






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


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

__UpperCAmelCase     							=						['names', 'prefix']
__UpperCAmelCase     							=						['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__UpperCAmelCase     							=						['encoding_errors', 'on_bad_lines']
__UpperCAmelCase     							=						['date_format']



@dataclass
class   lowerCamelCase				(datasets.BuilderConfig      ):







		'''simple docstring'''



		_snake_case   :   str							=  ","
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   Optional[Union[int, List[int], str]]							=  "infer"
		_snake_case   :   Optional[List[str]]							=  None
		_snake_case   :   Optional[List[str]]							=  None
		_snake_case   :   Optional[Union[int, str, List[int], List[str]]]							=  None
		_snake_case   :   Optional[Union[List[int], List[str]]]							=  None
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   bool							=  True
		_snake_case   :   Optional[Literal["c", "python", "pyarrow"]]							=  None
		_snake_case   :   Dict[Union[int, str], Callable[[Any], Any]]							=  None
		_snake_case   :   Optional[list]							=  None
		_snake_case   :   Optional[list]							=  None
		_snake_case   :   bool							=  False
		_snake_case   :   Optional[Union[int, List[int]]]							=  None
		_snake_case   :   Optional[int]							=  None
		_snake_case   :   Optional[Union[str, List[str]]]							=  None
		_snake_case   :   bool							=  True
		_snake_case   :   bool							=  True
		_snake_case   :   bool							=  False
		_snake_case   :   bool							=  True
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   str							=  "."
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   str							=  '"'
		_snake_case   :   int							=  0
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   bool							=  True
		_snake_case   :   bool							=  True
		_snake_case   :   int							=  0
		_snake_case   :   bool							=  True
		_snake_case   :   bool							=  False
		_snake_case   :   Optional[str]							=  None
		_snake_case   :   int							=  1_0_0_0_0
		_snake_case   :   Optional[datasets.Features]							=  None
		_snake_case   :   Optional[str]							=  "strict"
		_snake_case   :   Literal["error", "warn", "skip"]							=  "error"
		_snake_case   :   Optional[str]							=  None



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							if self.delimiter is not None:
												UpperCAmelCase_   :				str						=						self.delimiter
							if self.column_names is not None:
												UpperCAmelCase_   :				List[Any]						=						self.column_names



		@property
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				int						=						{
							    '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()    ,		_UpperCamelCase       ):
																	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   lowerCamelCase				(datasets.ArrowBasedBuilder      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  CsvConfig



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							return datasets.DatasetInfo(features=self.config.features       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Union[str, Any]:
							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}"       )
							UpperCAmelCase_   :				Optional[Any]						=						dl_manager.download_and_extract(self.config.data_files       )
							if isinstance(_UpperCamelCase    ,		(str, list, tuple)       ):
												UpperCAmelCase_   :				int						=						data_files
												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				Union[str, Any]						=						[files]
												UpperCAmelCase_   :				Union[str, Any]						=						[dl_manager.iter_files(_UpperCamelCase       ) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,		gen_kwargs={'files': files}       )]
							UpperCAmelCase_   :				Dict						=						[]
							for split_name, files in data_files.items():
												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				str						=						[files]
												UpperCAmelCase_   :				Optional[int]						=						[dl_manager.iter_files(_UpperCamelCase       ) for file in files]
												splits.append(datasets.SplitGenerator(name=_UpperCamelCase    ,		gen_kwargs={'files': files}       )       )
							return splits



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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Optional[int]						=						self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							UpperCAmelCase_   :				str						=						(
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(_UpperCamelCase       ) 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(_UpperCamelCase       )       ):
												UpperCAmelCase_   :				Tuple						=						pd.read_csv(_UpperCamelCase    ,		iterator=_UpperCamelCase    ,		dtype=_UpperCamelCase    ,		**self.config.pd_read_csv_kwargs       )
												try:
																	for batch_idx, df in enumerate(_UpperCamelCase       ):
																						UpperCAmelCase_   :				Optional[int]						=						pa.Table.from_pandas(_UpperCamelCase       )
																						# 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(_UpperCamelCase       )
												except ValueError as e:
																	logger.error(f"Failed to read file '{file}' with error {type(_UpperCamelCase       )}: {e}"       )
																	raise














from typing import TYPE_CHECKING

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


__UpperCAmelCase     							=						{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__UpperCAmelCase     							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






