




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
__magic_name__  :  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""")

__magic_name__  :  Union[str, Any]					=       list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__magic_name__  :  Tuple					=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class     SCREAMING_SNAKE_CASE__		:
     lowercase_ :      Optional[str]				= field(
         default="cifar10"    ,					metadata={"help": "Name of a dataset from the datasets package"}			)
     lowercase_ :      Optional[str]				= field(
         default=_UpperCamelCase    ,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
     lowercase_ :      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\'."}    ,					)
     lowercase_ :      Optional[str]				= field(default=_UpperCamelCase    ,					metadata={"help": "A folder containing the training data."}			)
     lowercase_ :      Optional[str]				= field(default=_UpperCamelCase    ,					metadata={"help": "A folder containing the validation data."}			)
     lowercase_ :      Optional[float]				= field(
         default=0.1_5    ,					metadata={"help": "Percent to split off of train for validation."}			)
     lowercase_ :      int				= field(default=32    ,					metadata={"help": "The size of the square patches to use for masking."}			)
     lowercase_ :      float				= field(
         default=0.6    ,					metadata={"help": "Percentage of patches to mask."}    ,					)
     lowercase_ :      Optional[int]				= field(
         default=_UpperCamelCase    ,					metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }    ,					)
     lowercase_ :      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	:		str						):



       """simple docstring"""
       lowerCAmelCase__										=      {}
       if self.train_dir is not None:
         lowerCAmelCase__										=      self.train_dir
       if self.validation_dir is not None:
         lowerCAmelCase__										=      self.validation_dir
       lowerCAmelCase__										=      data_files if data_files else None





@dataclass
class     SCREAMING_SNAKE_CASE__		:
     lowercase_ :      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."
             )
         }    ,					)
     lowercase_ :      Optional[str]				= field(
         default=_UpperCamelCase    ,					metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_UpperCamelCase			)}    ,					)
     lowercase_ :      Optional[str]				= field(
         default=_UpperCamelCase    ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
     lowercase_ :      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"
             )
         }    ,					)
     lowercase_ :      Optional[str]				= field(
         default=_UpperCamelCase    ,					metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}    ,					)
     lowercase_ :      str				= field(
         default="main"    ,					metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,					)
     lowercase_ :      str				= field(default=_UpperCamelCase    ,					metadata={"help": "Name or path of preprocessor config."}			)
     lowercase_ :      bool				= field(
         default=_UpperCamelCase    ,					metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }    ,					)
     lowercase_ :      Optional[int]				= field(
         default=_UpperCamelCase    ,					metadata={
             "help": (
                 "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
             )
         }    ,					)
     lowercase_ :      Optional[int]				= field(
         default=_UpperCamelCase    ,					metadata={
             "help": (
                 "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
             )
         }    ,					)
     lowercase_ :      Optional[int]				= field(
         default=_UpperCamelCase    ,					metadata={"help": "Stride to use for the encoder."}    ,					)





class     SCREAMING_SNAKE_CASE__		:
     def __init__(						self	:		Tuple       ,						__lowerCamelCase	:		Dict=1_92       ,						__lowerCamelCase	:		Tuple=32       ,						__lowerCamelCase	:		str=4       ,						__lowerCamelCase	:		Any=0.6						):



       """simple docstring"""
       lowerCAmelCase__										=      input_size
       lowerCAmelCase__										=      mask_patch_size
       lowerCAmelCase__										=      model_patch_size
       lowerCAmelCase__										=      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'''						)

       lowerCAmelCase__										=      self.input_size // self.mask_patch_size
       lowerCAmelCase__										=      self.mask_patch_size // self.model_patch_size

       lowerCAmelCase__										=      self.rand_size**2
       lowerCAmelCase__										=      int(np.ceil(self.token_count * self.mask_ratio						)						)



     def __call__(						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      np.random.permutation(self.token_count						)[: self.mask_count]
       lowerCAmelCase__										=      np.zeros(self.token_count       ,						dtype=__lowerCamelCase						)
       lowerCAmelCase__										=      1

       lowerCAmelCase__										=      mask.reshape((self.rand_size, self.rand_size)						)
       lowerCAmelCase__										=      mask.repeat(self.scale       ,						axis=0						).repeat(self.scale       ,						axis=1						)

       return torch.tensor(mask.flatten()						)



def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      torch.stack([example['''pixel_values'''] for example in examples]				)
  lowerCAmelCase__										=      torch.stack([example['''mask'''] for example in examples]				)
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 		a_							( ):
  lowerCAmelCase__										=      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.
    lowerCAmelCase__										=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
  else:
    lowerCAmelCase__										=      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'''   , SCREAMING_SNAKE_CASE__   , SCREAMING_SNAKE_CASE__				)

  # 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()

  lowerCAmelCase__										=      training_args.get_process_log_level()
  logger.setLevel(SCREAMING_SNAKE_CASE__				)
  transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__				)
  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.
  lowerCAmelCase__										=      None
  if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
    lowerCAmelCase__										=      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.
  lowerCAmelCase__										=      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.
  lowerCAmelCase__										=      None if "validation" in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split   , SCREAMING_SNAKE_CASE__				) and data_args.train_val_split > 0.0:
    lowerCAmelCase__										=      ds["train"].train_test_split(data_args.train_val_split				)
    lowerCAmelCase__										=      split["train"]
    lowerCAmelCase__										=      split["test"]

