
'''simple docstring'''
from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A__   :		Union[str, Any] 		=logging.get_logger(__name__)


A__   :		Optional[Any] 		=TypeVar('DatasetType', Dataset, IterableDataset)







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[DatasetType]			,     __SCREAMING_SNAKE_CASE						:  Optional[List[float]] = None			,     __SCREAMING_SNAKE_CASE						:  Optional[int] = None			,     __SCREAMING_SNAKE_CASE						:  Optional[DatasetInfo] = None			,     __SCREAMING_SNAKE_CASE						:  Optional[NamedSplit] = None			,     __SCREAMING_SNAKE_CASE						:  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"			,     )				->       DatasetType:





  """simple docstring"""

  from .arrow_dataset import Dataset
  from .iterable_dataset import IterableDataset

  if not datasets:
    raise ValueError("""Unable to interleave an empty list of datasets.""" )
  for i, dataset in enumerate(__SCREAMING_SNAKE_CASE ):
    if not isinstance(__SCREAMING_SNAKE_CASE			,     (Dataset, IterableDataset) ):
      if isinstance(__SCREAMING_SNAKE_CASE			,     (DatasetDict, IterableDatasetDict) ):
        if not dataset:
          raise ValueError(
              F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
              """is an empty dataset dictionary.""" )
        raise ValueError(
            F"Dataset at position {i} has at least one split: {list(__SCREAMING_SNAKE_CASE )}\n"
            F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__SCREAMING_SNAKE_CASE ) )}']" )
      raise ValueError(
          F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__SCREAMING_SNAKE_CASE ).__name__}." )
    if i == 0:
      __A							,					__A		:     Optional[Any]												=		(
          (Dataset, IterableDataset) if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) else (IterableDataset, Dataset)
      )
    elif not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
      raise ValueError(
          F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
  if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
    raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
  if dataset_type is Dataset:
    return _interleave_map_style_datasets(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     info=__SCREAMING_SNAKE_CASE			,     split=__SCREAMING_SNAKE_CASE			,     stopping_strategy=__SCREAMING_SNAKE_CASE )
  else:
    return _interleave_iterable_datasets(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     info=__SCREAMING_SNAKE_CASE			,     split=__SCREAMING_SNAKE_CASE			,     stopping_strategy=__SCREAMING_SNAKE_CASE )







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[DatasetType]			,     __SCREAMING_SNAKE_CASE						:  Optional[DatasetInfo] = None			,     __SCREAMING_SNAKE_CASE						:  Optional[NamedSplit] = None			,     __SCREAMING_SNAKE_CASE						:  int = 0			,     )				->       DatasetType:





  """simple docstring"""

  if not dsets:
    raise ValueError("""Unable to concatenate an empty list of datasets.""" )
  for i, dataset in enumerate(__SCREAMING_SNAKE_CASE ):
    if not isinstance(__SCREAMING_SNAKE_CASE			,     (Dataset, IterableDataset) ):
      if isinstance(__SCREAMING_SNAKE_CASE			,     (DatasetDict, IterableDatasetDict) ):
        if not dataset:
          raise ValueError(
              F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
              """is an empty dataset dictionary.""" )
        raise ValueError(
            F"Dataset at position {i} has at least one split: {list(__SCREAMING_SNAKE_CASE )}\n"
            F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__SCREAMING_SNAKE_CASE ) )}']" )
      raise ValueError(
          F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__SCREAMING_SNAKE_CASE ).__name__}." )
    if i == 0:
      __A							,					__A		:     Optional[Any]												=		(
          (Dataset, IterableDataset) if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) else (IterableDataset, Dataset)
      )
    elif not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
      raise ValueError(
          F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
  if dataset_type is Dataset:
    return _concatenate_map_style_datasets(__SCREAMING_SNAKE_CASE			,     info=__SCREAMING_SNAKE_CASE			,     split=__SCREAMING_SNAKE_CASE			,     axis=__SCREAMING_SNAKE_CASE )
  else:
    return _concatenate_iterable_datasets(__SCREAMING_SNAKE_CASE			,     info=__SCREAMING_SNAKE_CASE			,     split=__SCREAMING_SNAKE_CASE			,     axis=__SCREAMING_SNAKE_CASE )





'''simple docstring'''
from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A__   :		Any 		=logging.get_logger(__name__)







@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Union[str, Any]				,   *lowerCamelCase			:    int				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   super().__init__(*lowerCamelCase				,   **lowerCamelCase       )
   self.check_model_type(lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Union[str, Any]=None				,   lowerCamelCase			:    int=None				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A							,					__A		:     Tuple												=		{}, {}
   if padding is not None:
     __A		:     Any												=		padding
   if truncation is not None:
     __A		:     Optional[Any]												=		truncation
   if top_k is not None:
     __A		:     List[str]												=		top_k
   return preprocess_params, {}, postprocess_params
 def __call__(   self			:    Any				,   lowerCamelCase			:    Union["Image.Image", str]				,   lowerCamelCase			:    str = None				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   if isinstance(lowerCamelCase				,   (Image.Image, str)       ) and isinstance(lowerCamelCase				,   lowerCamelCase       ):
     __A		:     Tuple												=		{"""image""": image, """question""": question}
   else:
     __A		:     List[Any]												=		image
   __A		:     Any												=		super().__call__(lowerCamelCase				,   **lowerCamelCase       )
   return results
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Dict=False				,   lowerCamelCase			:    str=False       ):



   """simple docstring"""





   __A		:     List[str]												=		load_image(inputs["""image"""]       )
   __A		:     Optional[Any]												=		self.tokenizer(
       inputs["""question"""]				,   return_tensors=self.framework				,   padding=lowerCamelCase				,   truncation=lowerCamelCase       )
   __A		:     Union[str, Any]												=		self.image_processor(images=lowerCamelCase				,   return_tensors=self.framework       )
   model_inputs.update(lowerCamelCase       )
   return model_inputs
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     List[str]												=		self.model(**lowerCamelCase       )
   return model_outputs






 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[Any]=5       ):



   """simple docstring"""





   if top_k > self.model.config.num_labels:
     __A		:     str												=		self.model.config.num_labels

   if self.framework == "pt":
     __A		:     Optional[Any]												=		model_outputs.logits.sigmoid()[0]
     __A							,					__A		:     int												=		probs.topk(lowerCamelCase       )
   else:
     raise ValueError(f"Unsupported framework: {self.framework}"       )

   __A		:     Optional[Any]												=		scores.tolist()
   __A		:     Dict												=		ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase				,   lowerCamelCase       )]



