


'''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


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

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



@dataclass
class 	__lowerCAmelCase		(							datasets.BuilderConfig    ):


  '''simple docstring'''







  a_  					=							""","""
  a_  					=							None
  a_  					=							"""infer"""
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							True
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							False
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							True
  a_  					=							True
  a_  					=							False
  a_  					=							True
  a_  					=							None
  a_  					=							"""."""
  a_  					=							None
  a_  					=							"""\""""
  a_  					=							0
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							None
  a_  					=							True
  a_  					=							True
  a_  					=							0
  a_  					=							True
  a_  					=							False
  a_  					=							None
  a_  					=							10_000
  a_  					=							None
  a_  					=							"""strict"""
  a_  					=							"""error"""
  a_  					=							None






  def        _a							(							self :       Dict ):

        '''simple docstring'''




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






  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  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 	__lowerCAmelCase		(							datasets.ArrowBasedBuilder    ):


  '''simple docstring'''







  a_  					=							CsvConfig






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




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






  def        _a							(							self :       Optional[int]	,_a :       Dict ):

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






  def        _a							(							self :       List[Any]	,_a :       str ):

        '''simple docstring'''




        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_						:  List[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
                    A_						:  Optional[int]          =				table_cast(_a	,_a )
        return pa_table






  def        _a							(							self :       List[str]	,_a :       List[str] ):

        '''simple docstring'''




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


import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       Union[str, Any]	,*_a :       Optional[Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        warnings.warn(
            """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
            """ use OwlViTImageProcessor instead."""	,_a	,)
        super().__init__(*_a	,**_a )
