





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_         =						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(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowercase_         =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 							SCREAMING_SNAKE_CASE  :
   _UpperCamelCase       :						str					   =			field(
       default='cifar10'     ,      metadata={'help': 'Name of a dataset from the datasets package'} )
   _UpperCamelCase       :						Optional[int]					   =			field(
       default=_a     ,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
   _UpperCamelCase       :						Union[str, Any]					   =			field(
       default=_a     ,      metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}     ,      )
   _UpperCamelCase       :						Tuple					   =			field(default=_a     ,      metadata={'help': 'A folder containing the training data.'} )
   _UpperCamelCase       :						str					   =			field(default=_a     ,      metadata={'help': 'A folder containing the validation data.'} )
   _UpperCamelCase       :						Any					   =			field(
       default=0.15     ,      metadata={'help': 'Percent to split off of train for validation.'} )
   _UpperCamelCase       :						int					   =			field(default=32     ,      metadata={'help': 'The size of the square patches to use for masking.'} )
   _UpperCamelCase       :						Any					   =			field(
       default=0.6     ,      metadata={'help': 'Percentage of patches to mask.'}     ,      )
   _UpperCamelCase       :						Optional[int]					   =			field(
       default=_a     ,      metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       }     ,      )
   _UpperCamelCase       :						Optional[int]					   =			field(
       default=_a     ,      metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
               'value if set.'
           )
       }     ,      )


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[Any]			)->  int:





     """simple docstring"""







     lowercase__           =    {}
     if self.train_dir is not None:
       lowercase__           =    self.train_dir
     if self.validation_dir is not None:
       lowercase__           =    self.validation_dir
     lowercase__           =    data_files if data_files else None






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






class 							SCREAMING_SNAKE_CASE  :


   def __init__(       self     :							Any     ,	a     :							Union[str, Any]=192     ,	a     :							Dict=32     ,	a     :							Tuple=4     ,	a     :							Union[str, Any]=0.6			)->  List[Any]:





     """simple docstring"""







     lowercase__           =    input_size
     lowercase__           =    mask_patch_size
     lowercase__           =    model_patch_size
     lowercase__           =    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'			)

     lowercase__           =    self.input_size // self.mask_patch_size
     lowercase__           =    self.mask_patch_size // self.model_patch_size

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


   def __call__(       self     :							Any			)->  Union[str, Any]:





     """simple docstring"""







     lowercase__           =    np.random.permutation(self.token_count			)[: self.mask_count]
     lowercase__           =    np.zeros(self.token_count     ,	dtype=lowerCamelCase__			)
     lowercase__           =    1

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

     return torch.tensor(mask.flatten()			)






def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						)							->  str:
  lowercase__           =    torch.stack([example['pixel_values'] for example in examples]						)
  lowercase__           =    torch.stack([example['mask'] for example in examples]						)
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def      __UpperCamelCase    ()							->  List[str]:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  lowercase__           =    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.
    lowercase__           =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    lowercase__           =    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()

  lowercase__           =    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.
  lowercase__           =    None
  if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
    lowercase__           =    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.
  lowercase__           =    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.
  lowercase__           =    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:
    lowercase__           =    ds["train"].train_test_split(data_args.train_val_split						)
    lowercase__           =    split["train"]
    lowercase__           =    split["test"]

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

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

  # create model
  if model_args.model_name_or_path:
    lowercase__           =    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'						)
    lowercase__           =    AutoModelForMaskedImageModeling.from_config(_SCREAMING_SNAKE_CASE						)

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

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

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  lowercase__           =    Compose(
      [
          Lambda(lambda _SCREAMING_SNAKE_CASE						: img.convert('RGB'						) if img.mode != "RGB" else img						),
          RandomResizedCrop(model_args.image_size						,		scale=(0.6_7, 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
  lowercase__           =    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(_SCREAMING_SNAKE_CASE						):
    lowercase__           =    [transforms(_SCREAMING_SNAKE_CASE						) for image in examples[image_column_name]]
    lowercase__           =    [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:
      lowercase__           =    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:
      lowercase__           =    (
          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
  lowercase__           =    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:
    lowercase__           =    None
    if training_args.resume_from_checkpoint is not None:
      lowercase__           =    training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowercase__           =    last_checkpoint
    lowercase__           =    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:
    lowercase__           =    trainer.evaluate()
    trainer.log_metrics('eval'						,		_SCREAMING_SNAKE_CASE						)
    trainer.save_metrics('eval'						,		_SCREAMING_SNAKE_CASE						)

