





"""simple docstring"""





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      int						,				__lowercase       :      NestedDataStructureLike[PathLike]						,				__lowercase       :      Optional[NamedSplit] = None						,				__lowercase       :      Optional[Features] = None						,				__lowercase       :      str = None						,				__lowercase       :      bool = False						,				__lowercase       :      bool = False						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[int] = None						,				**__lowercase       :      int						,				):


        '''simple docstring'''




        super().__init__(
            __lowercase						,				split=__lowercase						,				features=__lowercase						,				cache_dir=__lowercase						,				keep_in_memory=__lowercase						,				streaming=__lowercase						,				num_proc=__lowercase						,				**__lowercase						,				)
        __UpperCAmelCase    :						Tuple       =						field
        __UpperCAmelCase    :						int       =						path_or_paths if isinstance(__lowercase						,				__lowercase ) else {self.split: path_or_paths}
        __UpperCAmelCase    :						List[Any]       =						Json(
            cache_dir=__lowercase						,				data_files=__lowercase						,				features=__lowercase						,				field=__lowercase						,				**__lowercase						,				)







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        if self.streaming:
          __UpperCAmelCase    :						Dict       =						self.builder.as_streaming_dataset(split=self.split )
        # Build regular (map-style) dataset
        else:
          __UpperCAmelCase    :						int       =						None
          __UpperCAmelCase    :						str       =						None
          __UpperCAmelCase    :						Union[str, Any]       =						None
          __UpperCAmelCase    :						List[Any]       =						None

          self.builder.download_and_prepare(
              download_config=__lowercase						,				download_mode=__lowercase						,				verification_mode=__lowercase						,				base_path=__lowercase						,				num_proc=self.num_proc						,				)
          __UpperCAmelCase    :						Optional[int]       =						self.builder.as_dataset(
              split=self.split						,				verification_mode=__lowercase						,				in_memory=self.keep_in_memory )
        return dataset




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      List[str]						,				__lowercase       :      Dataset						,				__lowercase       :      Union[PathLike, BinaryIO]						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				**__lowercase       :      int						,				):


        '''simple docstring'''




        if num_proc is not None and num_proc <= 0:
          raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )

        __UpperCAmelCase    :						int       =						dataset
        __UpperCAmelCase    :						Tuple       =						path_or_buf
        __UpperCAmelCase    :						int       =						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        __UpperCAmelCase    :						Optional[Any]       =						num_proc
        __UpperCAmelCase    :						Any       =						'''utf-8'''
        __UpperCAmelCase    :						Optional[int]       =						to_json_kwargs

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.to_json_kwargs.pop('''path_or_buf'''						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.to_json_kwargs.pop('''orient'''						,				'''records''' )
        __UpperCAmelCase    :						Optional[Any]       =						self.to_json_kwargs.pop('''lines'''						,				True if orient == '''records''' else False )
        __UpperCAmelCase    :						Dict       =						self.to_json_kwargs.pop('''index'''						,				False if orient in ['''split''', '''table'''] else True )
        __UpperCAmelCase    :						Tuple       =						self.to_json_kwargs.pop('''compression'''						,				__lowercase )

        if compression not in [None, "infer", "gzip", "bz2", "xz"]:
          raise NotImplementedError(f'''`datasets` currently does not support {compression} compression''' )

        if isinstance(self.path_or_buf						,				(str, bytes, os.PathLike) ):
          with fsspec.open(self.path_or_buf						,				'''wb'''						,				compression=__lowercase ) as buffer:
            __UpperCAmelCase    :						Any       =						self._write(file_obj=__lowercase						,				orient=__lowercase						,				lines=__lowercase						,				index=__lowercase						,				**self.to_json_kwargs )
        else:
          if compression:
            raise NotImplementedError(
                f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
                ''' was passed. Please provide a local path instead.''' )
          __UpperCAmelCase    :						Optional[Any]       =						self._write(
              file_obj=self.path_or_buf						,				orient=__lowercase						,				lines=__lowercase						,				index=__lowercase						,				**self.to_json_kwargs )
        return written

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      int ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[Any]       =						args

        __UpperCAmelCase    :						int       =						query_table(
            table=self.dataset.data						,				key=slice(__lowercase						,				offset + self.batch_size )						,				indices=self.dataset._indices						,				)
        __UpperCAmelCase    :						Dict       =						batch.to_pandas().to_json(
            path_or_buf=__lowercase						,				orient=__lowercase						,				lines=__lowercase						,				index=__lowercase						,				**__lowercase )
        if not json_str.endswith('''\n''' ):
          json_str += "\n"
        return json_str.encode(self.encoding )







      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      BinaryIO						,				__lowercase       :      int						,				__lowercase       :      int						,				__lowercase       :      List[str]						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						0

        if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0						,				len(self.dataset )						,				self.batch_size )						,				unit='''ba'''						,				disable=not logging.is_progress_bar_enabled()						,				desc='''Creating json from Arrow format'''						,				):
            __UpperCAmelCase    :						str       =						self._batch_json((offset, orient, lines, index, to_json_kwargs) )
            written += file_obj.write(__lowercase )
        else:
          __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						len(self.dataset ), self.batch_size
          with multiprocessing.Pool(self.num_proc ) as pool:
            for json_str in logging.tqdm(
                pool.imap(
                    self._batch_json						,				[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,				__lowercase						,				__lowercase )]						,				)						,				total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,				unit='''ba'''						,				disable=not logging.is_progress_bar_enabled()						,				desc='''Creating json from Arrow format'''						,				):
              written += file_obj.write(__lowercase )

        return written






"""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       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						checkpoints.load_tax_checkpoint(UpperCAmelCase_  )
  __UpperCAmelCase    :						Any       =						flatten_dict(UpperCAmelCase_  )
  return flax_params


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						{}

  __UpperCAmelCase    :						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    :						Optional[int]       =						{
      '''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    :						Optional[Any]       =						'''.'''.join(key[1:]  )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __UpperCAmelCase    :						List[str]       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __UpperCAmelCase    :						Dict       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

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

      __UpperCAmelCase    :						Dict       =						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    :						List[str]       =						torch.from_numpy(converted_dict[key].T  )
    else:
      __UpperCAmelCase    :						Optional[int]       =						torch.from_numpy(converted_dict[key]  )

  return converted_torch_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						get_flax_param(UpperCAmelCase_  )

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

  __UpperCAmelCase    :						Optional[Any]       =						PixaStructForConditionalGeneration(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						rename_and_convert_flax_params(UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''  )
  __UpperCAmelCase    :						Union[str, Any]       =						PixaStructImageProcessor()
  __UpperCAmelCase    :						Optional[int]       =						PixaStructProcessor(image_processor=UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  )

  if use_large:
    __UpperCAmelCase    :						str       =						40_96

  __UpperCAmelCase    :						str       =						True

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

  model.save_pretrained(UpperCAmelCase_  )
  processor.save_pretrained(UpperCAmelCase_  )

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


if __name__ == "__main__":
  lowercase__				:List[str]				   =       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.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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