




"""simple docstring"""

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)




def     _A					( __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						checkpoints.load_tax_checkpoint(_lowerCAmelCase    )
       lowerCamelCase__          =						flatten_dict(_lowerCAmelCase    )
       return flax_params




def     _A					( __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						{}

       lowerCamelCase__          =						{
           """token_embedder""": """embeddings""",
           """encoder_norm""": """layernorm""",
           """kernel""": """weight""",
           """.out""": """.output""",
           """scale""": """weight""",
           """embedders_0.pos_embedding""": """row_embedder.weight""",
           """embedders_1.pos_embedding""": """column_embedder.weight""",
       }

       lowerCamelCase__          =						{
           """query""": """attention.query""",
           """key""": """attention.key""",
           """value""": """attention.value""",
           """output.dense""": """output""",
           """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
           """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
           """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
           """mlp.""": """mlp.DenseReluDense.""",
           """pre_mlp_layer_norm""": """mlp.layer_norm""",
           """self_attention.o""": """self_attention.attention.o""",
           """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
           """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
           """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
           """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
       }

       for key in flax_dict.keys():
              if "target" in key:
                     # remove the first prefix from the key
                     lowerCamelCase__          =						""".""".join(key[1:]    )

                     # rename the key
                     for old, new in CONVERSION_MAPPING.items():
                            lowerCamelCase__          =						new_key.replace(_lowerCAmelCase							,     _lowerCAmelCase    )

                     if "decoder" in new_key:
                            for old, new in DECODER_CONVERSION_MAPPING.items():
                                   lowerCamelCase__          =						new_key.replace(_lowerCAmelCase							,     _lowerCAmelCase    )

                     if "layers" in new_key and "decoder" not in new_key:
                            # use regex to replace the layer number
                            lowerCamelCase__          =						re.sub(r"""layers_(\d+)"""							,     r"""layer.\1"""							,     _lowerCAmelCase    )
                            lowerCamelCase__          =						new_key.replace("""encoder"""							,     """encoder.encoder"""    )

                     elif "layers" in new_key and "decoder" in new_key:
                            # use regex to replace the layer number
                            lowerCamelCase__          =						re.sub(r"""layers_(\d+)"""							,     r"""layer.\1"""							,     _lowerCAmelCase    )

                     lowerCamelCase__          =						flax_dict[key]

       lowerCamelCase__          =						{}
       # convert converted_dict into torch format
       for key in converted_dict.keys():
              if ("embed_tokens" not in key) and ("embedder" not in key):
                     lowerCamelCase__          =						torch.from_numpy(converted_dict[key].T    )
              else:
                     lowerCamelCase__          =						torch.from_numpy(converted_dict[key]    )

       return converted_torch_dict




def     _A					( __lowercase							,     __lowercase							,     __lowercase=False							,     __lowercase=False    ):





       """simple docstring"""



       lowerCamelCase__          =						get_flax_param(_lowerCAmelCase    )

       if not use_large:
              lowerCamelCase__          =						PixaStructVisionConfig()
              lowerCamelCase__          =						PixaStructTextConfig()
       else:
              lowerCamelCase__          =						PixaStructVisionConfig(
                  hidden_size=1536							,     d_ff=3968							,     num_attention_heads=24							,     num_hidden_layers=18    )
              lowerCamelCase__          =						PixaStructTextConfig(hidden_size=1536							,     d_ff=3968							,     num_heads=24							,     num_layers=18    )
       lowerCamelCase__          =						PixaStructConfig(
           vision_config=encoder_config.to_dict()							,     text_config=decoder_config.to_dict()							,     is_vqa=_lowerCAmelCase    )

       lowerCamelCase__          =						PixaStructForConditionalGeneration(_lowerCAmelCase    )

       lowerCamelCase__          =						rename_and_convert_flax_params(_lowerCAmelCase    )
       model.load_state_dict(_lowerCAmelCase    )

       lowerCamelCase__          =						AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
       lowerCamelCase__          =						PixaStructImageProcessor()
       lowerCamelCase__          =						PixaStructProcessor(image_processor=_lowerCAmelCase							,     tokenizer=_lowerCAmelCase    )

       if use_large:
              lowerCamelCase__          =						4096

       lowerCamelCase__          =						True

       # mkdir if needed
       os.makedirs(_lowerCAmelCase							,     exist_ok=_lowerCAmelCase    )

       model.save_pretrained(_lowerCAmelCase    )
       processor.save_pretrained(_lowerCAmelCase    )

       print("""Model saved in {}""".format(_lowerCAmelCase    )    )


if __name__ == "__main__":
  __magic_name__				     =     argparse.ArgumentParser()
  parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
  parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
  __magic_name__				     =     parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )











'''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


__a  			=						"base_with_context"





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
      snake_case__    :       Dict       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)
      for lyr_num, lyr in enumerate(model.encoders						):
            snake_case__    :       List[str]       		= weights[f"layers_{lyr_num}"]
            snake_case__    :       Dict       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

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

            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

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

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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Any]:
      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

      snake_case__    :       str       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

      for lyr_num, lyr in enumerate(model.encoders						):
            snake_case__    :       List[Any]       		= weights[f"layers_{lyr_num}"]
            snake_case__    :       List[str]       		= ly_weight["""attention"""]

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

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

      snake_case__    :       str       		= nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)

      return model





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[str]:
      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T						)						)
      snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T						)						)

      snake_case__    :       List[Any]       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

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

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

            snake_case__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)

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

            snake_case__    :       int       		= ly_weight["""MultiHeadDotProductAttention_0"""]
            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)
            snake_case__    :       Union[str, Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)
            snake_case__    :       str       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
            snake_case__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]						)						)

      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T						)						)

      return model





def 		__snake_case(							_lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Tuple       		= checkpoints.load_tax_checkpoint(args.checkpoint_path						)
      snake_case__    :       List[str]       		= jnp.tree_util.tree_map(onp.array	,  _lowerCAmelCase						)

      snake_case__    :       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()""",
      ]

      snake_case__    :       Any       		= os.path.join(args.checkpoint_path	,  """.."""	,  """config.gin"""						)
      snake_case__    :       Dict       		= inference.parse_training_gin_file(_lowerCAmelCase	,  _lowerCAmelCase						)
      snake_case__    :       int       		= inference.InferenceModel(args.checkpoint_path	,  _lowerCAmelCase						)

      snake_case__    :       Optional[int]       		= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""	,  variance_type="""fixed_large"""						)

      snake_case__    :       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"""	,  )

      snake_case__    :       Union[str, Any]       		= 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"""	,  )

      snake_case__    :       Any       		= 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	,  )

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

      snake_case__    :       str       		= OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""						)

      snake_case__    :       Dict       		= 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__":
       __a  			=						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  			=						parser.parse_args()

       main(args)
