
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


SCREAMING_SNAKE_CASE :		List[Any]       			=							"base_with_context"



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):
				A_													=	nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']  )  )
				A_													=	nn.Parameter(
				    torch.FloatTensor(weights['''Embed_0''']['''embedding''']  )			,    requires_grad=_SCREAMING_SNAKE_CASE  )
				for lyr_num, lyr in enumerate(model.encoders  ):
								A_													=	weights[F"layers_{lyr_num}"]
								A_													=	nn.Parameter(
								    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']  )  )

								A_													=	ly_weight['''attention''']
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T  )  )

								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']  )  )

								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T  )  )

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



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):
				A_													=	nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T  )  )

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

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

								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(
								    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']  )  )

								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']  )  )

				A_													=	nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']  )  )

				return model



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):
				A_													=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T  )  )
				A_													=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T  )  )

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

				A_													=	nn.Parameter(
				    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T  )  )

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

								A_													=	nn.Parameter(
								    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T  )  )

								A_													=	ly_weight['''self_attention''']
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T  )  )

								A_													=	ly_weight['''MultiHeadDotProductAttention_0''']
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(
								    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']  )  )

								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']  )  )
								A_													=	nn.Parameter(
								    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T  )  )
								A_													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T  )  )

				A_													=	nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']  )  )

				A_													=	nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T  )  )

				return model



def 							lowerCamelCase_	( __UpperCamelCase  ):
				A_													=	checkpoints.load_tax_checkpoint(args.checkpoint_path  )
				A_													=	jnp.tree_util.tree_map(onp.array			,    _SCREAMING_SNAKE_CASE  )

				A_													=	[
				    '''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()''',
				]

				A_													=	os.path.join(args.checkpoint_path			,    '''..'''			,    '''config.gin'''  )
				A_													=	inference.parse_training_gin_file(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE  )
				A_													=	inference.InferenceModel(args.checkpoint_path			,    _SCREAMING_SNAKE_CASE  )

				A_													=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''			,    variance_type='''fixed_large'''  )

				A_													=	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'''			,    )

				A_													=	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'''			,    )

				A_													=	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			,    )

				A_													=	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']			,    _SCREAMING_SNAKE_CASE  )
				A_													=	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']			,    _SCREAMING_SNAKE_CASE  )
				A_													=	load_decoder(ta_checkpoint['''target''']['''decoder''']			,    _SCREAMING_SNAKE_CASE  )

				A_													=	OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''  )

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE :		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.",
						)
						SCREAMING_SNAKE_CASE :		str       			=							parser.parse_args()

						main(args)



'''simple docstring'''





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  			=				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  			=				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCAmelCase__  			=				'''|'''.join(sys.argv[1:])
UpperCAmelCase__  			=				re.compile(rF"""^({joined_dirs}).*?\.py$""")

UpperCAmelCase__  			=				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')



