





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						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						str        =							checkpoints.load_tax_checkpoint(UpperCAmelCase					)
   lowerCamelCase__       :						Dict        =							flatten_dict(UpperCAmelCase					)
   return flax_params



def 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							{}

   lowerCamelCase__       :						Tuple        =							{
       '''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''',
   }

   lowerCamelCase__       :						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
         lowerCamelCase__       :						str        =							'''.'''.join(key[1:]					)

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

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

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

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

         lowerCamelCase__       :						str        =							flax_dict[key]

   lowerCamelCase__       :						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):
         lowerCamelCase__       :						Tuple        =							torch.from_numpy(converted_dict[key].T					)
      else:
         lowerCamelCase__       :						Optional[Any]        =							torch.from_numpy(converted_dict[key]					)

   return converted_torch_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False      ,  UpperCAmelCase=False					)    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						List[str]        =							get_flax_param(UpperCAmelCase					)

   if not use_large:
      lowerCamelCase__       :						Tuple        =							PixaStructVisionConfig()
      lowerCamelCase__       :						List[str]        =							PixaStructTextConfig()
   else:
      lowerCamelCase__       :						Optional[int]        =							PixaStructVisionConfig(
          hidden_size=1536      ,  d_ff=3968      ,  num_attention_heads=24      ,  num_hidden_layers=18					)
      lowerCamelCase__       :						Optional[int]        =							PixaStructTextConfig(hidden_size=1536      ,  d_ff=3968      ,  num_heads=24      ,  num_layers=18					)
   lowerCamelCase__       :						Dict        =							PixaStructConfig(
       vision_config=encoder_config.to_dict()      ,  text_config=decoder_config.to_dict()      ,  is_vqa=UpperCAmelCase					)

   lowerCamelCase__       :						Optional[Any]        =							PixaStructForConditionalGeneration(UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							rename_and_convert_flax_params(UpperCAmelCase					)
   model.load_state_dict(UpperCAmelCase					)

   lowerCamelCase__       :						str        =							AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
   lowerCamelCase__       :						Union[str, Any]        =							PixaStructImageProcessor()
   lowerCamelCase__       :						Optional[int]        =							PixaStructProcessor(image_processor=UpperCAmelCase      ,  tokenizer=UpperCAmelCase					)

   if use_large:
      lowerCamelCase__       :						str        =							4096

   lowerCamelCase__       :						Dict        =							True

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

   model.save_pretrained(UpperCAmelCase					)
   processor.save_pretrained(UpperCAmelCase					)

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


if __name__ == "__main__":
       _A :   Optional[int]  			=  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.')
       _A :   Optional[int]  			=  parser.parse_args()

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










def 	_a						(   UpperCAmelCase					)    ->    bool:







   """simple docstring"""
   return str(UpperCAmelCase					) == str(UpperCAmelCase					)[::-1]



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   return int(UpperCAmelCase					) + int(str(UpperCAmelCase					)[::-1]					)



def 	_a						(   UpperCAmelCase = 10000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[]
   for num in range(1      ,  UpperCAmelCase					):
      lowerCamelCase__       :						List[str]        =							0
      lowerCamelCase__       :						Union[str, Any]        =							num
      while iterations < 50:
         lowerCamelCase__       :						Dict        =							sum_reverse(UpperCAmelCase					)
         iterations += 1
         if is_palindrome(UpperCAmelCase					):
            break
      else:
         lychrel_nums.append(UpperCAmelCase					)
   return len(UpperCAmelCase					)


if __name__ == "__main__":
       print(F'''{solution() = }''')



