





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


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]				)->		int:
  _snake_case   :				Union[str, Any]														=	{}

  _snake_case   :				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',
  }

  _snake_case   :				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
      _snake_case   :				str														=	'.'.join(key[1:]				)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _snake_case   :				Tuple														=	new_key.replace(lowerCAmelCase    ,							lowerCAmelCase				)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _snake_case   :				List[Any]														=	new_key.replace(lowerCAmelCase    ,							lowerCAmelCase				)

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

      _snake_case   :				str														=	flax_dict[key]

  _snake_case   :				Tuple														=	{}
  # 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   :				str														=	torch.from_numpy(converted_dict[key].T				)
    else:
      _snake_case   :				str														=	torch.from_numpy(converted_dict[key]				)

  return converted_torch_dict


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Union[str, Any]=False    ,							lowerCAmelCase:	str=False				)->		Any:
  _snake_case   :				Tuple														=	get_flax_param(lowerCAmelCase				)

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

  _snake_case   :				int														=	PixaStructForConditionalGeneration(lowerCAmelCase				)

  _snake_case   :				str														=	rename_and_convert_flax_params(lowerCAmelCase				)
  model.load_state_dict(lowerCAmelCase				)

  _snake_case   :				Optional[Any]														=	AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'				)
  _snake_case   :				str														=	PixaStructImageProcessor()
  _snake_case   :				Optional[Any]														=	PixaStructProcessor(image_processor=lowerCAmelCase    ,							tokenizer=lowerCAmelCase				)

  if use_large:
    _snake_case   :				Tuple														=	40_96

  _snake_case   :				Optional[int]														=	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__":
      lowerCAmelCase_						   =    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.""")
      lowerCAmelCase_						   =    parser.parse_args()

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











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowerCAmelCase_						   =    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase_						   =    {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase_						   =    {
    """unc-nlp/lxmert-base-uncased""": 512,
}

lowerCAmelCase_						   =    {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       List[Any]      					=VOCAB_FILES_NAMES
  a_							:       Tuple      					=PRETRAINED_VOCAB_FILES_MAP
  a_							:       Optional[Any]      					=PRETRAINED_INIT_CONFIGURATION
  a_							:       Any      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_							:       Any      					=LxmertTokenizer







  def __init__(      self      :					Any      ,				UpperCamelCase      :					Union[str, Any]=None      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					List[str]=True      ,				UpperCamelCase      :					List[str]="[UNK]"      ,				UpperCamelCase      :					List[Any]="[SEP]"      ,				UpperCamelCase      :					List[Any]="[PAD]"      ,				UpperCamelCase      :					Optional[Any]="[CLS]"      ,				UpperCamelCase      :					Optional[int]="[MASK]"      ,				UpperCamelCase      :					Optional[int]=True      ,				UpperCamelCase      :					str=None      ,				**UpperCamelCase      :					List[str]      ,				):


    '''simple docstring'''





    super().__init__(
        UpperCamelCase      ,				tokenizer_file=UpperCamelCase      ,				do_lower_case=UpperCamelCase      ,				unk_token=UpperCamelCase      ,				sep_token=UpperCamelCase      ,				pad_token=UpperCamelCase      ,				cls_token=UpperCamelCase      ,				mask_token=UpperCamelCase      ,				tokenize_chinese_chars=UpperCamelCase      ,				strip_accents=UpperCamelCase      ,				**UpperCamelCase      ,				)

    _snake_case   :				Tuple														=	json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
    if (
        normalizer_state.get('lowercase'      ,				UpperCamelCase							) != do_lower_case
        or normalizer_state.get('strip_accents'      ,				UpperCamelCase							) != strip_accents
        or normalizer_state.get('handle_chinese_chars'      ,				UpperCamelCase							) != tokenize_chinese_chars
    ):
      _snake_case   :				List[Any]														=	getattr(UpperCamelCase      ,				normalizer_state.pop('type'							)							)
      _snake_case   :				Optional[int]														=	do_lower_case
      _snake_case   :				Dict														=	strip_accents
      _snake_case   :				Optional[int]														=	tokenize_chinese_chars
      _snake_case   :				Optional[Any]														=	normalizer_class(**UpperCamelCase							)

    _snake_case   :				int														=	do_lower_case







  def UpperCamelCase_ (      self      :					int      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					str=None							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

    return output







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					List[int]      ,				UpperCamelCase      :					Optional[List[int]] = None							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	[self.sep_token_id]
    _snake_case   :				List[str]														=	[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							) * [0] + len(token_ids_a + sep							) * [1]







  def UpperCamelCase_ (      self      :					int      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Optional[str] = None							):


    '''simple docstring'''





    _snake_case   :				int														=	self._tokenizer.model.save(UpperCamelCase      ,				name=UpperCamelCase							)
    return tuple(UpperCamelCase							)




