"""simple docstring"""





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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
lowerCAmelCase_     =	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-classification/requirements.txt''')

lowerCAmelCase_     =	list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase_     =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Tuple:
							with open(__SCREAMING_SNAKE_CASE     ,	"""rb"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						str			  =    Image.open(__SCREAMING_SNAKE_CASE  )
														return im.convert("""RGB"""  )




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
				a			=						field(default=__snake_case   ,       metadata={"help": "A folder containing the training data."} )
				a			=						field(default=__snake_case   ,       metadata={"help": "A folder containing the validation data."} )
				a			=						field(
				    default=0.15   ,       metadata={"help": "Percent to split off of train for validation."} )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of training examples to this "
				            "value if set."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
				            "value if set."
				        )
				    }   ,       )





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
																		raise ValueError(
																		    """You must specify either a dataset name from the hub or a train and/or validation directory.""")




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(
				    default="google/vit-base-patch16-224-in21k"   ,       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__snake_case )}   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
				a			=						field(
				    default="main"   ,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}   ,       )
				a			=						field(default=__snake_case   ,       metadata={"help": "Name or path of preprocessor config."} )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
				            "with private models)."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}   ,       )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.stack([example["""pixel_values"""] for example in examples]  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.tensor([example["""labels"""] for example in examples]  )
							return {"pixel_values": pixel_values, "labels": labels}



def      lowerCamelCase_()->    Any:
							# 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.

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    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.
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    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_image_classification"""     ,	__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()

							_SCREAMING_SNAKE_CASE		:						str			  =    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.
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														_SCREAMING_SNAKE_CASE		:						int			  =    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."""  )

    # Set seed before initializing model.
							set_seed(training_args.seed  )

							# Initialize our dataset and prepare it for the 'image-classification' task.
							if data_args.dataset_name is not None:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    load_dataset(
														    data_args.dataset_name     ,	data_args.dataset_config_name     ,	cache_dir=model_args.cache_dir     ,	task="""image-classification"""     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
							else:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    {}
														if data_args.train_dir is not None:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    os.path.join(data_args.train_dir     ,	"""**"""  )
														if data_args.validation_dir is not None:
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    os.path.join(data_args.validation_dir     ,	"""**"""  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    load_dataset(
														    """imagefolder"""     ,	data_files=__SCREAMING_SNAKE_CASE     ,	cache_dir=model_args.cache_dir     ,	task="""image-classification"""     ,	)

							# If we don't have a validation split, split off a percentage of train as validation.
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    None if """validation""" in dataset.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:
														_SCREAMING_SNAKE_CASE		:						Any			  =    dataset["""train"""].train_test_split(data_args.train_val_split  )
														_SCREAMING_SNAKE_CASE		:						Dict			  =    split["""train"""]
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    split["""test"""]

							# Prepare label mappings.
							# We'll include these in the model's config to get human readable labels in the Inference API.
							_SCREAMING_SNAKE_CASE		:						int			  =    dataset["""train"""].features["""labels"""].names
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    {}, {}
							for i, label in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						str			  =    str(__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    label

							# Load the accuracy metric from the datasets package
							_SCREAMING_SNAKE_CASE		:						int			  =    evaluate.load("""accuracy"""  )

							# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(__SCREAMING_SNAKE_CASE  ):
														return metric.compute(predictions=np.argmax(p.predictions     ,	axis=1  )     ,	references=p.label_ids  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path     ,	num_labels=len(__SCREAMING_SNAKE_CASE  )     ,	labelaid=__SCREAMING_SNAKE_CASE     ,	idalabel=__SCREAMING_SNAKE_CASE     ,	finetuning_task="""image-classification"""     ,	cache_dir=model_args.cache_dir     ,	revision=model_args.model_revision     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
							_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoModelForImageClassification.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     ,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,	)
							_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoImageProcessor.from_pretrained(
							    model_args.image_processor_name or model_args.model_name_or_path     ,	cache_dir=model_args.cache_dir     ,	revision=model_args.model_revision     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)

