



"""simple docstring"""
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


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

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




@dataclass
class 			snake_case_						(						datasets.BuilderConfig				):
							__lowerCAmelCase    					=					","
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					"infer"
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					"."
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					'"'
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					1_0_0_0_0
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					"strict"
							__lowerCAmelCase    					=					"error"
							__lowerCAmelCase    					=					None






							def snake_case_  (   self      ):
									if self.delimiter is not None:
											a_     :      Dict 						=     self.delimiter
									if self.column_names is not None:
											a_     :      int 						=     self.column_names






							@property
							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     {
									    "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 			snake_case_						(						datasets.ArrowBasedBuilder				):
							__lowerCAmelCase    					=					CsvConfig






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






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






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






							def snake_case_  (   self			,     a_      ):
									a_     :      str 						=     self.config.features.arrow_schema if self.config.features else None
									# dtype allows reading an int column as str
									a_     :      Optional[Any] 						=     (
									    {
									        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_      )      ):
											a_     :      Union[str, Any] 						=     pd.read_csv(a_			,     iterator=a_			,     dtype=a_			,     **self.config.pd_read_csv_kwargs      )
											try:
													for batch_idx, df in enumerate(a_      ):
															a_     :      Tuple 						=     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




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"realm"






							def __init__(   self			,     a_=3_0_5_2_2			,     a_=7_6_8			,     a_=1_2_8			,     a_=1_2			,     a_=1_2			,     a_=8			,     a_=3_0_7_2			,     a_="gelu_new"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=2			,     a_=0.02			,     a_=1e-12			,     a_=2_5_6			,     a_=1_0			,     a_=1e-3			,     a_=5			,     a_=3_2_0			,     a_=1_3_3_5_3_7_1_8			,     a_=5_0_0_0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

									# Common config
									a_     :      Optional[int] 						=     vocab_size
									a_     :      List[Any] 						=     max_position_embeddings
									a_     :      Optional[Any] 						=     hidden_size
									a_     :      Optional[Any] 						=     retriever_proj_size
									a_     :      List[str] 						=     num_hidden_layers
									a_     :      List[Any] 						=     num_attention_heads
									a_     :      Tuple 						=     num_candidates
									a_     :      str 						=     intermediate_size
									a_     :      Optional[int] 						=     hidden_act
									a_     :      List[str] 						=     hidden_dropout_prob
									a_     :      List[str] 						=     attention_probs_dropout_prob
									a_     :      Tuple 						=     initializer_range
									a_     :      Tuple 						=     type_vocab_size
									a_     :      str 						=     layer_norm_eps

									# Reader config
									a_     :      str 						=     span_hidden_size
									a_     :      Union[str, Any] 						=     max_span_width
									a_     :      Tuple 						=     reader_layer_norm_eps
									a_     :      List[Any] 						=     reader_beam_size
									a_     :      str 						=     reader_seq_len

									# Retrieval config
									a_     :      str 						=     num_block_records
									a_     :      int 						=     searcher_beam_size