





"""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


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

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



@dataclass
class      __SCREAMING_SNAKE_CASE    (						datasets.BuilderConfig   ):







  '''simple docstring'''


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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				List[str]:
        if self.delimiter is not None:
              _UpperCamelCase      :	str						=       self.delimiter
        if self.column_names is not None:
              _UpperCamelCase      :	List[str]						=       self.column_names


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Any:
        _UpperCamelCase      :	Tuple						=       {
            "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      __SCREAMING_SNAKE_CASE    (						datasets.ArrowBasedBuilder   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					CsvConfig

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Any:
        return datasets.DatasetInfo(features=self.config.features	)

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       pa.Table	)		->				pa.Table:
        if self.config.features is not None:
              _UpperCamelCase      :	Optional[Any]						=       self.config.features.arrow_schema
              if all(not require_storage_cast(__a	) for feature in self.config.features.values()	):
                    # cheaper cast
                    _UpperCamelCase      :	Optional[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
                    _UpperCamelCase      :	str						=       table_cast(__a					,   __a	)
        return pa_table


  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       Dict	)		->				str:
        _UpperCamelCase      :	List[str]						=       self.config.features.arrow_schema if self.config.features else None
        # dtype allows reading an int column as str
        _UpperCamelCase      :	Optional[int]						=       (
            {
                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	)	):
              _UpperCamelCase      :	str						=       pd.read_csv(__a					,   iterator=__a					,   dtype=__a					,   **self.config.pd_read_csv_kwargs	)
              try:
                    for batch_idx, df in enumerate(__a	):
                          _UpperCamelCase      :	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,
)


lowerCamelCase__				  =   {
    "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:
  lowerCamelCase__				  =   ["OwlViTFeatureExtractor"]
  lowerCamelCase__				  =   ["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "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

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
