




"""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


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       lowercase(  datasets.BuilderConfig				):







 '''simple docstring'''





 lowercase__				      =      ","
 lowercase__				      =      None
 lowercase__				      =      "infer"
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      True
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      False
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      True
 lowercase__				      =      True
 lowercase__				      =      False
 lowercase__				      =      True
 lowercase__				      =      None
 lowercase__				      =      "."
 lowercase__				      =      None
 lowercase__				      =      '"'
 lowercase__				      =      0
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      None
 lowercase__				      =      True
 lowercase__				      =      True
 lowercase__				      =      0
 lowercase__				      =      True
 lowercase__				      =      False
 lowercase__				      =      None
 lowercase__				      =      1_00_00
 lowercase__				      =      None
 lowercase__				      =      "strict"
 lowercase__				      =      "error"
 lowercase__				      =      None


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   if self.delimiter is not None:
     _snake_case							:	int			=						self.delimiter
   if self.column_names is not None:
     _snake_case							:	Dict			=						self.column_names


 @property
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	List[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       lowercase(  datasets.ArrowBasedBuilder				):







 '''simple docstring'''





 lowercase__				      =      CsvConfig


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[int]	):




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


 def 				UpperCamelCase_     (       self:	List[str],   a_:	pa.Table	):




   '''simple docstring'''



   if self.config.features is not None:
     _snake_case							:	Dict			=						self.config.features.arrow_schema
     if all(not require_storage_cast(a_	) for feature in self.config.features.values()	):
       # cheaper cast
       _snake_case							:	Dict			=						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							:	Optional[int]			=						table_cast(a_,   a_	)
   return pa_table


 def 				UpperCamelCase_     (       self:	str,   a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.config.features.arrow_schema if self.config.features else None
   # dtype allows reading an int column as str
   _snake_case							:	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_	)	):
     _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_	):
         _snake_case							:	Optional[int]			=						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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A_      			=       {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       ['''OwlViTFeatureExtractor''']
     A_      			=       ['''OwlViTImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       [
         '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''OwlViTModel''',
         '''OwlViTPreTrainedModel''',
         '''OwlViTTextModel''',
         '''OwlViTVisionModel''',
         '''OwlViTForObjectDetection''',
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

     A_      			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


