






"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_lowerCAmelCase       =				datasets.utils.logging.get_logger(__name__)



@dataclass
class   SCREAMING_SNAKE_CASE	(					datasets.BuilderConfig    ):





	"""simple docstring"""




	_A							:					int 			=					1_0000
	_A							:					Optional[List[str]] 			=					None
	_A							:					Optional[datasets.Features] 			=					None






class   SCREAMING_SNAKE_CASE	(					datasets.ArrowBasedBuilder    ):





	"""simple docstring"""




	_A							:					Any 			=					ParquetConfig





	def  lowerCamelCase(self				):
		return datasets.DatasetInfo(features=self.config.features				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if not self.config.data_files:
			raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""				)
		A_		:    Tuple						     = dl_manager.download_and_extract(self.config.data_files				)
		if isinstance(lowerCAmelCase_  ,  (str, list, tuple)				):
			A_		:    Tuple						     = data_files
			if isinstance(lowerCAmelCase_  ,  lowerCAmelCase_				):
				A_		:    Optional[int]						     = [files]
			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
			A_		:    Optional[int]						     = [dl_manager.iter_files(lowerCAmelCase_				) for file in files]
			return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,  gen_kwargs={"""files""": files}				)]
		A_		:    Any						     = []
		for split_name, files in data_files.items():
			if isinstance(lowerCAmelCase_  ,  lowerCAmelCase_				):
				A_		:    int						     = [files]
			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
			A_		:    Optional[int]						     = [dl_manager.iter_files(lowerCAmelCase_				) for file in files]
			# Infer features is they are stoed in the arrow schema
			if self.info.features is None:
				for file in itertools.chain.from_iterable(lowerCAmelCase_				):
					with open(lowerCAmelCase_  ,  """rb"""				) as f:
						A_		:    List[Any]						     = datasets.Features.from_arrow_schema(pq.read_schema(lowerCAmelCase_				)				)
					break
			splits.append(datasets.SplitGenerator(name=lowerCAmelCase_  ,  gen_kwargs={"""files""": files}				)				)
		return splits





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.info.features is not None:
			# more expensive cast to support nested features with keys in a different order
			# allows str <-> int/float or str to Audio for example
			A_		:    Union[str, Any]						     = table_cast(lowerCAmelCase_  ,  self.info.features.arrow_schema				)
		return pa_table





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = self.info.features.arrow_schema if self.info.features is not None else None
		if self.info.features is not None and self.config.columns is not None:
			if sorted(field.name for field in schema				) != sorted(self.config.columns				):
				raise ValueError(
				    f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""				)
		for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase_				)				):
			with open(lowerCAmelCase_  ,  """rb"""				) as f:
				A_		:    List[Any]						     = pq.ParquetFile(lowerCAmelCase_				)
				try:
					for batch_idx, record_batch in enumerate(
					    parquet_file.iter_batches(batch_size=self.config.batch_size  ,  columns=self.config.columns				)				):
						A_		:    List[str]						     = pa.Table.from_batches([record_batch]				)
						# Uncomment for debugging (will print the Arrow table size and elements)
						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
						yield f"""{file_idx}_{batch_idx}""", self._cast_table(lowerCAmelCase_				)
				except ValueError as e:
					logger.error(f"""Failed to read file '{file}' with error {type(lowerCAmelCase_				)}: {e}"""				)
					raise









"""simple docstring"""
_lowerCAmelCase       =				[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages

