"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__  :  str						     =	{
    '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:
						lowerCAmelCase__  :  Any						     =	[
						    '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

						lowerCAmelCase__  :  Any						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast




@dataclass
class       __lowerCAmelCase				(							datasets.BuilderConfig):
       _a           =  None







class       __lowerCAmelCase				(							datasets.ArrowBasedBuilder):
       _a           =  PandasConfig






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






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    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}" )
         lowercase			:int							=  dl_manager.download_and_extract(self.config.data_files )
         if isinstance(_lowerCAmelCase				,    (str, list, tuple) ):
           lowercase			:Union[str, Any]							=  data_files
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:str							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[int]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"files": files} )]
         lowercase			:List[Any]							=  []
         for split_name, files in data_files.items():
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:Optional[Any]							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[Any]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           splits.append(datasets.SplitGenerator(name=_lowerCAmelCase				,    gen_kwargs={"files": files} ) )
         return splits






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    pa.Table ):
         if self.config.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
           lowercase			:Any							=  table_cast(_lowerCAmelCase				,    self.config.features.arrow_schema )
         return pa_table






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Union[str, Any] ):
         for i, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase ) ):
           with open(_lowerCAmelCase				,    "rb" ) as f:
             lowercase			:int							=  pa.Table.from_pandas(pd.read_pickle(_lowerCAmelCase ) )
             yield i, self._cast_table(_lowerCAmelCase )



