




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__    :					Optional[int]					=						{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Tuple					=						[
		    "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "QDQBertForMaskedLM",
		    "QDQBertForMultipleChoice",
		    "QDQBertForNextSentencePrediction",
		    "QDQBertForQuestionAnswering",
		    "QDQBertForSequenceClassification",
		    "QDQBertForTokenClassification",
		    "QDQBertLayer",
		    "QDQBertLMHeadModel",
		    "QDQBertModel",
		    "QDQBertPreTrainedModel",
		    "load_tf_weights_in_qdqbert",
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

		lowercase__    :					Union[str, Any]					=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__    :					str					=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 		UpperCAmelCase       (    datasets.BuilderConfig      ):







						'''simple docstring'''





						lowerCAmelCase_							=       None







class 		UpperCAmelCase       (    datasets.ArrowBasedBuilder      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ArrowConfig






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							return datasets.DatasetInfo(features=self.config.features						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							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}"						)
							snake_case_		   =					dl_manager.download_and_extract(self.config.data_files						)
							if isinstance(__lowercase	,     (str, list, tuple)						):
								snake_case_		   =					data_files
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,     gen_kwargs={"files": files}						)]
							snake_case_		   =					[]
							for split_name, files in data_files.items():
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) 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(__lowercase						):
										with open(__lowercase	,     "rb"						) as f:
											snake_case_		   =					datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowercase						).schema						)
										break
								splits.append(datasets.SplitGenerator(name=__lowercase	,     gen_kwargs={"files": files}						)						)
							return splits






						def 	snake_case__						(						self							:					str	,     __lowercase							:					pa.Table						):






							"""simple docstring"""






							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
								snake_case_		   =					table_cast(__lowercase	,     self.info.features.arrow_schema						)
							return pa_table






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase						)						):
								with open(__lowercase	,     "rb"						) as f:
									try:
										for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowercase						)						):
											snake_case_		   =					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(__lowercase						)
									except ValueError as e:
										logger.error(f"Failed to read file '{file}' with error {type(__lowercase						)}: {e}"						)
										raise
