





"""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 					__lowerCamelCase       (							a_					:		Optional[int]							)      ->				str:
      __SCREAMING_SNAKE_CASE       :Any		    =  checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  flatten_dict(__SCREAMING_SNAKE_CASE							)
      return flax_params



def 					__lowerCamelCase       (							a_					:		Union[str, Any]							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {}

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {
          "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",
      }

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {
          "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
                  __SCREAMING_SNAKE_CASE       :str		    =  ".".join(key[1:]							)

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

                  if "decoder" in new_key:
                        for old, new in DECODER_CONVERSION_MAPPING.items():
                              __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  new_key.replace(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE							)

                  if "layers" in new_key and "decoder" not in new_key:
                        # use regex to replace the layer number
                        __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  re.sub(r'''layers_(\d+)'''			,       r'''layer.\1'''			,       __SCREAMING_SNAKE_CASE							)
                        __SCREAMING_SNAKE_CASE       :Tuple		    =  new_key.replace('''encoder'''			,       '''encoder.encoder'''							)

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

                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  flax_dict[key]

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

      return converted_torch_dict



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[Any]			,       a_					:		List[Any]=False			,       a_					:		List[Any]=False							)      ->				Dict:
      __SCREAMING_SNAKE_CASE       :str		    =  get_flax_param(__SCREAMING_SNAKE_CASE							)

      if not use_large:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  PixaStructVisionConfig()
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  PixaStructTextConfig()
      else:
            __SCREAMING_SNAKE_CASE       :str		    =  PixaStructVisionConfig(
                hidden_size=15_36			,       d_ff=39_68			,       num_attention_heads=24			,       num_hidden_layers=18							)
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  PixaStructTextConfig(hidden_size=15_36			,       d_ff=39_68			,       num_heads=24			,       num_layers=18							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  PixaStructConfig(
          vision_config=encoder_config.to_dict()			,       text_config=decoder_config.to_dict()			,       is_vqa=__SCREAMING_SNAKE_CASE							)

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE							)
      model.load_state_dict(__SCREAMING_SNAKE_CASE							)

      __SCREAMING_SNAKE_CASE       :Tuple		    =  AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
      __SCREAMING_SNAKE_CASE       :Any		    =  PixaStructImageProcessor()
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE			,       tokenizer=__SCREAMING_SNAKE_CASE							)

      if use_large:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  40_96

      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  True

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

      model.save_pretrained(__SCREAMING_SNAKE_CASE							)
      processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

      print('''Model saved in {}'''.format(__SCREAMING_SNAKE_CASE							)							)


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

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






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		int = 10			,       a_					:		int = 22							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  range(1			,       a_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  range(1			,       a_							)
      return sum(
          1 for power in powers for base in bases if len(str(base**power							)							) == power							)


if __name__ == "__main__":
  print(f'{solution(1_0, 2_2) = }')