
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


lowercase__		:List[str]   =    datasets.utils.logging.get_logger(__name__)

lowercase__		:Tuple   =    ["names", "prefix"]
lowercase__		:List[Any]   =    ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
lowercase__		:Any   =    ["encoding_errors", "on_bad_lines"]
lowercase__		:Optional[int]   =    ["date_format"]







@dataclass
class 				lowercase		(					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       					=10000
							lowercase_       :       Optional[datasets.Features]       					=None
							lowercase_       :       Optional[str]       					="strict"
							lowercase_       :       Literal["error", "warn", "skip"]       					="error"
							lowercase_       :       Optional[str]       					=None




							def  A__      (				self):
												if self.delimiter is not None:
																	lowercase							=						self.delimiter
												if self.column_names is not None:
																	lowercase							=						self.column_names





							@property
							def  A__      (				self):
												lowercase							=						{
												    '''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 				lowercase		(					datasets.ArrowBasedBuilder		):
							lowercase_       :       Optional[int]       					=CsvConfig




							def  A__      (				self):
												return datasets.DatasetInfo(features=self.config.features)




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




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





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


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






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


