
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[Any]:
   _A:  Any					=       checkpoints.load_tax_checkpoint(a      )
   _A:  Optional[int]					=       flatten_dict(a      )
   return flax_params






def   lowerCamelCase__							(					a      )      ->   Union[str, Any]:
   _A:  Optional[int]					=       {}

   _A:  str					=       {
       '''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:  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
         _A:  List[str]					=       '''.'''.join(key[1:]      )

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            _A:  str					=       new_key.replace(a		,		a      )

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               _A:  Any					=       new_key.replace(a		,		a      )

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

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

         _A:  int					=       flax_dict[key]

   _A:  List[Any]					=       {}
   # 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:  Union[str, Any]					=       torch.from_numpy(converted_dict[key]      )

   return converted_torch_dict






def   lowerCamelCase__							(					a		,		a		,		a=False		,		a=False      )      ->   Optional[Any]:
   _A:  Optional[Any]					=       get_flax_param(a      )

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

   _A:  List[str]					=       PixaStructForConditionalGeneration(a      )

   _A:  Dict					=       rename_and_convert_flax_params(a      )
   model.load_state_dict(a      )

   _A:  Optional[Any]					=       AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''      )
   _A:  List[Any]					=       PixaStructImageProcessor()
   _A:  Optional[Any]					=       PixaStructProcessor(image_processor=a		,		tokenizer=a      )

   if use_large:
      _A:  str					=       40_96

   _A:  List[Any]					=       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__":
   UpperCAmelCase__		:     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.')
   UpperCAmelCase__		:     Any     			=      parser.parse_args()

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





def   lowerCamelCase__							(					a      )      ->   bool:
   _A:  Dict					=       [int(a      ) for i in ip_va_address.split('''.'''      ) if i.isdigit()]
   return len(a      ) == 4 and all(0 <= int(a      ) <= 2_54 for octet in octets      )


if __name__ == "__main__":
   UpperCAmelCase__		:     str     			=      input().strip()
   UpperCAmelCase__		:     Any     			=      'valid' if is_ip_va_address_valid(ip) else 'invalid'
   print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")



