





"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase__				:int				   =       logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')

lowercase__				:List[Any]				   =       list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowercase__				:List[Any]				   =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 							snake_case							:

      '''simple docstring'''



      _A     :       Optional[str]         =       field(
          default='cifar10'      ,  metadata={'help': 'Name of a dataset from the datasets package'} )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}      ,  )
      _A     :       Optional[str]         =       field(default=__UpperCAmelCase      ,  metadata={'help': 'A folder containing the training data.'} )
      _A     :       Optional[str]         =       field(default=__UpperCAmelCase      ,  metadata={'help': 'A folder containing the validation data.'} )
      _A     :       Optional[float]         =       field(
          default=0.15      ,  metadata={'help': 'Percent to split off of train for validation.'} )
      _A     :       int         =       field(default=32      ,  metadata={'help': 'The size of the square patches to use for masking.'} )
      _A     :       float         =       field(
          default=0.6      ,  metadata={'help': 'Percentage of patches to mask.'}      ,  )
      _A     :       Optional[int]         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of training examples to this '
                  'value if set.'
              )
          }      ,  )
      _A     :       Optional[int]         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                  'value if set.'
              )
          }      ,  )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						{}
        if self.train_dir is not None:
          __UpperCAmelCase    :						Optional[int]       =						self.train_dir
        if self.validation_dir is not None:
          __UpperCAmelCase    :						Tuple       =						self.validation_dir
        __UpperCAmelCase    :						List[Any]       =						data_files if data_files else None




@dataclass
class 							snake_case							:

      '''simple docstring'''



      _A     :       str         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
                  'checkpoint identifier on the hub. '
                  'Don\'t set if you want to train a model from scratch.'
              )
          }      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__UpperCAmelCase )}      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'Override some existing default config settings when a model is trained from scratch. Example: '
                  'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
              )
          }      ,  )
      _A     :       Optional[str]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}      ,  )
      _A     :       str         =       field(
          default='main'      ,  metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}      ,  )
      _A     :       str         =       field(default=__UpperCAmelCase      ,  metadata={'help': 'Name or path of preprocessor config.'} )
      _A     :       bool         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }      ,  )
      _A     :       Optional[int]         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
              )
          }      ,  )
      _A     :       Optional[int]         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': (
                  'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
              )
          }      ,  )
      _A     :       Optional[int]         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Stride to use for the encoder.'}      ,  )




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      Dict						,				__lowercase       :      str=192						,				__lowercase       :      Optional[Any]=32						,				__lowercase       :      Union[str, Any]=4						,				__lowercase       :      Optional[int]=0.6 ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						input_size
        __UpperCAmelCase    :						Tuple       =						mask_patch_size
        __UpperCAmelCase    :						Any       =						model_patch_size
        __UpperCAmelCase    :						Any       =						mask_ratio

        if self.input_size % self.mask_patch_size != 0:
          raise ValueError('''Input size must be divisible by mask patch size''' )
        if self.mask_patch_size % self.model_patch_size != 0:
          raise ValueError('''Mask patch size must be divisible by model patch size''' )

        __UpperCAmelCase    :						int       =						self.input_size // self.mask_patch_size
        __UpperCAmelCase    :						Any       =						self.mask_patch_size // self.model_patch_size

        __UpperCAmelCase    :						Optional[Any]       =						self.rand_size**2
        __UpperCAmelCase    :						List[Any]       =						int(np.ceil(self.token_count * self.mask_ratio ) )







      def __call__(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						np.random.permutation(self.token_count )[: self.mask_count]
        __UpperCAmelCase    :						Dict       =						np.zeros(self.token_count						,				dtype=__lowercase )
        __UpperCAmelCase    :						List[Any]       =						1

        __UpperCAmelCase    :						Tuple       =						mask.reshape((self.rand_size, self.rand_size) )
        __UpperCAmelCase    :						Any       =						mask.repeat(self.scale						,				axis=0 ).repeat(self.scale						,				axis=1 )

        return torch.tensor(mask.flatten() )


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						torch.stack([example['''pixel_values'''] for example in examples]  )
  __UpperCAmelCase    :						int       =						torch.stack([example['''mask'''] for example in examples]  )
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def       lowerCamelCase_       (   )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						Any       =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
  if len(sys.argv  ) == 2 and sys.argv[1].endswith('''.json'''  ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Tuple       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
  else:
    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry('''run_mim'''  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,			datefmt='''%m/%d/%Y %H:%M:%S'''  ,			handlers=[logging.StreamHandler(sys.stdout  )]  ,			)

