

'''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


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

_snake_case         =  ['names', 'prefix']
_snake_case         =  ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
_snake_case         =  ['encoding_errors', 'on_bad_lines']
_snake_case         =  ['date_format']


@dataclass
class        a__  (							datasets.BuilderConfig  ):
   _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     	=							1_0000
   _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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self.delimiter is not None:
         _lowercase      :				Union[str, Any]      					=       self.delimiter
      if self.column_names is not None:
         _lowercase      :				List[str]      					=       self.column_names






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, 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()      ,    _UpperCamelCase  ):
            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  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							CsvConfig


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if self.config.features is not None:
         _lowercase      :				List[str]      					=       self.config.features.arrow_schema
         if all(not require_storage_cast(_UpperCamelCase  ) for feature in self.config.features.values()  ):
            # cheaper cast
            _lowercase      :				Dict      					=       pa.Table.from_arrays([pa_table[field.name] for field in schema]      ,    schema=_UpperCamelCase  )
         else:
            # more expensive cast; allows str <-> int/float or str to Audio for example
            _lowercase      :				Optional[int]      					=       table_cast(_UpperCamelCase      ,    _UpperCamelCase  )
      return pa_table






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.config.features.arrow_schema if self.config.features else None
      # dtype allows reading an int column as str
      _lowercase      :				str      					=       (
          {
              name: dtype.to_pandas_dtype() if not require_storage_cast(_UpperCamelCase  ) 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(_UpperCamelCase  )  ):
         _lowercase      :				Any      					=       pd.read_csv(_UpperCamelCase      ,    iterator=_UpperCamelCase      ,    dtype=_UpperCamelCase      ,    **self.config.pd_read_csv_kwargs  )
         try:
            for batch_idx, df in enumerate(_UpperCamelCase  ):
               _lowercase      :				Tuple      					=       pa.Table.from_pandas(_UpperCamelCase  )
               # 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(_UpperCamelCase  )
         except ValueError as e:
            logger.error(f'''Failed to read file \'{file}\' with error {type(_UpperCamelCase  )}: {e}'''  )
            raise









'''simple docstring'''
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					str     	=							'OwlViTImageProcessor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							('CLIPTokenizer', 'CLIPTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,    _UpperCamelCase      ,    )
         _lowercase      :				Optional[int]      					=       kwargs.pop("feature_extractor"  )

      _lowercase      :				str      					=       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."  )
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."  )

      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase="max_length"      ,    _UpperCamelCase="np"      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if text is None and query_images is None and images is None:
         raise ValueError(
             "You have to specify at least one text or query image or image. All three cannot be none."  )

      if text is not None:
         if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) or (isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and not isinstance(text[0]      ,    _UpperCamelCase  )):
            _lowercase      :				int      					=       [self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )]

         elif isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and isinstance(text[0]      ,    _UpperCamelCase  ):
            _lowercase      :				str      					=       []

            # Maximum number of queries across batch
            _lowercase      :				str      					=       max([len(_UpperCamelCase  ) for t in text]  )

            # Pad all batch samples to max number of text queries
            for t in text:
               if len(_UpperCamelCase  ) != max_num_queries:
                  _lowercase      :				List[Any]      					=       t + [" "] * (max_num_queries - len(_UpperCamelCase  ))

               _lowercase      :				Tuple      					=       self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )
               encodings.append(_UpperCamelCase  )
         else:
            raise TypeError("Input text should be a string, a list of strings or a nested list of strings"  )

         if return_tensors == "np":
            _lowercase      :				List[Any]      					=       np.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				Optional[Any]      					=       np.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "jax" and is_flax_available():
            import jax.numpy as jnp

            _lowercase      :				Union[str, Any]      					=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				int      					=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "pt" and is_torch_available():
            import torch

            _lowercase      :				int      					=       torch.cat([encoding["input_ids"] for encoding in encodings]      ,    dim=0  )
            _lowercase      :				Dict      					=       torch.cat([encoding["attention_mask"] for encoding in encodings]      ,    dim=0  )

         elif return_tensors == "tf" and is_tf_available():
            import tensorflow as tf

            _lowercase      :				Optional[int]      					=       tf.stack([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				List[str]      					=       tf.stack([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         else:
            raise ValueError("Target return tensor type could not be returned"  )

         _lowercase      :				Optional[int]      					=       BatchEncoding()
         _lowercase      :				List[Any]      					=       input_ids
         _lowercase      :				Dict      					=       attention_mask

      if query_images is not None:
         _lowercase      :				int      					=       BatchEncoding()
         _lowercase      :				Any      					=       self.image_processor(
             _UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  ).pixel_values
         _lowercase      :				Any      					=       query_pixel_values

      if images is not None:
         _lowercase      :				str      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is not None and images is not None:
         _lowercase      :				List[Any]      					=       image_features.pixel_values
         return encoding
      elif query_images is not None and images is not None:
         _lowercase      :				Optional[Any]      					=       image_features.pixel_values
         return encoding
      elif text is not None or query_images is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**_UpperCamelCase  )      ,    tensor_type=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_object_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor_class






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor






