





"""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        __snake_case	(					_lowercase    ):
      a__				=		["""image_processor""", """tokenizer"""]
      a__				=		"""OwlViTImageProcessor"""
      a__				=		("""CLIPTokenizer""", """CLIPTokenizerFast""")



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      **lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     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   ,      )
          a__:					Any      							=     kwargs.pop('feature_extractor')

        a__:					Union[str, Any]      							=     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   ,      lowercase=None   ,      lowercase=None   ,      lowercase=None   ,      lowercase="max_length"   ,      lowercase="np"   ,      **lowercase)     ->      Optional[int]:





        '''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)):
            a__:					Optional[Any]      							=     [self.tokenizer(__UpperCamelCase   ,      padding=__UpperCamelCase   ,      return_tensors=__UpperCamelCase   ,      **__UpperCamelCase)]

          elif isinstance(__UpperCamelCase   ,      __UpperCamelCase) and isinstance(text[0]   ,      __UpperCamelCase):
            a__:					Any      							=     []

            # Maximum number of queries across batch
            a__:					List[Any]      							=     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:
                a__:					List[Any]      							=     t + [' '] * (max_num_queries - len(__UpperCamelCase))

              a__:					List[Any]      							=     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":
            a__:					List[str]      							=     np.concatenate([encoding['input_ids'] for encoding in encodings]   ,      axis=0)
            a__:					List[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

            a__:					Optional[Any]      							=     jnp.concatenate([encoding['input_ids'] for encoding in encodings]   ,      axis=0)
            a__:					Tuple      							=     jnp.concatenate([encoding['attention_mask'] for encoding in encodings]   ,      axis=0)

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

            a__:					Union[str, Any]      							=     torch.cat([encoding['input_ids'] for encoding in encodings]   ,      dim=0)
            a__:					Dict      							=     torch.cat([encoding['attention_mask'] for encoding in encodings]   ,      dim=0)

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

            a__:					List[Any]      							=     tf.stack([encoding['input_ids'] for encoding in encodings]   ,      axis=0)
            a__:					Optional[int]      							=     tf.stack([encoding['attention_mask'] for encoding in encodings]   ,      axis=0)

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

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

        if query_images is not None:
          a__:					Dict      							=     BatchEncoding()
          a__:					Optional[int]      							=     self.image_processor(
              __UpperCamelCase   ,      return_tensors=__UpperCamelCase   ,      **__UpperCamelCase).pixel_values
          a__:					Dict      							=     query_pixel_values

        if images is not None:
          a__:					Any      							=     self.image_processor(__UpperCamelCase   ,      return_tensors=__UpperCamelCase   ,      **__UpperCamelCase)

        if text is not None and images is not None:
          a__:					Union[str, Any]      							=     image_features.pixel_values
          return encoding
        elif query_images is not None and images is not None:
          a__:					Optional[int]      							=     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   ,      *lowercase   ,      **lowercase)     ->      List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      Optional[int]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      str:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      List[str]:





        '''simple docstring'''





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



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''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)     ->      Optional[Any]:





        '''simple docstring'''





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



"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowercase__ 	=     '2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
 raise ImportWarning(
     'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
 )

if version.parse(pyarrow.__version__).major < 8:
 raise ImportWarning(
     'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
     'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
 )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowercase__ 	=     concatenate_datasets
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadManager
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