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  __UpperCAmelCase    :						List[Any]       =						training_args.get_process_log_level()
  logger.setLevel(UpperCAmelCase_  )
  transformers.utils.logging.set_verbosity(UpperCAmelCase_  )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
      + f'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
  logger.info(f'''Training/evaluation parameters {training_args}'''  )

  # Detecting last checkpoint.
  __UpperCAmelCase    :						Tuple       =						None
  if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
    __UpperCAmelCase    :						List[str]       =						get_last_checkpoint(training_args.output_dir  )
    if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
      raise ValueError(
          f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
          '''Use --overwrite_output_dir to overcome.'''  )
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
          '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''  )

    # Initialize our dataset.
  __UpperCAmelCase    :						List[str]       =						load_dataset(
      data_args.dataset_name  ,			data_args.dataset_config_name  ,			data_files=data_args.data_files  ,			cache_dir=model_args.cache_dir  ,			use_auth_token=True if model_args.use_auth_token else None  ,			)

  # If we don't have a validation split, split off a percentage of train as validation.
  __UpperCAmelCase    :						Union[str, Any]       =						None if '''validation''' in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split  ,			UpperCAmelCase_  ) and data_args.train_val_split > 0.0:
    __UpperCAmelCase    :						Union[str, Any]       =						ds['''train'''].train_test_split(data_args.train_val_split  )
    __UpperCAmelCase    :						Optional[Any]       =						split['''train''']
    __UpperCAmelCase    :						str       =						split['''test''']

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  __UpperCAmelCase    :						Dict       =						{
      '''cache_dir''': model_args.cache_dir,
      '''revision''': model_args.model_revision,
      '''use_auth_token''': True if model_args.use_auth_token else None,
  }
  if model_args.config_name_or_path:
    __UpperCAmelCase    :						List[Any]       =						AutoConfig.from_pretrained(model_args.config_name_or_path  ,			**UpperCAmelCase_  )
  elif model_args.model_name_or_path:
    __UpperCAmelCase    :						Optional[Any]       =						AutoConfig.from_pretrained(model_args.model_name_or_path  ,			**UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						Optional[int]       =						CONFIG_MAPPING[model_args.model_type]()
    logger.warning('''You are instantiating a new config instance from scratch.'''  )
    if model_args.config_overrides is not None:
      logger.info(f'''Overriding config: {model_args.config_overrides}'''  )
      config.update_from_string(model_args.config_overrides  )
      logger.info(f'''New config: {config}'''  )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
  if hasattr(UpperCAmelCase_  ,			'''decoder_type'''  ):
    __UpperCAmelCase    :						str       =						'''simmim'''

  # adapt config
  __UpperCAmelCase    :						Union[str, Any]       =						model_args.image_size if model_args.image_size is not None else config.image_size
  __UpperCAmelCase    :						List[Any]       =						model_args.patch_size if model_args.patch_size is not None else config.patch_size
  __UpperCAmelCase    :						Union[str, Any]       =						(
      model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
  )

  config.update(
      {
          '''image_size''': model_args.image_size,
          '''patch_size''': model_args.patch_size,
          '''encoder_stride''': model_args.encoder_stride,
      }  )

  # create image processor
  if model_args.image_processor_name:
    __UpperCAmelCase    :						List[str]       =						AutoImageProcessor.from_pretrained(model_args.image_processor_name  ,			**UpperCAmelCase_  )
  elif model_args.model_name_or_path:
    __UpperCAmelCase    :						int       =						AutoImageProcessor.from_pretrained(model_args.model_name_or_path  ,			**UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						int       =						{
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    __UpperCAmelCase    :						Dict       =						IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    __UpperCAmelCase    :						Union[str, Any]       =						AutoModelForMaskedImageModeling.from_pretrained(
        model_args.model_name_or_path  ,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )  ,			config=UpperCAmelCase_  ,			cache_dir=model_args.cache_dir  ,			revision=model_args.model_revision  ,			use_auth_token=True if model_args.use_auth_token else None  ,			)
  else:
    logger.info('''Training new model from scratch'''  )
    __UpperCAmelCase    :						Tuple       =						AutoModelForMaskedImageModeling.from_config(UpperCAmelCase_  )

