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			:						int				  =		"""base_with_context"""



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]       )       )
 __a							=	nn.Parameter(
     torch.FloatTensor(weights["Embed_0"]["embedding"]       )				,					requires_grad=_A       )
 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 	__A		(      _A				,					_A       ):



 """simple docstring"""


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

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

 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 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __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=_A       )

 __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 	__A		(      _A       ):



 """simple docstring"""


 __a							=	checkpoints.load_tax_checkpoint(args.checkpoint_path       )
 __a							=	jnp.tree_util.tree_map(onp.array				,					_A       )

 __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(_A				,					_A       )
 __a							=	inference.InferenceModel(args.checkpoint_path				,					_A       )

 __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"]				,					_A       )
 __a							=	load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]				,					_A       )
 __a							=	load_decoder(ta_checkpoint["target"]["decoder"]				,					_A       )

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						int				  =		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			:						Union[str, Any]				  =		parser.parse_args()

  main(args)



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

 # We have the base model one level deeper than the original XLM repository
 __a							=	{}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   __a							=	v
  else:
   __a							=	v

 __a							=	chkpt["params"]
 __a							=	{n: v for n, v in config.items() if not isinstance(_A				,					(torch.FloatTensor, numpy.ndarray)       )}

 __a							=	chkpt["dico_word2id"]
 __a							=	{s + "</w>" if s.find("@@"       ) == -1 and i > 13 else s.replace("@@"				,					""       ): i for s, i in vocab.items()}

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

 print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
 torch.save(_A				,					_A       )

 print(f"""Save configuration file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )

 print(f"""Save vocab file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


