






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			:		Any   				=    datasets.utils.logging.get_logger(__name__)

_UpperCAmelCase			:		Tuple   				=    ["names", "prefix"]
_UpperCAmelCase			:		str   				=    ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
_UpperCAmelCase			:		List[Any]   				=    ["encoding_errors", "on_bad_lines"]
_UpperCAmelCase			:		List[Any]   				=    ["date_format"]
@dataclass
class  lowerCAmelCase_       (				datasets.BuilderConfig					):
							UpperCamelCase_		:Tuple		      =    ','
							UpperCamelCase_		:Optional[Any]		      =    None
							UpperCamelCase_		:List[Any]		      =    'infer'
							UpperCamelCase_		:List[str]		      =    None
							UpperCamelCase_		:int		      =    None
							UpperCamelCase_		:str		      =    None
							UpperCamelCase_		:List[str]		      =    None
							UpperCamelCase_		:Union[str, Any]		      =    None
							UpperCamelCase_		:str		      =    True
							UpperCamelCase_		:int		      =    None
							UpperCamelCase_		:str		      =    None
							UpperCamelCase_		:List[Any]		      =    None
							UpperCamelCase_		:List[Any]		      =    None
							UpperCamelCase_		:Dict		      =    False
							UpperCamelCase_		:Union[str, Any]		      =    None
							UpperCamelCase_		:Dict		      =    None
							UpperCamelCase_		:int		      =    None
							UpperCamelCase_		:List[str]		      =    True
							UpperCamelCase_		:List[str]		      =    True
							UpperCamelCase_		:Optional[Any]		      =    False
							UpperCamelCase_		:int		      =    True
							UpperCamelCase_		:Optional[int]		      =    None
							UpperCamelCase_		:str		      =    '.'
							UpperCamelCase_		:Optional[int]		      =    None
							UpperCamelCase_		:Any		      =    '"'
							UpperCamelCase_		:Optional[Any]		      =    0
							UpperCamelCase_		:Optional[int]		      =    None
							UpperCamelCase_		:List[Any]		      =    None
							UpperCamelCase_		:int		      =    None
							UpperCamelCase_		:Tuple		      =    None
							UpperCamelCase_		:str		      =    True
							UpperCamelCase_		:Union[str, Any]		      =    True
							UpperCamelCase_		:Tuple		      =    0
							UpperCamelCase_		:str		      =    True
							UpperCamelCase_		:str		      =    False
							UpperCamelCase_		:Tuple		      =    None
							UpperCamelCase_		:Dict		      =    1_0000
							UpperCamelCase_		:Union[str, Any]		      =    None
							UpperCamelCase_		:int		      =    'strict'
							UpperCamelCase_		:Tuple		      =    'error'
							UpperCamelCase_		:str		      =    None




							def 						__snake_case					(   self     :   Optional[int]	):
											if self.delimiter is not None:
															lowerCAmelCase__      			=						self.delimiter
											if self.column_names is not None:
															lowerCAmelCase__      			=						self.column_names




							@property
							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						{
											    "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() ,							SCREAMING_SNAKE_CASE_	):
																			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					):
							UpperCamelCase_		:int		      =    CsvConfig




							def 						__snake_case					(   self     :   Union[str, Any]	):
											return datasets.DatasetInfo(features=self.config.features	)




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   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}'	)
											lowerCAmelCase__      			=						dl_manager.download_and_extract(self.config.data_files	)
											if isinstance(SCREAMING_SNAKE_CASE_ ,							(str, list, tuple)	):
															lowerCAmelCase__      			=						data_files
															if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																			lowerCAmelCase__      			=						[files]
															lowerCAmelCase__      			=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE_	) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,							gen_kwargs={'''files''': files}	)]
											lowerCAmelCase__      			=						[]
											for split_name, files in data_files.items():
															if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																			lowerCAmelCase__      			=						[files]
															lowerCAmelCase__      			=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE_	) for file in files]
															splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_ ,							gen_kwargs={'''files''': files}	)	)
											return splits




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




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













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		Optional[Any]   				=    {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ErnieForCausalLM",
			    "ErnieForMaskedLM",
			    "ErnieForMultipleChoice",
			    "ErnieForNextSentencePrediction",
			    "ErnieForPreTraining",
			    "ErnieForQuestionAnswering",
			    "ErnieForSequenceClassification",
			    "ErnieForTokenClassification",
			    "ErnieModel",
			    "ErniePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ernie import (
						    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ErnieForCausalLM,
						    ErnieForMaskedLM,
						    ErnieForMultipleChoice,
						    ErnieForNextSentencePrediction,
						    ErnieForPreTraining,
						    ErnieForQuestionAnswering,
						    ErnieForSequenceClassification,
						    ErnieForTokenClassification,
						    ErnieModel,
						    ErniePreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





