(args)





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@slow
					@require_torch
					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,   """prajjwal1/bert-tiny"""						)
							lowercase__	     =						BertTokenizer.from_pretrained("""bert-base-uncased"""						)

							lowercase__	     =						bertabert.config.encoder.vocab_size
							lowercase__	     =						tokenizer.sep_token_id
							lowercase__	     =						tokenizer.cls_token_id
							lowercase__	     =						128

							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""train[:1%]"""						)
							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""validation[:1%]"""						)

							lowercase__	     =						train_dataset.select(range(32						)						)
							lowercase__	     =						val_dataset.select(range(16						)						)

							lowercase__	     =						4

							def _map_to_encoder_decoder_inputs(_UpperCAmelCase       :		Any						):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									lowercase__	     =						tokenizer(batch["""article"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=512						)
									lowercase__	     =						tokenizer(batch["""highlights"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=128						)
									lowercase__	     =						inputs.input_ids
									lowercase__	     =						inputs.attention_mask

									lowercase__	     =						outputs.input_ids
									lowercase__	     =						outputs.input_ids.copy()
									lowercase__	     =						[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									lowercase__	     =						outputs.attention_mask

									assert all(len(_UpperCAmelCase						) == 512 for x in inputs.input_ids						)
									assert all(len(_UpperCAmelCase						) == 128 for x in outputs.input_ids						)

									return batch

							def _compute_metrics(_UpperCAmelCase       :		List[Any]						):
									lowercase__	     =						pred.label_ids
									lowercase__	     =						pred.predictions

									# all unnecessary tokens are removed
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)

									lowercase__	     =						sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__	     =						train_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							train_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							# same for validation dataset
							lowercase__	     =						val_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							val_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							lowercase__	     =						self.get_auto_remove_tmp_dir()

							lowercase__	     =						SeqaSeqTrainingArguments(
							    output_dir=_UpperCAmelCase ,   per_device_train_batch_size=_UpperCAmelCase ,   per_device_eval_batch_size=_UpperCAmelCase ,   predict_with_generate=_UpperCAmelCase ,   evaluation_strategy="""steps""" ,   do_train=_UpperCAmelCase ,   do_eval=_UpperCAmelCase ,   warmup_steps=0 ,   eval_steps=2 ,   logging_steps=2 ,   )

							# instantiate trainer
							lowercase__	     =						SeqaSeqTrainer(
							    model=_UpperCAmelCase ,   args=_UpperCAmelCase ,   compute_metrics=_compute_metrics ,   train_dataset=_UpperCAmelCase ,   eval_dataset=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   )

							# start training
							trainer.train()