  # Write model card and (optionally) push to hub
  lowercase__           =    {
      "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()





import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([1, 2, 3]						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises(lowerCamelCase__						):
							UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([1, 2, 3]						)				,      type=pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises(lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= pa.array(TypedSequence([1, 2, 3]				,      try_type=Value("bool"						)				,      type=Value("int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= pa.array(TypedSequence([1, 2, 3]				,      type=Value("int32"						)						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
							UpperCAmelCase__:	List[str]									= pa.array(TypedSequence(["foo", "bar"]				,      type=Value("int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= pa.array(TypedSequence([1, 2, 3]				,      try_type=Value("int32"						)						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= pa.array(TypedSequence(["foo", "bar"]				,      try_type=Value("int64"						)						)						)
						self.assertEqual(arr.type				,      pa.string()						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([[[1, 2, 3]]]				,      type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      ArrayaDExtensionType((1, 3)				,      "int64"						)						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
							UpperCAmelCase__:	Union[str, Any]									= pa.array(TypedSequence(["foo", "bar"]				,      type=ArrayaD((1, 3)				,      "int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= pa.array(TypedSequence([[[1, 2, 3]]]				,      try_type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      ArrayaDExtensionType((1, 3)				,      "int64"						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= pa.array(TypedSequence(["foo", "bar"]				,      try_type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      pa.string()						)





					@require_pil
					def    _UpperCAmelCase    (				self						):
						import PIL.Image

						UpperCAmelCase__:	int									= PIL.Image.fromarray(np.arange(1_0				,      dtype=np.uinta						).reshape(2				,      5						)						)
						with patch(
						    "datasets.arrow_writer.cast_to_python_objects"				,      side_effect=lowerCamelCase__						) as mock_cast_to_python_objects:
							UpperCAmelCase__:	Tuple									= pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]				,      type=Image()						)						)
							UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= mock_cast_to_python_objects.call_args_list[-1]
							self.assertIn("optimize_list_casting"				,      lowerCamelCase__						)
							self.assertFalse(kwargs["optimize_list_casting"]						)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= pa.BufferReader(SCREAMING_SNAKE_CASE      ) if isinstance(SCREAMING_SNAKE_CASE	,pa.Buffer      ) else pa.memory_map(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	str									= pa.ipc.open_stream(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	pa.Table									= f.read_all()
	assert len(pa_table.to_batches()      ) == expected_num_chunks
	assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
	del pa_table



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	UpperCAmelCase__:	Tuple									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Optional[int]									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



def 			_A	(      ):
	UpperCAmelCase__:	Tuple									= pa.BufferOutputStream()
	UpperCAmelCase__:	Any									= Features({"labels": ClassLabel(names=["neg", "pos"]      )}      )
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,features=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"labels": 0}      )
		writer.write({"labels": 1}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	assert writer._schema == features.arrow_schema
	assert writer._schema.metadata == features.arrow_schema.metadata
	UpperCAmelCase__:	Dict									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	Any									= pa.ipc.open_stream(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	pa.Table									= f.read_all()
	UpperCAmelCase__:	Union[str, Any]									= pa_table.schema
	assert pa_table.num_rows == 2
	assert schema == features.arrow_schema
	assert schema.metadata == features.arrow_schema.metadata
	assert features == Features.from_arrow_schema(SCREAMING_SNAKE_CASE      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		with pytest.raises(SCREAMING_SNAKE_CASE      ):
			writer.write({"col_1": "foo", "col_2": 1}	,key=[1, 2]      )
			UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()



@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		with pytest.raises(SCREAMING_SNAKE_CASE      ):
			writer.write({"col_1": "foo", "col_2": 1}	,key=1_0      )
			writer.write({"col_1": "bar", "col_2": 2}	,key=1_0      )
			UpperCAmelCase__							,						UpperCAmelCase__:	str									= writer.finalize()



@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Dict									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		writer.write({"col_1": "foo", "col_2": 1}	,key=1      )
		writer.write({"col_1": "bar", "col_2": 2}	,key=2      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= pa.BufferOutputStream()
	UpperCAmelCase__:	Optional[Any]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )
		writer.write_batch({"col_1": [], "col_2": []}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Optional[Any]									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= pa.BufferOutputStream()
	UpperCAmelCase__:	List[str]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	str									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	UpperCAmelCase__:	List[Any]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}      )      )
		writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}      )      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Dict									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



def 			_A	(      ):
	with tempfile.TemporaryDirectory() as tmp_dir:
		UpperCAmelCase__:	List[str]									= {"col_1": pa.string(), "col_2": pa.intaa()}
		UpperCAmelCase__:	str									= os.path.join(SCREAMING_SNAKE_CASE	,"test.arrow"      )
		with ArrowWriter(path=SCREAMING_SNAKE_CASE	,schema=pa.schema(SCREAMING_SNAKE_CASE      )      ) as writer:
			writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )
			UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
		_check_output(SCREAMING_SNAKE_CASE	,1      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	if pa.types.is_list(SCREAMING_SNAKE_CASE      ):
		return get_base_dtype(arr_type.value_type      )
	else:
		return arr_type