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowerCAmelCase__										=      {
      "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:
    lowerCAmelCase__										=      AutoConfig.from_pretrained(model_args.config_name_or_path   , **SCREAMING_SNAKE_CASE__				)
  elif model_args.model_name_or_path:
    lowerCAmelCase__										=      AutoConfig.from_pretrained(model_args.model_name_or_path   , **SCREAMING_SNAKE_CASE__				)
  else:
    lowerCAmelCase__										=      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(SCREAMING_SNAKE_CASE__   , '''decoder_type'''				):
    lowerCAmelCase__										=      "simmim"

  # adapt config
  lowerCAmelCase__										=      model_args.image_size if model_args.image_size is not None else config.image_size
  lowerCAmelCase__										=      model_args.patch_size if model_args.patch_size is not None else config.patch_size
  lowerCAmelCase__										=      (
      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:
    lowerCAmelCase__										=      AutoImageProcessor.from_pretrained(model_args.image_processor_name   , **SCREAMING_SNAKE_CASE__				)
  elif model_args.model_name_or_path:
    lowerCAmelCase__										=      AutoImageProcessor.from_pretrained(model_args.model_name_or_path   , **SCREAMING_SNAKE_CASE__				)
  else:
    lowerCAmelCase__										=      {
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    lowerCAmelCase__										=      IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    lowerCAmelCase__										=      AutoModelForMaskedImageModeling.from_pretrained(
        model_args.model_name_or_path   , from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)   , config=SCREAMING_SNAKE_CASE__   , 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'''				)
    lowerCAmelCase__										=      AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__				)

  if training_args.do_train:
    lowerCAmelCase__										=      ds["train"].column_names
  else:
    lowerCAmelCase__										=      ds["validation"].column_names

  if data_args.image_column_name is not None:
    lowerCAmelCase__										=      data_args.image_column_name
  elif "image" in column_names:
    lowerCAmelCase__										=      "image"
  elif "img" in column_names:
    lowerCAmelCase__										=      "img"
  else:
    lowerCAmelCase__										=      column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  lowerCAmelCase__										=      Compose(
      [
          Lambda(lambda __lowerCAmelCase				: 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
  lowerCAmelCase__										=      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(__lowerCAmelCase				):
    lowerCAmelCase__										=      [transforms(SCREAMING_SNAKE_CASE__				) for image in examples[image_column_name]]
    lowerCAmelCase__										=      [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:
      lowerCAmelCase__										=      ds["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
    # Set the training transforms
    ds["train"].set_transform(SCREAMING_SNAKE_CASE__				)

  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:
      lowerCAmelCase__										=      (
          ds["validation"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
      )
    # Set the validation transforms
    ds["validation"].set_transform(SCREAMING_SNAKE_CASE__				)

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

  # Training
  if training_args.do_train:
    lowerCAmelCase__										=      None
    if training_args.resume_from_checkpoint is not None:
      lowerCAmelCase__										=      training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowerCAmelCase__										=      last_checkpoint
    lowerCAmelCase__										=      trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__				)
    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:
    lowerCAmelCase__										=      trainer.evaluate()
    trainer.log_metrics('''eval'''   , SCREAMING_SNAKE_CASE__				)
    trainer.save_metrics('''eval'''   , SCREAMING_SNAKE_CASE__				)

  # Write model card and (optionally) push to hub
  lowerCAmelCase__										=      {
      "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(**SCREAMING_SNAKE_CASE__				)
  else:
    trainer.create_model_card(**SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
      main()






"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       tmp_path / "file.csv"
     _UpperCAmelCase     :      Union[str, Any]														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       tmp_path / "malformed_file.csv"
     _UpperCAmelCase     :      Dict														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       tmp_path / "csv_with_image.csv"
     _UpperCAmelCase     :      List[str]														=       textwrap.dedent(
         f'\\n        image\n        {image_file}\n        '							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       tmp_path / "csv_with_label.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       tmp_path / "csv_with_int_list.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       Csv()
     _UpperCAmelCase     :      Optional[int]														=       csv._generate_tables([[csv_file, malformed_csv_file]]							)
     with pytest.raises(SCREAMING_SNAKE_CASE__		,    match="Error tokenizing data"							):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(SCREAMING_SNAKE_CASE__							) in record.message
         for record in caplog.records							)

@require_pil
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Union[str, Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Union[str, Any]														=       f.read().splitlines()[1]
     _UpperCAmelCase     :      Tuple														=       Csv(encoding="utf-8"		,    features=Features({"image": Image()}							)							)
     _UpperCAmelCase     :      Tuple														=       csv._generate_tables([[csv_file_with_image]]							)
     _UpperCAmelCase     :      List[str]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("image"							).type == Image()()
     _UpperCAmelCase     :      Optional[Any]														=       pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Tuple														=       f.read().splitlines()[1:]
     _UpperCAmelCase     :      Optional[Any]														=       Csv(encoding="utf-8"		,    features=Features({"label": ClassLabel(names=["good", "bad"]							)}							)							)
     _UpperCAmelCase     :      int														=       csv._generate_tables([[csv_file_with_label]]							)
     _UpperCAmelCase     :      Dict														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("label"							).type == ClassLabel(names=["good", "bad"]							)()
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]							).straint(SCREAMING_SNAKE_CASE__							) for label in labels]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       Csv(encoding="utf-8"		,    sep=","		,    converters={"int_list": lambda SCREAMING_SNAKE_CASE__							: [int(SCREAMING_SNAKE_CASE__							) for i in x.split()]}							)
     _UpperCAmelCase     :      Dict														=       csv._generate_tables([[csv_file_with_int_list]]							)
     _UpperCAmelCase     :      List[Any]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa.types.is_list(pa_table.schema.field("int_list"							).type							)
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