  if training_args.do_train:
    __UpperCAmelCase    :						List[Any]       =						ds['''train'''].column_names
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						ds['''validation'''].column_names

  if data_args.image_column_name is not None:
    __UpperCAmelCase    :						int       =						data_args.image_column_name
  elif "image" in column_names:
    __UpperCAmelCase    :						int       =						'''image'''
  elif "img" in column_names:
    __UpperCAmelCase    :						Union[str, Any]       =						'''img'''
  else:
    __UpperCAmelCase    :						List[Any]       =						column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  __UpperCAmelCase    :						Optional[int]       =						Compose(
      [
          Lambda(lambda UpperCAmelCase_  : img.convert('''RGB'''  ) if img.mode != "RGB" else img  ),
          RandomResizedCrop(model_args.image_size  ,			scale=(0.67, 1.0)  ,			ratio=(3.0 / 4.0, 4.0 / 3.0)  ),
          RandomHorizontalFlip(),
          ToTensor(),
          Normalize(mean=image_processor.image_mean  ,			std=image_processor.image_std  ),
      ]  )

  # create mask generator
  __UpperCAmelCase    :						Any       =						MaskGenerator(
      input_size=model_args.image_size  ,			mask_patch_size=data_args.mask_patch_size  ,			model_patch_size=model_args.patch_size  ,			mask_ratio=data_args.mask_ratio  ,			)

  def preprocess_images(UpperCAmelCase_  ):
    __UpperCAmelCase    :						Optional[Any]       =						[transforms(UpperCAmelCase_  ) for image in examples[image_column_name]]
    __UpperCAmelCase    :						Union[str, Any]       =						[mask_generator() for i in range(len(examples[image_column_name]  )  )]

    return examples

  if training_args.do_train:
    if "train" not in ds:
      raise ValueError('''--do_train requires a train dataset'''  )
    if data_args.max_train_samples is not None:
      __UpperCAmelCase    :						Tuple       =						ds['''train'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
    # Set the training transforms
    ds["train"].set_transform(UpperCAmelCase_  )

  if training_args.do_eval:
    if "validation" not in ds:
      raise ValueError('''--do_eval requires a validation dataset'''  )
    if data_args.max_eval_samples is not None:
      __UpperCAmelCase    :						Tuple       =						(
          ds['''validation'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
      )
    # Set the validation transforms
    ds["validation"].set_transform(UpperCAmelCase_  )

  # Initialize our trainer
  __UpperCAmelCase    :						List[str]       =						Trainer(
      model=UpperCAmelCase_  ,			args=UpperCAmelCase_  ,			train_dataset=ds['''train'''] if training_args.do_train else None  ,			eval_dataset=ds['''validation'''] if training_args.do_eval else None  ,			tokenizer=UpperCAmelCase_  ,			data_collator=UpperCAmelCase_  ,			)

  # Training
  if training_args.do_train:
    __UpperCAmelCase    :						Any       =						None
    if training_args.resume_from_checkpoint is not None:
      __UpperCAmelCase    :						str       =						training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      __UpperCAmelCase    :						Optional[int]       =						last_checkpoint
    __UpperCAmelCase    :						str       =						trainer.train(resume_from_checkpoint=UpperCAmelCase_  )
    trainer.save_model()
    trainer.log_metrics('''train'''  ,			train_result.metrics  )
    trainer.save_metrics('''train'''  ,			train_result.metrics  )
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    __UpperCAmelCase    :						Optional[Any]       =						trainer.evaluate()
    trainer.log_metrics('''eval'''  ,			UpperCAmelCase_  )
    trainer.save_metrics('''eval'''  ,			UpperCAmelCase_  )

  # Write model card and (optionally) push to hub
  __UpperCAmelCase    :						Dict       =						{
      '''finetuned_from''': model_args.model_name_or_path,
      '''tasks''': '''masked-image-modeling''',
      '''dataset''': data_args.dataset_name,
      '''tags''': ['''masked-image-modeling'''],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**UpperCAmelCase_  )
  else:
    trainer.create_model_card(**UpperCAmelCase_  )


if __name__ == "__main__":
  main()






"""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
  )