





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__    (							snake_case_	:  Optional[Any]       )      ->				List[Any]:
   __snake_case							       =   checkpoints.load_tax_checkpoint(snake_case_       )
   __snake_case							       =   flatten_dict(snake_case_       )
   return flax_params






def lowerCamelCase__    (							snake_case_	:  Dict       )      ->				Optional[int]:
   __snake_case							       =   {}

   __snake_case							       =   {
       '''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''',
   }

   __snake_case							       =   {
       '''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
         __snake_case							       =   '''.'''.join(key[1:]       )

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            __snake_case							       =   new_key.replace(snake_case_       ,      snake_case_       )

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

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            __snake_case							       =   re.sub(R'''layers_(\d+)'''       ,      R'''layer.\1'''       ,      snake_case_       )
            __snake_case							       =   new_key.replace('''encoder'''       ,      '''encoder.encoder'''       )

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

         __snake_case							       =   flax_dict[key]

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

   return converted_torch_dict






def lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  Tuple       ,      snake_case_	:  Union[str, Any]=False       ,      snake_case_	:  Optional[int]=False       )      ->				Union[str, Any]:
   __snake_case							       =   get_flax_param(snake_case_       )

   if not use_large:
      __snake_case							       =   PixaStructVisionConfig()
      __snake_case							       =   PixaStructTextConfig()
   else:
      __snake_case							       =   PixaStructVisionConfig(
          hidden_size=1536       ,      d_ff=3968       ,      num_attention_heads=24       ,      num_hidden_layers=18       )
      __snake_case							       =   PixaStructTextConfig(hidden_size=1536       ,      d_ff=3968       ,      num_heads=24       ,      num_layers=18       )
   __snake_case							       =   PixaStructConfig(
       vision_config=encoder_config.to_dict()       ,      text_config=decoder_config.to_dict()       ,      is_vqa=snake_case_       )

   __snake_case							       =   PixaStructForConditionalGeneration(snake_case_       )

   __snake_case							       =   rename_and_convert_flax_params(snake_case_       )
   model.load_state_dict(snake_case_       )

   __snake_case							       =   AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''       )
   __snake_case							       =   PixaStructImageProcessor()
   __snake_case							       =   PixaStructProcessor(image_processor=snake_case_       ,      tokenizer=snake_case_       )

   if use_large:
      __snake_case							       =   4096

   __snake_case							       =   True

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

   model.save_pretrained(snake_case_       )
   processor.save_pretrained(snake_case_       )

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


if __name__ == "__main__":
    snake_case_				=			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.')
    snake_case_				=			parser.parse_args()

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








import socket






def lowerCamelCase__    (							)      ->				Any:
   __snake_case							       =   socket.socket(socket.AF_INET       ,      socket.SOCK_STREAM       )
   __snake_case							       =   socket.gethostname()
   __snake_case							       =   1_2312

   sock.connect((host, port)       )
   sock.send(B'''Hello server!'''       )

   with open('''Received_file'''       ,      '''wb'''       ) as out_file:
      print('''File opened'''       )
      print('''Receiving data...'''       )
      while True:
         __snake_case							       =   sock.recv(1024       )
         if not data:
            break
         out_file.write(snake_case_       )

   print('''Successfully received the file'''       )
   sock.close()
   print('''Connection closed'''       )


if __name__ == "__main__":
    main()

