


'''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


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





@dataclass
class 						__SCREAMING_SNAKE_CASE					(      datasets.BuilderConfig ):
     UpperCAmelCase 					=				10_000
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None







class 						__SCREAMING_SNAKE_CASE					(      datasets.ArrowBasedBuilder ):
     UpperCAmelCase 					=				ParquetConfig






     def  a_					(			self		)       ->    Optional[Any]:
          return datasets.DatasetInfo(features=self.config.features		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          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(__UpperCamelCase     ,							(str, list, tuple)		):
               _a						=		data_files
               if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
                    _a						=		[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               _a						=		[dl_manager.iter_files(__UpperCamelCase		) 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(__UpperCamelCase     ,							__UpperCamelCase		):
                    _a						=		[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               _a						=		[dl_manager.iter_files(__UpperCamelCase		) 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(__UpperCamelCase		):
                         with open(__UpperCamelCase     ,							"rb"		) as f:
                              _a						=		datasets.Features.from_arrow_schema(pq.read_schema(__UpperCamelCase		)		)
                         break
               splits.append(datasets.SplitGenerator(name=__UpperCamelCase     ,							gen_kwargs={"files": files}		)		)
          return splits






     def  a_					(			self     ,							__UpperCamelCase		)       ->    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(__UpperCamelCase     ,							self.info.features.arrow_schema		)
          return pa_table






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _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(__UpperCamelCase		)		):
               with open(__UpperCamelCase     ,							"rb"		) as f:
                    _a						=		pq.ParquetFile(__UpperCamelCase		)
                    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(__UpperCamelCase		)
                    except ValueError as e:
                         logger.error(f"Failed to read file '{file}' with error {type(__UpperCamelCase		)}: {e}"		)
                         raise




'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__				     =		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				     =		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   lowercase__				     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

