
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


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

_A						      =				["names", "prefix"]
_A						      =				["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
_A						      =				["encoding_errors", "on_bad_lines"]
_A						      =				["date_format"]







@dataclass
class   lowerCamelCase	(						datasets.BuilderConfig						):
     UpperCAmelCase__       :							str     =	","
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							Optional[Union[int, List[int], str]]     =	"infer"
     UpperCAmelCase__       :							Optional[List[str]]     =	None
     UpperCAmelCase__       :							Optional[List[str]]     =	None
     UpperCAmelCase__       :							Optional[Union[int, str, List[int], List[str]]]     =	None
     UpperCAmelCase__       :							Optional[Union[List[int], List[str]]]     =	None
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							bool     =	True
     UpperCAmelCase__       :							Optional[Literal["c", "python", "pyarrow"]]     =	None
     UpperCAmelCase__       :							Dict[Union[int, str], Callable[[Any], Any]]     =	None
     UpperCAmelCase__       :							Optional[list]     =	None
     UpperCAmelCase__       :							Optional[list]     =	None
     UpperCAmelCase__       :							bool     =	False
     UpperCAmelCase__       :							Optional[Union[int, List[int]]]     =	None
     UpperCAmelCase__       :							Optional[int]     =	None
     UpperCAmelCase__       :							Optional[Union[str, List[str]]]     =	None
     UpperCAmelCase__       :							bool     =	True
     UpperCAmelCase__       :							bool     =	True
     UpperCAmelCase__       :							bool     =	False
     UpperCAmelCase__       :							bool     =	True
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							str     =	"."
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							str     =	'"'
     UpperCAmelCase__       :							int     =	0
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							bool     =	True
     UpperCAmelCase__       :							bool     =	True
     UpperCAmelCase__       :							int     =	0
     UpperCAmelCase__       :							bool     =	True
     UpperCAmelCase__       :							bool     =	False
     UpperCAmelCase__       :							Optional[str]     =	None
     UpperCAmelCase__       :							int     =	1_00_00
     UpperCAmelCase__       :							Optional[datasets.Features]     =	None
     UpperCAmelCase__       :							Optional[str]     =	"strict"
     UpperCAmelCase__       :							Literal["error", "warn", "skip"]     =	"error"
     UpperCAmelCase__       :							Optional[str]     =	None





     def 	UpperCAmelCase(self   :					Dict     )		->		Optional[Any]:
         if self.delimiter is not None:
             snake_case				  =      self.delimiter
         if self.column_names is not None:
             snake_case				  =      self.column_names





     @property
     def 	UpperCAmelCase(self   :					Dict     )		->		Any:
         snake_case				  =      {
             "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   lowerCamelCase	(						datasets.ArrowBasedBuilder						):
     UpperCAmelCase__       :							str     =	CsvConfig





     def 	UpperCAmelCase(self   :					Dict     )		->		int:
         return datasets.DatasetInfo(features=self.config.features     )





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





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					pa.Table     )		->		pa.Table:
         if self.config.features is not None:
             snake_case				  =      self.config.features.arrow_schema
             if all(not require_storage_cast(_A     ) for feature in self.config.features.values()     ):
                 # cheaper cast
                 snake_case				  =      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				  =      table_cast(_A       ,					_A     )
         return pa_table





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					Union[str, Any]     )		->		Optional[int]:
         snake_case				  =      self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         snake_case				  =      (
             {
                 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				  =      pd.read_csv(_A       ,					iterator=_A       ,					dtype=_A       ,					**self.config.pd_read_csv_kwargs     )
             try:
                 for batch_idx, df in enumerate(_A     ):
                     snake_case				  =      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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Optional[int]     =	"roberta"





     def __init__(self   :					Union[str, Any]       ,					_A   :					List[Any]=5_0_2_6_5       ,					_A   :					Dict=7_6_8       ,					_A   :					Tuple=1_2       ,					_A   :					Optional[Any]=1_2       ,					_A   :					int=3_0_7_2       ,					_A   :					List[str]="gelu"       ,					_A   :					Tuple=0.1       ,					_A   :					Dict=0.1       ,					_A   :					Optional[int]=5_1_2       ,					_A   :					Dict=2       ,					_A   :					Optional[Any]=0.02       ,					_A   :					Optional[Any]=1E-12       ,					_A   :					str=1       ,					_A   :					Dict=0       ,					_A   :					Optional[int]=2       ,					_A   :					int="absolute"       ,					_A   :					Any=True       ,					_A   :					Union[str, Any]=None       ,					**_A   :					Optional[int]       ,					)		->		Tuple:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      vocab_size
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      hidden_act
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      max_position_embeddings
         snake_case				  =      type_vocab_size
         snake_case				  =      initializer_range
         snake_case				  =      layer_norm_eps
         snake_case				  =      position_embedding_type
         snake_case				  =      use_cache
         snake_case				  =      classifier_dropout







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             snake_case				  =      {0: "batch", 1: "choice", 2: "sequence"}
         else:
             snake_case				  =      {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]     )

