





import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


_snake_case           =			logging.get_logger(__name__)

_snake_case           =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_snake_case           =			{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

_snake_case           =			{
    """Salesforce/codegen-350M-mono""": 2048,
}





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      ['input_ids', 'attention_mask']
  __lowerCamelCase		=      CodeGenTokenizer







  def __init__(      self  :str   ,							_lowercase  :Optional[int]=None   ,							_lowercase  :Tuple=None   ,							_lowercase  :Union[str, Any]=None   ,							_lowercase  :str="<|endoftext|>"   ,							_lowercase  :Tuple="<|endoftext|>"   ,							_lowercase  :List[Any]="<|endoftext|>"   ,							_lowercase  :Optional[int]=False   ,							**_lowercase  :int   ,							):






     '''simple docstring'''





     super().__init__(
         _lowercase   ,							_lowercase   ,							tokenizer_file=_lowercase   ,							unk_token=_lowercase   ,							bos_token=_lowercase   ,							eos_token=_lowercase   ,							add_prefix_space=_lowercase   ,							**_lowercase   ,							)

     if kwargs.pop("add_bos_token"   ,							_lowercase ):
        lowercase__			       =  kwargs.pop("name_or_path"   ,							"" )
        raise ValueError(
            "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
            "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
            f'''`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'''
            f'''`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'''
            "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
            " so that the fast tokenizer works correctly." )

     lowercase__			       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
     if pre_tok_state.get("add_prefix_space"   ,							_lowercase ) != add_prefix_space:
        lowercase__			       =  getattr(_lowercase   ,							pre_tok_state.pop("type" ) )
        lowercase__			       =  add_prefix_space
        lowercase__			       =  pre_tok_class(**_lowercase )

     lowercase__			       =  add_prefix_space







  def   UpperCAmelCase					(      self  :List[Any]   ,							*_lowercase  :Dict   ,							**_lowercase  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  kwargs.get("is_split_into_words"   ,							_lowercase )
     assert self.add_prefix_space or not is_split_into_words, (
         f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*_lowercase   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							*_lowercase  :str   ,							**_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  kwargs.get("is_split_into_words"   ,							_lowercase )

     assert self.add_prefix_space or not is_split_into_words, (
         f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*_lowercase   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :str   ,							_lowercase  :Optional[str] = None ):






     '''simple docstring'''





     lowercase__			       =  self._tokenizer.model.save(_lowercase   ,							name=_lowercase )
     return tuple(_lowercase )







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]   ,							_lowercase  :bool = False   ,							_lowercase  :bool = None   ,							_lowercase  :Optional[List[str]] = None   ,							**_lowercase  :Union[str, Any]   ,							):






     '''simple docstring'''





     lowercase__			       =  super().decode(
         token_ids=_lowercase   ,							skip_special_tokens=_lowercase   ,							clean_up_tokenization_spaces=_lowercase   ,							**_lowercase   ,							)

     if truncate_before_pattern is not None and len(_lowercase ) > 0:
        lowercase__			       =  self.truncate(_lowercase   ,							_lowercase )

     return decoded_text







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Any   ,							_lowercase  :str ):






     '''simple docstring'''





     def find_re(_lowercase  :List[str]   ,							_lowercase  :List[Any]   ,							_lowercase  :List[str] ):
        lowercase__			       =  pattern.search(_lowercase   ,							_lowercase )
        return m.start() if m else -1

     lowercase__			       =  [re.compile(_lowercase   ,							re.MULTILINE ) for pattern in truncate_before_pattern]

     lowercase__			       =  list(re.finditer("^print"   ,							_lowercase   ,							re.MULTILINE ) )

     if len(_lowercase ) > 1:
        lowercase__			       =  completion[: prints[1].start()]

     lowercase__			       =  list(re.finditer("^def"   ,							_lowercase   ,							re.MULTILINE ) )

     if len(_lowercase ) > 1:
        lowercase__			       =  completion[: defs[1].start()]

     lowercase__			       =  0

     lowercase__			       =  [
         pos for pos in [find_re(_lowercase   ,							_lowercase   ,							_lowercase ) for terminal in terminals] if pos != -1
     ]

     if len(_lowercase ) > 0:
        return completion[: min(_lowercase )]
     else:
        return completion













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       (   __magic_name__	):
   lowercase__			       =  checkpoints.load_tax_checkpoint(__magic_name__	)
   lowercase__			       =  flatten_dict(__magic_name__	)
   return flax_params


def _A       (   __magic_name__	):
   lowercase__			       =  {}

   lowercase__			       =  {
       "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",
   }

   lowercase__			       =  {
       "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
         lowercase__			       =  ".".join(key[1:]	)

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

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

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

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

         lowercase__			       =  flax_dict[key]

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

   return converted_torch_dict


def _A       (   __magic_name__  , __magic_name__  , __magic_name__=False  , __magic_name__=False	):
   lowercase__			       =  get_flax_param(__magic_name__	)

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

   lowercase__			       =  PixaStructForConditionalGeneration(__magic_name__	)

   lowercase__			       =  rename_and_convert_flax_params(__magic_name__	)
   model.load_state_dict(__magic_name__	)

   lowercase__			       =  AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"	)
   lowercase__			       =  PixaStructImageProcessor()
   lowercase__			       =  PixaStructProcessor(image_processor=__magic_name__  , tokenizer=__magic_name__	)

   if use_large:
      lowercase__			       =  4096

   lowercase__			       =  True

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

   model.save_pretrained(__magic_name__	)
   processor.save_pretrained(__magic_name__	)

   print("Model saved in {}".format(__magic_name__	)	)


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
  )






