


'''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__      (						__lowerCamelCase	:							List[str]							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =checkpoints.load_tax_checkpoint(__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =flatten_dict(__lowerCamelCase							)
   return flax_params





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   ={}

   _UpperCAmelCase       :       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',
   }

   _UpperCAmelCase       :       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
         _UpperCAmelCase       :       Union[str, Any]							   ='.'.join(key[1:]							)

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            _UpperCAmelCase       :       Tuple							   =new_key.replace(__lowerCamelCase	,   __lowerCamelCase							)

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

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

         elif "layers" in new_key and "decoder" in new_key:
            # use regex to replace the layer number
            _UpperCAmelCase       :       Optional[Any]							   =re.sub(R'layers_(\d+)'	,   R'layer.\1'	,   __lowerCamelCase							)

         _UpperCAmelCase       :       Dict							   =flax_dict[key]

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

   return converted_torch_dict





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							Any=False	,   __lowerCamelCase	:							Union[str, Any]=False							):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =get_flax_param(__lowerCamelCase							)

   if not use_large:
      _UpperCAmelCase       :       Any							   =PixaStructVisionConfig()
      _UpperCAmelCase       :       str							   =PixaStructTextConfig()
   else:
      _UpperCAmelCase       :       List[Any]							   =PixaStructVisionConfig(
          hidden_size=1_5_3_6	,   d_ff=3_9_6_8	,   num_attention_heads=2_4	,   num_hidden_layers=1_8							)
      _UpperCAmelCase       :       Optional[int]							   =PixaStructTextConfig(hidden_size=1_5_3_6	,   d_ff=3_9_6_8	,   num_heads=2_4	,   num_layers=1_8							)
   _UpperCAmelCase       :       str							   =PixaStructConfig(
       vision_config=encoder_config.to_dict()	,   text_config=decoder_config.to_dict()	,   is_vqa=__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =PixaStructForConditionalGeneration(__lowerCamelCase							)

   _UpperCAmelCase       :       Optional[Any]							   =rename_and_convert_flax_params(__lowerCamelCase							)
   model.load_state_dict(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'							)
   _UpperCAmelCase       :       Optional[int]							   =PixaStructImageProcessor()
   _UpperCAmelCase       :       Tuple							   =PixaStructProcessor(image_processor=__lowerCamelCase	,   tokenizer=__lowerCamelCase							)

   if use_large:
      _UpperCAmelCase       :       int							   =4_0_9_6

   _UpperCAmelCase       :       Optional[Any]							   =True

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

   model.save_pretrained(__lowerCamelCase							)
   processor.save_pretrained(__lowerCamelCase							)

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


if __name__ == "__main__":
 lowercase							=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.')
 lowercase							=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 __future__ import annotations





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]							):







   '''simple docstring'''




   if not nums:
      return 0
   _UpperCAmelCase       :       Tuple							   =nums[0]
   _UpperCAmelCase       :       int							   =0
   for num in nums[1:]:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =(
          max_excluding + num,
          max(__lowerCamelCase	,   __lowerCamelCase							),
      )
   return max(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

