
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


lowercase						:      List[Any]									= 'base_with_context'




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Optional[int])      ->					List[Any]:






							'''simple docstring'''





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

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

														__UpperCamelCase			:     str         =      nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))

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

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    str)      ->					Tuple:






							'''simple docstring'''





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

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

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

														__UpperCamelCase			:     int         =      nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
														__UpperCamelCase			:     Optional[Any]         =      nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
														__UpperCamelCase			:     str         =      nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
														__UpperCamelCase			:     Dict         =      nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
														__UpperCamelCase			:     Dict         =      nn.Parameter(
														    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]))

														__UpperCamelCase			:     Dict         =      nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
														__UpperCamelCase			:     Dict         =      nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
														__UpperCamelCase			:     Optional[int]         =      nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
														__UpperCamelCase			:     Dict         =      nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))

							__UpperCamelCase			:     List[Any]         =      nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))

							return model




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    str)      ->					Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T))
							__UpperCamelCase			:     Union[str, Any]         =      nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T))

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

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

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

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

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

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

														__UpperCamelCase			:     List[Any]         =      nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
														__UpperCamelCase			:     Tuple         =      nn.Parameter(
														    torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T))
														__UpperCamelCase			:     str         =      nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
														__UpperCamelCase			:     Dict         =      nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
														__UpperCamelCase			:     Tuple         =      nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))

							__UpperCamelCase			:     Dict         =      nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]))

							__UpperCamelCase			:     Union[str, Any]         =      nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T))

							return model




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      checkpoints.load_tax_checkpoint(args.checkpoint_path)
							__UpperCamelCase			:     Optional[int]         =      jnp.tree_util.tree_map(onp.array		,						_lowerCamelCase)

							__UpperCamelCase			:     Optional[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()",
							]

							__UpperCamelCase			:     Tuple         =      os.path.join(args.checkpoint_path		,						".."		,						"config.gin")
							__UpperCamelCase			:     Optional[Any]         =      inference.parse_training_gin_file(_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase			:     Dict         =      inference.InferenceModel(args.checkpoint_path		,						_lowerCamelCase)

							__UpperCamelCase			:     Union[str, Any]         =      DDPMScheduler(beta_schedule="squaredcos_cap_v2"		,						variance_type="fixed_large")

							__UpperCamelCase			:     Union[str, 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"		,						)

							__UpperCamelCase			:     str         =      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			:     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		,						)

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

							__UpperCamelCase			:     Tuple         =      OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder")

							__UpperCamelCase			:     Optional[int]         =      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__":
		lowercase						:      Any									= 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.',
		)
		lowercase						:      Tuple									= parser.parse_args()

		main(args)

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      Any									= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)