							# Define torchvision transforms to be applied to each image.
							if "shortest_edge" in image_processor.size:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    image_processor.size["""shortest_edge"""]
							else:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    (image_processor.size["""height"""], image_processor.size["""width"""])
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    Normalize(mean=image_processor.image_mean     ,	std=image_processor.image_std  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    Compose(
							    [
							        RandomResizedCrop(__SCREAMING_SNAKE_CASE  ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Compose(
							    [
							        Resize(__SCREAMING_SNAKE_CASE  ),
							        CenterCrop(__SCREAMING_SNAKE_CASE  ),
							        ToTensor(),
							        normalize,
							    ]  )

							def train_transforms(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    [
														    _train_transforms(pil_img.convert("""RGB"""  )  ) for pil_img in example_batch["""image"""]
														]
														return example_batch

							def val_transforms(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [_val_transforms(pil_img.convert("""RGB"""  )  ) for pil_img in example_batch["""image"""]]
														return example_batch

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

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

							# Initalize our trainer
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Trainer(
							    model=__SCREAMING_SNAKE_CASE     ,	args=__SCREAMING_SNAKE_CASE     ,	train_dataset=dataset["""train"""] if training_args.do_train else None     ,	eval_dataset=dataset["""validation"""] if training_args.do_eval else None     ,	compute_metrics=__SCREAMING_SNAKE_CASE     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	data_collator=__SCREAMING_SNAKE_CASE     ,	)

							# Training
							if training_args.do_train:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
														if training_args.resume_from_checkpoint is not None:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    last_checkpoint
														_SCREAMING_SNAKE_CASE		:						Dict			  =    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:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    trainer.evaluate()
														trainer.log_metrics("""eval"""     ,	__SCREAMING_SNAKE_CASE  )
														trainer.save_metrics("""eval"""     ,	__SCREAMING_SNAKE_CASE  )

							# Write model card and (optionally) push to hub
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    {
							    """finetuned_from""": model_args.model_name_or_path,
							    """tasks""": """image-classification""",
							    """dataset""": data_args.dataset_name,
							    """tags""": ["""image-classification""", """vision"""],
							}
							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 pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory"""     ,	[False, True]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize(
    """features"""     ,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]     ,	)
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						int			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						str			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize("""split"""     ,	[None, NamedSplit("""train"""  ), """train""", """test"""]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						int			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	split=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""     ,	[str, list]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							if issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Any			  =    parquet_path
							elif issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [parquet_path]
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						str			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=("train",)  )->    Union[str, Any]:
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							for split in splits:
														_SCREAMING_SNAKE_CASE		:						int			  =    dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 3
														assert dataset.column_names == ["col_1", "col_2", "col_3"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory"""     ,	[False, True]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    ParquetDatasetReader(
														    {"""train""": parquet_path}     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize(
    """features"""     ,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]     ,	)
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						str			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						int			  =    ParquetDatasetReader({"""train""": parquet_path}     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize("""split"""     ,	[None, NamedSplit("""train"""  ), """train""", """test"""]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							if split:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {split: parquet_path}
							else:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """train"""
														_SCREAMING_SNAKE_CASE		:						Any			  =    {"""train""": parquet_path, """test""": parquet_path}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	splits=list(path.keys()  )  )
							assert all(dataset[split].split == split for split in path.keys()  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    ParquetDatasetWriter(__SCREAMING_SNAKE_CASE     ,	tmp_path / """foo.parquet"""  )
							assert writer.write() > 0
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    pq.ParquetFile(tmp_path / """foo.parquet"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    pf.read()
							assert dataset.data.table == output_table



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    str(shared_datadir / """test_image_rgb.jpg"""  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""image""": [image_path]}
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    Features({"""image""": Image()}  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Dataset.from_dict(__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    ParquetDatasetWriter(__SCREAMING_SNAKE_CASE     ,	tmp_path / """foo.parquet"""  )
							assert writer.write() > 0

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    Dataset.from_parquet(str(tmp_path / """foo.parquet"""  )  )
							assert dataset.features == reloaded_dataset.features

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(str(tmp_path / """foo.parquet"""  )     ,	streaming=__SCREAMING_SNAKE_CASE  ).read()
							assert dataset.features == reloaded_iterable_dataset.features



@pytest.mark.parametrize(
    """feature, expected"""     ,	[
        (Features({"""foo""": Value("""int32"""  )}  ), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]     ,	)
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							assert get_writer_batch_size(__SCREAMING_SNAKE_CASE  ) == expected


