


"""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     =  'base_with_context'


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

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

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

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

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


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T  )  )

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

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

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

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

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

				return model


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Union[str, Any]  ):
				SCREAMING_SNAKE_CASE__     							=				nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T  )  )
				SCREAMING_SNAKE_CASE__     							=				nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T  )  )

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

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

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

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

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

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

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

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

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

				return model


def __lowercase     (      lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				checkpoints.load_tax_checkpoint(args.checkpoint_path  )
				SCREAMING_SNAKE_CASE__     							=				jnp.tree_util.tree_map(onp.array						,					lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				[
				    "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__     							=				os.path.join(args.checkpoint_path						,					".."						,					"config.gin"  )
				SCREAMING_SNAKE_CASE__     							=				inference.parse_training_gin_file(lowerCamelCase_						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				inference.InferenceModel(args.checkpoint_path						,					lowerCamelCase_  )

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

				SCREAMING_SNAKE_CASE__     							=				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__     							=				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__     							=				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__     							=				load_notes_encoder(ta_checkpoint["target"]["token_encoder"]						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				load_decoder(ta_checkpoint["target"]["decoder"]						,					lowerCamelCase_  )

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

				SCREAMING_SNAKE_CASE__     							=				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     =  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     =  parser.parse_args()

	main(args)




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      Tuple  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				SCREAMING_SNAKE_CASE__     							=				len(lowerCamelCase_  ) if (len(lowerCamelCase_  ) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8  )						,					"Stack".center(lowerCamelCase_  )						,					"Postfix".center(lowerCamelCase_  )						,					sep=" | "						,					)
				print("-" * (print_width * 3 + 7)  )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase_  )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase_  )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()  )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase_  ) == 0:
																stack.append(lowerCamelCase_  )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase_  ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()  )  # pop stack & add to Postfix
																stack.append(lowerCamelCase_  )  # push x to stack

								print(
								    x.center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				while len(lowerCamelCase_  ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()  )  # pop stack & add to Postfix
								print(
								    " ".center(8  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					("".join(lowerCamelCase_  )).ljust(lowerCamelCase_  )						,					sep=" | "						,					)  # Output in tabular format

				return "".join(lowerCamelCase_  )  # return Postfix as str


def __lowercase     (      lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				list(infix[::-1]  )  # reverse the infix equation

				for i in range(len(lowerCamelCase_  )  ):
								if infix[i] == "(":
												SCREAMING_SNAKE_CASE__     							=				")"  # change "(" to ")"
								elif infix[i] == ")":
												SCREAMING_SNAKE_CASE__     							=				"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase_  )  ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
	_lowerCamelCase     =  input('\nEnter an Infix Equation = ')  # Input an Infix equation
	_lowerCamelCase     =  ''.join(Infix.split())  # Remove spaces from the input
	print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')
