




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		:				List[Any]       			=		datasets.utils.logging.get_logger(__name__)




@dataclass
class     snake_case__							(datasets.BuilderConfig   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					1_00_00
				SCREAMING_SNAKE_CASE_       :							Optional[List[str]]							 =					None
				SCREAMING_SNAKE_CASE_       :							Optional[datasets.Features]							 =					None




class     snake_case__							(datasets.ArrowBasedBuilder   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]							 =					ParquetConfig





				def 							__UpperCAmelCase							(      self				:     int							)					->     Any:
							return datasets.DatasetInfo(features=self.config.features							)





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Dict							)					->     Union[str, Any]:
							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    =			dl_manager.download_and_extract(self.config.data_files							)
							if isinstance(__lowerCamelCase					,      (str, list, tuple)							):
										a    =			data_files
										if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
													a    =			[files]
										# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
										a    =			[dl_manager.iter_files(__lowerCamelCase							) for file in files]
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,      gen_kwargs={"files": files}							)]
							a    =			[]
							for split_name, files in data_files.items():
										if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
													a    =			[files]
										# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
										a    =			[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    =			datasets.Features.from_arrow_schema(pq.read_schema(__lowerCamelCase							)							)
																break
										splits.append(datasets.SplitGenerator(name=__lowerCamelCase					,      gen_kwargs={"files": files}							)							)
							return splits





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     pa.Table							)					->     pa.Table:
							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    =			table_cast(__lowerCamelCase					,      self.info.features.arrow_schema							)
							return pa_table





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Union[str, Any]							)					->     Optional[int]:
							a    =			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    =			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    =			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












import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


__lowerCAmelCase		:				List[Any]       			=		logging.get_logger(__name__)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Union[str, Any]					,      *__lowerCamelCase				:     Optional[Any]					,      **__lowerCamelCase				:     Dict							)					->     None:
							warnings.warn(
							    "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use DonutImageProcessor instead."					,      __lowerCamelCase					,      )
							super().__init__(*__lowerCamelCase					,      **__lowerCamelCase							)






