

"""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 					_lowerCAmelCase  (	UpperCamelCase_   ):







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	['image_processor', 'tokenizer']
 __UpperCAmelCase	:					List[Any]   		=	'OwlViTImageProcessor'
 __UpperCAmelCase	:					Tuple   		=	('CLIPTokenizer', 'CLIPTokenizerFast')


 def __init__(			self							:			Union[str, Any],					UpperCAmelCase__							:			int=None,					UpperCAmelCase__							:			str=None,					**UpperCAmelCase__							:			List[str]	):
    __lowercase								=						None
    if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead.",					lowercase_,					)
       __lowercase								=						kwargs.pop("feature_extractor"	)

    __lowercase								=						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__(lowercase_,					lowercase_	)


 def __call__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str]=None,					UpperCAmelCase__							:			str=None,					UpperCAmelCase__							:			Tuple=None,					UpperCAmelCase__							:			Any="max_length",					UpperCAmelCase__							:			Any="np",					**UpperCAmelCase__							:			str	):




    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(lowercase_,					lowercase_	) or (isinstance(lowercase_,					lowercase_	) and not isinstance(text[0],					lowercase_	)):
          __lowercase								=						[self.tokenizer(lowercase_,					padding=lowercase_,					return_tensors=lowercase_,					**lowercase_	)]

       elif isinstance(lowercase_,					lowercase_	) and isinstance(text[0],					lowercase_	):
          __lowercase								=						[]

          # Maximum number of queries across batch
          __lowercase								=						max([len(lowercase_	) for t in text]	)

          # Pad all batch samples to max number of text queries
          for t in text:
             if len(lowercase_	) != max_num_queries:
                __lowercase								=						t + [' '] * (max_num_queries - len(lowercase_	))

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

       if return_tensors == "np":
          __lowercase								=						np.concatenate([encoding["input_ids"] for encoding in encodings],					axis=0	)
          __lowercase								=						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								=						jnp.concatenate([encoding["input_ids"] for encoding in encodings],					axis=0	)
          __lowercase								=						jnp.concatenate([encoding["attention_mask"] for encoding in encodings],					axis=0	)

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

          __lowercase								=						torch.cat([encoding["input_ids"] for encoding in encodings],					dim=0	)
          __lowercase								=						torch.cat([encoding["attention_mask"] for encoding in encodings],					dim=0	)

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

          __lowercase								=						tf.stack([encoding["input_ids"] for encoding in encodings],					axis=0	)
          __lowercase								=						tf.stack([encoding["attention_mask"] for encoding in encodings],					axis=0	)

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

       __lowercase								=						BatchEncoding()
       __lowercase								=						input_ids
       __lowercase								=						attention_mask

    if query_images is not None:
       __lowercase								=						BatchEncoding()
       __lowercase								=						self.image_processor(
           lowercase_,					return_tensors=lowercase_,					**lowercase_	).pixel_values
       __lowercase								=						query_pixel_values

    if images is not None:
       __lowercase								=						self.image_processor(lowercase_,					return_tensors=lowercase_,					**lowercase_	)

    if text is not None and images is not None:
       __lowercase								=						image_features.pixel_values
       return encoding
    elif query_images is not None and images is not None:
       __lowercase								=						image_features.pixel_values
       return encoding
    elif text is not None or query_images is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**lowercase_	),					tensor_type=lowercase_	)


 def 					_lowercase (			self							:			int,					*UpperCAmelCase__							:			Optional[Any],					**UpperCAmelCase__							:			int	):
    return self.image_processor.post_process(*lowercase_,					**lowercase_	)


 def 					_lowercase (			self							:			int,					*UpperCAmelCase__							:			Union[str, Any],					**UpperCAmelCase__							:			List[str]	):
    return self.image_processor.post_process_object_detection(*lowercase_,					**lowercase_	)


 def 					_lowercase (			self							:			Tuple,					*UpperCAmelCase__							:			List[Any],					**UpperCAmelCase__							:			Optional[int]	):
    return self.image_processor.post_process_image_guided_detection(*lowercase_,					**lowercase_	)


 def 					_lowercase (			self							:			List[Any],					*UpperCAmelCase__							:			Optional[int],					**UpperCAmelCase__							:			Tuple	):
    return self.tokenizer.batch_decode(*lowercase_,					**lowercase_	)


 def 					_lowercase (			self							:			List[str],					*UpperCAmelCase__							:			int,					**UpperCAmelCase__							:			Optional[int]	):
    return self.tokenizer.decode(*lowercase_,					**lowercase_	)


 @property
 def 					_lowercase (			self							:			Union[str, Any]	):
    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",					lowercase_,					)
    return self.image_processor_class



 @property
 def 					_lowercase (			self							:			Any	):
    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",					lowercase_,					)
    return self.image_processor








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 A  (   datasets.BuilderConfig     ):
      UpperCamelCase__							:  str      =","
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[Union[int, List[int], str]]      ="infer"
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[Union[int, str, List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[Union[List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[Literal["c", "python", "pyarrow"]]      =None
      UpperCamelCase__							:  Dict[Union[int, str], Callable[[Any], Any]]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[Union[int, List[int]]]      =None
      UpperCamelCase__							:  Optional[int]      =None
      UpperCamelCase__							:  Optional[Union[str, List[str]]]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ="."
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ='"'
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  int      =10000
      UpperCamelCase__							:  Optional[datasets.Features]      =None
      UpperCamelCase__							:  Optional[str]      ="strict"
      UpperCamelCase__							:  Literal["error", "warn", "skip"]      ="error"
      UpperCamelCase__							:  Optional[str]      =None






      def        lowerCamelCase       (					self						:     List[str] )					->						str:
             """simple docstring"""




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






      @property
      def        lowerCamelCase       (					self						:     int )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		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()			,   lowercase_ ):
                           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     ):
      UpperCamelCase__							:  List[str]      =CsvConfig






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




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






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






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     pa.Table )					->						pa.Table:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[Any] )					->						Any:
             """simple docstring"""




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


