





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	(		SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  a__     : Any  =checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE						)
  a__     : Any  =flatten_dict(SCREAMING_SNAKE_CASE						)
  return flax_params
def 					_A	(		SCREAMING_SNAKE_CASE					:       str						):





  """simple docstring"""



  a__     : Optional[Any]  ={}

  a__     : 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",
  }

  a__     : Dict  ={
      "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
      a__     : Union[str, Any]  =".".join(key[1:]						)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        a__     : Optional[Any]  =new_key.replace(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          a__     : Tuple  =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
        a__     : int  =re.sub(r"layers_(\d+)"	,	r"layer.\1"	,	SCREAMING_SNAKE_CASE						)
        a__     : List[str]  =new_key.replace("encoder"	,	"encoder.encoder"						)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        a__     : Optional[int]  =re.sub(r"layers_(\d+)"	,	r"layer.\1"	,	SCREAMING_SNAKE_CASE						)

      a__     : int  =flax_dict[key]

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

  return converted_torch_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       int=False	,	SCREAMING_SNAKE_CASE					:       Any=False						):





  """simple docstring"""



  a__     : List[Any]  =get_flax_param(SCREAMING_SNAKE_CASE						)

  if not use_large:
    a__     : Any  =PixaStructVisionConfig()
    a__     : Optional[Any]  =PixaStructTextConfig()
  else:
    a__     : Tuple  =PixaStructVisionConfig(
        hidden_size=1_536	,	d_ff=3_968	,	num_attention_heads=24	,	num_hidden_layers=18						)
    a__     : Union[str, Any]  =PixaStructTextConfig(hidden_size=1_536	,	d_ff=3_968	,	num_heads=24	,	num_layers=18						)
  a__     : str  =PixaStructConfig(
      vision_config=encoder_config.to_dict()	,	text_config=decoder_config.to_dict()	,	is_vqa=SCREAMING_SNAKE_CASE						)

  a__     : int  =PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE						)

  a__     : Tuple  =rename_and_convert_flax_params(SCREAMING_SNAKE_CASE						)
  model.load_state_dict(SCREAMING_SNAKE_CASE						)

  a__     : int  =AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"						)
  a__     : Optional[int]  =PixaStructImageProcessor()
  a__     : List[Any]  =PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE	,	tokenizer=SCREAMING_SNAKE_CASE						)

  if use_large:
    a__     : Optional[int]  =4_096

  a__     : 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__":
     UpperCAmelCase     :     List[Any]							=	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     :     Optional[Any]							=	parser.parse_args()

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













from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,				split=lowerCAmelCase__		,				features=lowerCAmelCase__		,				cache_dir=lowerCAmelCase__		,				keep_in_memory=lowerCAmelCase__		,				streaming=lowerCAmelCase__		,				num_proc=lowerCAmelCase__		,				**lowerCAmelCase__		,				)
        a__     : Tuple  =path_or_paths if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							) else {self.split: path_or_paths}
        a__     : List[str]  =Text(
            cache_dir=lowerCAmelCase__		,				data_files=lowerCAmelCase__		,				features=lowerCAmelCase__		,				**lowerCAmelCase__		,				)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        if self.streaming:
          a__     : str  =self.builder.as_streaming_dataset(split=self.split							)
        # Build regular (map-style) dataset
        else:
          a__     : Dict  =None
          a__     : Optional[Any]  =None
          a__     : Union[str, Any]  =None
          a__     : Tuple  =None

          self.builder.download_and_prepare(
              download_config=lowerCAmelCase__		,				download_mode=lowerCAmelCase__		,				verification_mode=lowerCAmelCase__		,				base_path=lowerCAmelCase__		,				num_proc=self.num_proc		,				)
          a__     : Tuple  =self.builder.as_dataset(
              split=self.split		,				verification_mode=lowerCAmelCase__		,				in_memory=self.keep_in_memory							)
        return dataset






