





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbart import MBartTokenizer
else:
  _snake_case           =			None


_snake_case           =			logging.get_logger(__name__)


_snake_case           =			{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_snake_case           =			{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

_snake_case           =			{
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
_snake_case           =			["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]





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

  __lowerCamelCase		=      []
  __lowerCamelCase		=      []







  def __init__(      self  :List[Any]   ,							_lowercase  :int=None   ,							_lowercase  :int=None   ,							_lowercase  :List[str]="<s>"   ,							_lowercase  :Union[str, Any]="</s>"   ,							_lowercase  :Optional[int]="</s>"   ,							_lowercase  :Union[str, Any]="<s>"   ,							_lowercase  :List[str]="<unk>"   ,							_lowercase  :List[str]="<pad>"   ,							_lowercase  :Union[str, Any]="<mask>"   ,							_lowercase  :str=None   ,							_lowercase  :List[str]=None   ,							_lowercase  :Optional[int]=None   ,							**_lowercase  :List[Any]   ,							):






     '''simple docstring'''





     lowercase__			       =  AddedToken(_a   ,							lstrip=_a   ,							rstrip=_a ) if isinstance(_a   ,							_a ) else mask_token

     super().__init__(
         vocab_file=_a   ,							tokenizer_file=_a   ,							bos_token=_a   ,							eos_token=_a   ,							sep_token=_a   ,							cls_token=_a   ,							unk_token=_a   ,							pad_token=_a   ,							mask_token=_a   ,							src_lang=_a   ,							tgt_lang=_a   ,							additional_special_tokens=_a   ,							**_a   ,							)

     lowercase__			       =  vocab_file
     lowercase__			       =  False if not self.vocab_file else True

     lowercase__			       =  FAIRSEQ_LANGUAGE_CODES.copy()

     if additional_special_tokens is not None:
        # Only add those special tokens if they are not already there.
        _additional_special_tokens.extend(
            [t for t in additional_special_tokens if t not in _additional_special_tokens] )

     self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
     lowercase__			       =  {
         lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     lowercase__			       =  src_lang if src_lang is not None else "en_XX"
     lowercase__			       =  self.convert_tokens_to_ids(self._src_lang )
     lowercase__			       =  tgt_lang
     self.set_src_lang_special_tokens(self._src_lang )







  @property
  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     return self._src_lang







  @src_lang.setter
  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :int ):






     '''simple docstring'''





     lowercase__			       =  new_src_lang
     self.set_src_lang_special_tokens(self._src_lang )







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :Union[str, Any] = None ):






     '''simple docstring'''





     if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







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






     '''simple docstring'''





     lowercase__			       =  [self.sep_token_id]
     lowercase__			       =  [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]







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






     '''simple docstring'''





     if src_lang is None or tgt_lang is None:
        raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
     lowercase__			       =  src_lang
     lowercase__			       =  self(_a   ,							add_special_tokens=_a   ,							return_tensors=_a   ,							**_a )
     lowercase__			       =  self.convert_tokens_to_ids(_a )
     lowercase__			       =  tgt_lang_id
     return inputs







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :Optional[int] = "en_XX"   ,							_lowercase  :Union[str, Any] = None   ,							_lowercase  :Tuple = "ro_RO"   ,							**_lowercase  :str   ,							):






     '''simple docstring'''





     lowercase__			       =  src_lang
     lowercase__			       =  tgt_lang
     return super().prepare_seqaseq_batch(_a   ,							_a   ,							**_a )







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     return self.set_src_lang_special_tokens(self.src_lang )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     return self.set_tgt_lang_special_tokens(self.tgt_lang )







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






     '''simple docstring'''





     lowercase__			       =  self.convert_tokens_to_ids(_a )
     lowercase__			       =  []
     lowercase__			       =  [self.eos_token_id, self.cur_lang_code]

     lowercase__			       =  self.convert_ids_to_tokens(self.prefix_tokens )
     lowercase__			       =  self.convert_ids_to_tokens(self.suffix_tokens )

     lowercase__			       =  processors.TemplateProcessing(
         single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,							pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,							self.prefix_tokens + self.suffix_tokens ) )   ,							)







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






     '''simple docstring'''





     lowercase__			       =  self.convert_tokens_to_ids(_a )
     lowercase__			       =  []
     lowercase__			       =  [self.eos_token_id, self.cur_lang_code]

     lowercase__			       =  self.convert_ids_to_tokens(self.prefix_tokens )
     lowercase__			       =  self.convert_ids_to_tokens(self.suffix_tokens )

     lowercase__			       =  processors.TemplateProcessing(
         single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,							pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,							self.prefix_tokens + self.suffix_tokens ) )   ,							)







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :List[str] = None ):






     '''simple docstring'''





     if not self.can_save_slow_tokenizer:
        raise ValueError(
            "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
            "tokenizer." )

     if not os.path.isdir(_a ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
        return
     lowercase__			       =  os.path.join(
         _a   ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
        copyfile(self.vocab_file   ,							_a )

     return (out_vocab_file,)













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
  )






