





import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

_SCREAMING_SNAKE_CASE 		=						["""names""", """prefix"""]
_SCREAMING_SNAKE_CASE 		=						["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
_SCREAMING_SNAKE_CASE 		=						["""encoding_errors""", """on_bad_lines"""]
_SCREAMING_SNAKE_CASE 		=						["""date_format"""]
@dataclass
class SCREAMING_SNAKE_CASE_  (   datasets.BuilderConfig   ):
						__magic_name__:     str	 =    ","
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     Optional[Union[int, List[int], str]]	 =    "infer"
						__magic_name__:     Optional[List[str]]	 =    None
						__magic_name__:     Optional[List[str]]	 =    None
						__magic_name__:     Optional[Union[int, str, List[int], List[str]]]	 =    None
						__magic_name__:     Optional[Union[List[int], List[str]]]	 =    None
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     bool	 =    True
						__magic_name__:     Optional[Literal["c", "python", "pyarrow"]]	 =    None
						__magic_name__:     Dict[Union[int, str], Callable[[Any], Any]]	 =    None
						__magic_name__:     Optional[list]	 =    None
						__magic_name__:     Optional[list]	 =    None
						__magic_name__:     bool	 =    False
						__magic_name__:     Optional[Union[int, List[int]]]	 =    None
						__magic_name__:     Optional[int]	 =    None
						__magic_name__:     Optional[Union[str, List[str]]]	 =    None
						__magic_name__:     bool	 =    True
						__magic_name__:     bool	 =    True
						__magic_name__:     bool	 =    False
						__magic_name__:     bool	 =    True
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     str	 =    "."
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     str	 =    '"'
						__magic_name__:     int	 =    0
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     bool	 =    True
						__magic_name__:     bool	 =    True
						__magic_name__:     int	 =    0
						__magic_name__:     bool	 =    True
						__magic_name__:     bool	 =    False
						__magic_name__:     Optional[str]	 =    None
						__magic_name__:     int	 =    10000
						__magic_name__:     Optional[datasets.Features]	 =    None
						__magic_name__:     Optional[str]	 =    "strict"
						__magic_name__:     Literal["error", "warn", "skip"]	 =    "error"
						__magic_name__:     Optional[str]	 =    None






						def UpperCAmelCase_			( self : str		)							->      Any:







							"""simple docstring"""


							if self.delimiter is not None:
								snake_case_						:     int     =		self.delimiter
							if self.column_names is not None:
								snake_case_						:     List[str]     =		self.column_names






						@property
						def UpperCAmelCase_			( self : int		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Any     =		{
							    'sep': self.sep,
							    'header': self.header,
							    'names': self.names,
							    'index_col': self.index_col,
							    'usecols': self.usecols,
							    'prefix': self.prefix,
							    'mangle_dupe_cols': self.mangle_dupe_cols,
							    'engine': self.engine,
							    'converters': self.converters,
							    'true_values': self.true_values,
							    'false_values': self.false_values,
							    'skipinitialspace': self.skipinitialspace,
							    'skiprows': self.skiprows,
							    'nrows': self.nrows,
							    'na_values': self.na_values,
							    'keep_default_na': self.keep_default_na,
							    'na_filter': self.na_filter,
							    'verbose': self.verbose,
							    'skip_blank_lines': self.skip_blank_lines,
							    'thousands': self.thousands,
							    'decimal': self.decimal,
							    'lineterminator': self.lineterminator,
							    'quotechar': self.quotechar,
							    'quoting': self.quoting,
							    'escapechar': self.escapechar,
							    'comment': self.comment,
							    'encoding': self.encoding,
							    'dialect': self.dialect,
							    'error_bad_lines': self.error_bad_lines,
							    'warn_bad_lines': self.warn_bad_lines,
							    'skipfooter': self.skipfooter,
							    'doublequote': self.doublequote,
							    'memory_map': self.memory_map,
							    'float_precision': self.float_precision,
							    'chunksize': self.chunksize,
							    'encoding_errors': self.encoding_errors,
							    'on_bad_lines': self.on_bad_lines,
							    'date_format': self.date_format,
							}

							# some kwargs must not be passed if they don't have a default value
							# some others are deprecated and we can also not pass them if they are the default value
							for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
								if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,				_A		):
									del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 2):
								for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
									del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
							if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
								for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
									del pd_read_csv_kwargs[pd_read_csv_parameter]

							return pd_read_csv_kwargs
class SCREAMING_SNAKE_CASE_  (   datasets.ArrowBasedBuilder   ):
						__magic_name__:     Tuple	 =    CsvConfig






						def UpperCAmelCase_			( self : Optional[int]		)							->      str:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : int						,				_A : List[Any]		)							->      str:







							"""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_						:     Union[str, Any]     =		dl_manager.download_and_extract(self.config.data_files		)
							if isinstance(_A						,				(str, list, tuple)		):
								snake_case_						:     Tuple     =		data_files
								if isinstance(_A						,				_A		):
									snake_case_						:     int     =		[files]
								snake_case_						:     Union[str, Any]     =		[dl_manager.iter_files(_A		) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,				gen_kwargs={'files': files}		)]
							snake_case_						:     List[str]     =		[]
							for split_name, files in data_files.items():
								if isinstance(_A						,				_A		):
									snake_case_						:     Optional[Any]     =		[files]
								snake_case_						:     int     =		[dl_manager.iter_files(_A		) for file in files]
								splits.append(datasets.SplitGenerator(name=_A						,				gen_kwargs={'files': files}		)		)
							return splits






						def UpperCAmelCase_			( self : str						,				_A : pa.Table		)							->      pa.Table:







							"""simple docstring"""


							if self.config.features is not None:
								snake_case_						:     List[str]     =		self.config.features.arrow_schema
								if all(not require_storage_cast(_A		) for feature in self.config.features.values()		):
									# cheaper cast
									snake_case_						:     List[Any]     =		pa.Table.from_arrays([pa_table[field.name] for field in schema]						,				schema=_A		)
								else:
									# more expensive cast; allows str <-> int/float or str to Audio for example
									snake_case_						:     str     =		table_cast(_A						,				_A		)
							return pa_table






						def UpperCAmelCase_			( self : str						,				_A : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							snake_case_						:     Any     =		(
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(_A		) else object
							        for name, dtype, feature in zip(schema.names						,				schema.types						,				self.config.features.values()		)
							    }
							    if schema is not None
							    else None
							)
							for file_idx, file in enumerate(itertools.chain.from_iterable(_A		)		):
								snake_case_						:     List[str]     =		pd.read_csv(_A						,				iterator=_A						,				dtype=_A						,				**self.config.pd_read_csv_kwargs		)
								try:
									for batch_idx, df in enumerate(_A		):
										snake_case_						:     str     =		pa.Table.from_pandas(_A		)
										# 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 (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


_SCREAMING_SNAKE_CASE 		=						{
    """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:
						_SCREAMING_SNAKE_CASE 		=						[
						    """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

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

