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        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=  checkpoints.load_tax_checkpoint(__A							)
      UpperCAmelCase__								=  flatten_dict(__A							)
      return flax_params




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  {}

      UpperCAmelCase__								=  {
          "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",
      }

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

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

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

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

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

                  UpperCAmelCase__								=  flax_dict[key]

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

      return converted_torch_dict




def        lowerCAmelCase_			(					__A,  __A,  __A=False,  __A=False							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  get_flax_param(__A							)

      if not use_large:
            UpperCAmelCase__								=  PixaStructVisionConfig()
            UpperCAmelCase__								=  PixaStructTextConfig()
      else:
            UpperCAmelCase__								=  PixaStructVisionConfig(
                hidden_size=1_536,  d_ff=3_968,  num_attention_heads=24,  num_hidden_layers=18							)
            UpperCAmelCase__								=  PixaStructTextConfig(hidden_size=1_536,  d_ff=3_968,  num_heads=24,  num_layers=18							)
      UpperCAmelCase__								=  PixaStructConfig(
          vision_config=encoder_config.to_dict(),  text_config=decoder_config.to_dict(),  is_vqa=__A							)

      UpperCAmelCase__								=  PixaStructForConditionalGeneration(__A							)

      UpperCAmelCase__								=  rename_and_convert_flax_params(__A							)
      model.load_state_dict(__A							)

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

      if use_large:
            UpperCAmelCase__								=  4_096

      UpperCAmelCase__								=  True

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

      model.save_pretrained(__A							)
      processor.save_pretrained(__A							)

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


if __name__ == "__main__":
      UpperCamelCase__    							= 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.')
      UpperCamelCase__    							= parser.parse_args()

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




def        lowerCAmelCase_			(					__A							)					->    str:







      '''simple docstring'''


      return " ".join(input_str.split()[::-1]							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



