




import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__   :    Union[str, Any] 					=      datasets.utils.logging.get_logger(__name__)




@dataclass
class     a__ (      datasets.BuilderConfig   ):
     a	:    Optional[datasets.Features]	      =			None







class     a__ (      datasets.ArrowBasedBuilder   ):
     a	:    Optional[Any]	      =			ArrowConfig







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A				)						->					Optional[int]:



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







     def     lowerCAmelCase_       (			self						,	A				)						->					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
               a											=   table_cast(A						,	self.info.features.arrow_schema				)
          return pa_table







     def     lowerCAmelCase_       (			self						,	A				)						->					List[Any]:



          '''simple docstring'''



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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__   :    Union[str, Any] 					=      {"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    List[Any] 					=      [
       "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "VisualBertForMultipleChoice",
       "VisualBertForPreTraining",
       "VisualBertForQuestionAnswering",
       "VisualBertForRegionToPhraseAlignment",
       "VisualBertForVisualReasoning",
       "VisualBertLayer",
       "VisualBertModel",
       "VisualBertPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_visual_bert import (
          VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VisualBertForMultipleChoice,
          VisualBertForPreTraining,
          VisualBertForQuestionAnswering,
          VisualBertForRegionToPhraseAlignment,
          VisualBertForVisualReasoning,
          VisualBertLayer,
          VisualBertModel,
          VisualBertPreTrainedModel,
      )


else:
   import sys

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

