




'''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


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

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






@dataclass
class 							_a				(			datasets.BuilderConfig ):





       '''simple docstring'''






       A						:				str        =						","
       A						:				Optional[str]        =						None
       A						:				Optional[Union[int, List[int], str]]        =						"infer"
       A						:				Optional[List[str]]        =						None
       A						:				Optional[List[str]]        =						None
       A						:				Optional[Union[int, str, List[int], List[str]]]        =						None
       A						:				Optional[Union[List[int], List[str]]]        =						None
       A						:				Optional[str]        =						None
       A						:				bool        =						True
       A						:				Optional[Literal["c", "python", "pyarrow"]]        =						None
       A						:				Dict[Union[int, str], Callable[[Any], Any]]        =						None
       A						:				Optional[list]        =						None
       A						:				Optional[list]        =						None
       A						:				bool        =						False
       A						:				Optional[Union[int, List[int]]]        =						None
       A						:				Optional[int]        =						None
       A						:				Optional[Union[str, List[str]]]        =						None
       A						:				bool        =						True
       A						:				bool        =						True
       A						:				bool        =						False
       A						:				bool        =						True
       A						:				Optional[str]        =						None
       A						:				str        =						"."
       A						:				Optional[str]        =						None
       A						:				str        =						'"'
       A						:				int        =						0
       A						:				Optional[str]        =						None
       A						:				Optional[str]        =						None
       A						:				Optional[str]        =						None
       A						:				Optional[str]        =						None
       A						:				bool        =						True
       A						:				bool        =						True
       A						:				int        =						0
       A						:				bool        =						True
       A						:				bool        =						False
       A						:				Optional[str]        =						None
       A						:				int        =						10_000
       A						:				Optional[datasets.Features]        =						None
       A						:				Optional[str]        =						"strict"
       A						:				Literal["error", "warn", "skip"]        =						"error"
       A						:				Optional[str]        =						None







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if self.delimiter is not None:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						self.delimiter
             if self.column_names is not None:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self.column_names







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						{
                 '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 							_a				(			datasets.ArrowBasedBuilder ):





       '''simple docstring'''






       A						:				Tuple        =						CsvConfig







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.config.features is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.config.features.arrow_schema
                   if all(not require_storage_cast(A  ) for feature in self.config.features.values()  ):
                         # cheaper cast
                         SCREAMING_SNAKE_CASE   :      Any    					=						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
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						table_cast(A,							A  )
             return pa_table







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.config.features.arrow_schema if self.config.features else None
             # dtype allows reading an int column as str
             SCREAMING_SNAKE_CASE   :      List[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  )  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						pd.read_csv(A,							iterator=A,							dtype=A,							**self.config.pd_read_csv_kwargs  )
                   try:
                         for batch_idx, df in enumerate(A  ):
                               SCREAMING_SNAKE_CASE   :      Dict    					=						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 typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )


