



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


__lowerCamelCase     :							List[Any]  							=				datasets.utils.logging.get_logger(__name__)

__lowerCamelCase     :							Optional[Any]  							=				["names", "prefix"]
__lowerCamelCase     :							str  							=				["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
__lowerCamelCase     :							Union[str, Any]  							=				["encoding_errors", "on_bad_lines"]
__lowerCamelCase     :							Any  							=				["date_format"]





@dataclass
class  __magic_name__   (							datasets.BuilderConfig						):
		lowercase      :       str          =","
		lowercase      :       Optional[str]          =None
		lowercase      :       Optional[Union[int, List[int], str]]          ="infer"
		lowercase      :       Optional[List[str]]          =None
		lowercase      :       Optional[List[str]]          =None
		lowercase      :       Optional[Union[int, str, List[int], List[str]]]          =None
		lowercase      :       Optional[Union[List[int], List[str]]]          =None
		lowercase      :       Optional[str]          =None
		lowercase      :       bool          =True
		lowercase      :       Optional[Literal["c", "python", "pyarrow"]]          =None
		lowercase      :       Dict[Union[int, str], Callable[[Any], Any]]          =None
		lowercase      :       Optional[list]          =None
		lowercase      :       Optional[list]          =None
		lowercase      :       bool          =False
		lowercase      :       Optional[Union[int, List[int]]]          =None
		lowercase      :       Optional[int]          =None
		lowercase      :       Optional[Union[str, List[str]]]          =None
		lowercase      :       bool          =True
		lowercase      :       bool          =True
		lowercase      :       bool          =False
		lowercase      :       bool          =True
		lowercase      :       Optional[str]          =None
		lowercase      :       str          ="."
		lowercase      :       Optional[str]          =None
		lowercase      :       str          ='"'
		lowercase      :       int          =0
		lowercase      :       Optional[str]          =None
		lowercase      :       Optional[str]          =None
		lowercase      :       Optional[str]          =None
		lowercase      :       Optional[str]          =None
		lowercase      :       bool          =True
		lowercase      :       bool          =True
		lowercase      :       int          =0
		lowercase      :       bool          =True
		lowercase      :       bool          =False
		lowercase      :       Optional[str]          =None
		lowercase      :       int          =1_0000
		lowercase      :       Optional[datasets.Features]          =None
		lowercase      :       Optional[str]          ="strict"
		lowercase      :       Literal["error", "warn", "skip"]          ="error"
		lowercase      :       Optional[str]          =None






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Any:
								'''simple docstring'''







								if self.delimiter is not None:
														UpperCAmelCase         =     self.delimiter
								if self.column_names is not None:
														UpperCAmelCase         =     self.column_names







		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     {
								    "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  __magic_name__   (							datasets.ArrowBasedBuilder						):
		lowercase      :       str          =CsvConfig






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







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






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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     pa.Table      )				->					pa.Table:
								'''simple docstring'''







								if self.config.features is not None:
														UpperCAmelCase         =     self.config.features.arrow_schema
														if all(not require_storage_cast(UpperCamelCase__      ) for feature in self.config.features.values()      ):
																				# cheaper cast
																				UpperCAmelCase         =     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         =     table_cast(UpperCamelCase__				,		UpperCamelCase__      )
								return pa_table







		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.config.features.arrow_schema if self.config.features else None
								# dtype allows reading an int column as str
								UpperCAmelCase         =     (
								    {
								        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         =     pd.read_csv(UpperCamelCase__				,		iterator=UpperCamelCase__				,		dtype=UpperCamelCase__				,		**self.config.pd_read_csv_kwargs      )
														try:
																				for batch_idx, df in enumerate(UpperCamelCase__      ):
																										UpperCAmelCase         =     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_torch_available,
)


__lowerCamelCase     :							Any  							=				{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							Optional[int]  							=				[
		    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MegaForCausalLM",
		    "MegaForMaskedLM",
		    "MegaForMultipleChoice",
		    "MegaForQuestionAnswering",
		    "MegaForSequenceClassification",
		    "MegaForTokenClassification",
		    "MegaModel",
		    "MegaPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

		__lowerCamelCase     :							int  							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



