

"""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__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							Any							=    checkpoints.load_tax_checkpoint(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    flatten_dict(lowerCAmelCase    )
      return flax_params




def 					a__						(						lowerCAmelCase    )	-> Optional[Any]:
      UpperCAmelCase__			:							List[str]							=    {}

      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__			:							Dict							=    {
          """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__			:							List[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__			:							Union[str, Any]							=    new_key.replace(lowerCAmelCase			,	lowerCAmelCase    )

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

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

                  UpperCAmelCase__			:							Optional[int]							=    flax_dict[key]

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

      return converted_torch_dict




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase=False			,	lowerCAmelCase=False    )	-> Optional[Any]:
      UpperCAmelCase__			:							Dict							=    get_flax_param(lowerCAmelCase    )

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

      UpperCAmelCase__			:							int							=    PixaStructForConditionalGeneration(lowerCAmelCase    )

      UpperCAmelCase__			:							List[str]							=    rename_and_convert_flax_params(lowerCAmelCase    )
      model.load_state_dict(lowerCAmelCase    )

      UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
      UpperCAmelCase__			:							Tuple							=    PixaStructImageProcessor()
      UpperCAmelCase__			:							Optional[Any]							=    PixaStructProcessor(image_processor=lowerCAmelCase			,	tokenizer=lowerCAmelCase    )

      if use_large:
            UpperCAmelCase__			:							Dict							=    40_96

      UpperCAmelCase__			:							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__":
    _A							=  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							=  parser.parse_args()

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




"""simple docstring"""
_A							=  """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def 					a__						(						)	-> None:
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter message: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter key [alphanumeric]: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Encrypt/Decrypt [e/d]: """    )

      if mode.lower().startswith("""e"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """encrypt"""
            UpperCAmelCase__			:							List[Any]							=    encrypt_message(lowerCAmelCase			,	lowerCAmelCase    )
      elif mode.lower().startswith("""d"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """decrypt"""
            UpperCAmelCase__			:							Dict							=    decrypt_message(lowerCAmelCase			,	lowerCAmelCase    )

      print(F"""\n{mode.title()}ed message:"""    )
      print(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""encrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""decrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							Optional[Any]							=    []
      UpperCAmelCase__			:							int							=    0
      UpperCAmelCase__			:							str							=    key.upper()

      for symbol in message:
            UpperCAmelCase__			:							Optional[Any]							=    LETTERS.find(symbol.upper()    )
            if num != -1:
                  if mode == "encrypt":
                        num += LETTERS.find(key[key_index]    )
                  elif mode == "decrypt":
                        num -= LETTERS.find(key[key_index]    )

                  num %= len(lowerCAmelCase    )

                  if symbol.isupper():
                        translated.append(LETTERS[num]    )
                  elif symbol.islower():
                        translated.append(LETTERS[num].lower()    )

                  key_index += 1
                  if key_index == len(lowerCAmelCase    ):
                        UpperCAmelCase__			:							List[str]							=    0
            else:
                  translated.append(lowerCAmelCase    )
      return "".join(lowerCAmelCase    )


if __name__ == "__main__":
    main()

