





import os
import sys

_snake_case           =			os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_snake_case           =			[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]


@add_start_docstrings(AutoConfig.__doc__	)
def _A       (   *__magic_name__  , **__magic_name__	):
   return AutoConfig.from_pretrained(*__magic_name__  , **__magic_name__	)


@add_start_docstrings(AutoTokenizer.__doc__	)
def _A       (   *__magic_name__  , **__magic_name__	):
   return AutoTokenizer.from_pretrained(*__magic_name__  , **__magic_name__	)


@add_start_docstrings(AutoModel.__doc__	)
def _A       (   *__magic_name__  , **__magic_name__	):
   return AutoModel.from_pretrained(*__magic_name__  , **__magic_name__	)


@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def _A       (   *__magic_name__  , **__magic_name__	):
   return AutoModelForCausalLM.from_pretrained(*__magic_name__  , **__magic_name__	)


@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def _A       (   *__magic_name__  , **__magic_name__	):
   return AutoModelForMaskedLM.from_pretrained(*__magic_name__  , **__magic_name__	)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def _A       (   *__magic_name__  , **__magic_name__	):
   return AutoModelForSequenceClassification.from_pretrained(*__magic_name__  , **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def _A       (   *__magic_name__  , **__magic_name__	):
   return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__  , **__magic_name__	)













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       (   __magic_name__	):
   lowercase__			       =  checkpoints.load_tax_checkpoint(__magic_name__	)
   lowercase__			       =  flatten_dict(__magic_name__	)
   return flax_params


def _A       (   __magic_name__	):
   lowercase__			       =  {}

   lowercase__			       =  {
       "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",
   }

   lowercase__			       =  {
       "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
         lowercase__			       =  ".".join(key[1:]	)

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

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

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

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

         lowercase__			       =  flax_dict[key]

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

   return converted_torch_dict


def _A       (   __magic_name__  , __magic_name__  , __magic_name__=False  , __magic_name__=False	):
   lowercase__			       =  get_flax_param(__magic_name__	)

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

   lowercase__			       =  PixaStructForConditionalGeneration(__magic_name__	)

   lowercase__			       =  rename_and_convert_flax_params(__magic_name__	)
   model.load_state_dict(__magic_name__	)

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

   if use_large:
      lowercase__			       =  4096

   lowercase__			       =  True

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

   model.save_pretrained(__magic_name__	)
   processor.save_pretrained(__magic_name__	)

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


if __name__ == "__main__":
  _snake_case           =			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.""")
  _snake_case           =			parser.parse_args()

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






