"""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   (		A      ):






     '''simple docstring'''



     lowercase__				 =							checkpoints.load_tax_checkpoint(A      )
     lowercase__				 =							flatten_dict(A      )
     return flax_params







def 					__a   (		A      ):






     '''simple docstring'''



     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(A    ,				A      )

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

               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"    ,				A      )
                    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"    ,				A      )

               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   (		A    ,				A    ,				A=False    ,				A=False      ):






     '''simple docstring'''



     lowercase__				 =							get_flax_param(A      )

     if not use_large:
          lowercase__				 =							PixaStructVisionConfig()
          lowercase__				 =							PixaStructTextConfig()
     else:
          lowercase__				 =							PixaStructVisionConfig(
              hidden_size=15_36    ,				d_ff=39_68    ,				num_attention_heads=24    ,				num_hidden_layers=18      )
          lowercase__				 =							PixaStructTextConfig(hidden_size=15_36    ,				d_ff=39_68    ,				num_heads=24    ,				num_layers=18      )
     lowercase__				 =							PixaStructConfig(
         vision_config=encoder_config.to_dict()    ,				text_config=decoder_config.to_dict()    ,				is_vqa=A      )

     lowercase__				 =							PixaStructForConditionalGeneration(A      )

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

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

     if use_large:
          lowercase__				 =							40_96

     lowercase__				 =							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__":
 lowerCAmelCase_:					List[str]     =					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_:					int     =					parser.parse_args()

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





"""simple docstring"""


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    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            _UpperCAmelCase,				split=_UpperCAmelCase,				features=_UpperCAmelCase,				cache_dir=_UpperCAmelCase,				keep_in_memory=_UpperCAmelCase,				streaming=_UpperCAmelCase,				num_proc=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							path_or_paths if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) else {self.split: path_or_paths}
        lowercase__				 =							Text(
            cache_dir=_UpperCAmelCase,				data_files=_UpperCAmelCase,				features=_UpperCAmelCase,				**_UpperCAmelCase,				)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if self.streaming:
             lowercase__				 =							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None

             self.builder.download_and_prepare(
                 download_config=_UpperCAmelCase,				download_mode=_UpperCAmelCase,				verification_mode=_UpperCAmelCase,				base_path=_UpperCAmelCase,				num_proc=self.num_proc,				)
             lowercase__				 =							self.builder.as_dataset(
                 split=self.split,				verification_mode=_UpperCAmelCase,				in_memory=self.keep_in_memory  )
        return dataset