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if isinstance(lst[0]	,SCREAMING_SNAKE_CASE      ):
		change_first_primitive_element_in_list(lst[0]	,SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	Dict									= value



@pytest.mark.parametrize("optimized_int_type, expected_dtype"	,[(None, pa.intaa()), (Value("int32"      ), pa.intaa())]      )
@pytest.mark.parametrize("sequence"	,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= pa.array(TypedSequence(SCREAMING_SNAKE_CASE	,optimized_int_type=SCREAMING_SNAKE_CASE      )      )
	assert get_base_dtype(arr.type      ) == expected_dtype



@pytest.mark.parametrize(
    "col, expected_dtype"	,[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]	,)
@pytest.mark.parametrize("sequence"	,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	# in range
	UpperCAmelCase__:	List[Any]									= pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE	,col=SCREAMING_SNAKE_CASE      )      )
	assert get_base_dtype(arr.type      ) == expected_dtype

	# not in range
	if col != "other":
		# avoids errors due to in-place modifications
		UpperCAmelCase__:	Dict									= copy.deepcopy(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	List[Any]									= np.iinfo(expected_dtype.to_pandas_dtype()      ).max + 1
		change_first_primitive_element_in_list(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Tuple									= pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE	,col=SCREAMING_SNAKE_CASE      )      )
		assert get_base_dtype(arr.type      ) == pa.intaa()



@pytest.mark.parametrize("raise_exception"	,[False, True]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= str(tmp_path / "dataset-train.arrow"      )
	try:
		with ArrowWriter(path=SCREAMING_SNAKE_CASE      ) as writer:
			if raise_exception:
				raise pa.lib.ArrowInvalid()
			else:
				writer.stream.close()
	except pa.lib.ArrowInvalid:
		pass
	finally:
		assert writer.stream.closed



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= "mock://dataset-train.arrow"
	with ArrowWriter(path=SCREAMING_SNAKE_CASE	,storage_options=mockfs.storage_options      ) as writer:
		assert isinstance(writer._fs	,type(SCREAMING_SNAKE_CASE      )      )
		assert writer._fs.storage_options == mockfs.storage_options
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	assert mockfs.exists(SCREAMING_SNAKE_CASE      )



def 			_A	(      ):
	UpperCAmelCase__:	Dict									= pa.BufferOutputStream()
	with ParquetWriter(stream=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	str									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	UpperCAmelCase__:	str									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	pa.Table									= pq.read_table(SCREAMING_SNAKE_CASE      )
	assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}



@require_pil
@pytest.mark.parametrize("embed_local_files"	,[False, True]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	import PIL.Image

	UpperCAmelCase__:	List[str]									= str(tmp_path / "test_image_rgb.jpg"      )
	PIL.Image.fromarray(np.zeros((5, 5)	,dtype=np.uinta      )      ).save(SCREAMING_SNAKE_CASE	,format="png"      )
	UpperCAmelCase__:	List[Any]									= pa.BufferOutputStream()
	with ParquetWriter(
	    stream=SCREAMING_SNAKE_CASE	,features=Features({"image": Image()}      )	,embed_local_files=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"image": image_path}      )
		writer.finalize()
	UpperCAmelCase__:	Optional[int]									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	pa.Table									= pq.read_table(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= pa_table.to_pydict()
	if embed_local_files:
		assert isinstance(out["image"][0]["path"]	,SCREAMING_SNAKE_CASE      )
		with open(SCREAMING_SNAKE_CASE	,"rb"      ) as f:
			assert out["image"][0]["bytes"] == f.read()
	else:
		assert out["image"][0]["path"] == image_path
		assert out["image"][0]["bytes"] is None



def 			_A	(      ):
	UpperCAmelCase__:	List[str]									= pa.schema([pa.field("col_1"	,pa.string()	,nullable=SCREAMING_SNAKE_CASE      )]      )
	UpperCAmelCase__:	Optional[Any]									= pa.BufferOutputStream()
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE      ) as writer:
		writer._build_writer(inferred_schema=SCREAMING_SNAKE_CASE      )
		assert writer._schema == pa.schema([pa.field("col_1"	,pa.string()      )]      )