



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_      :      str    =     logging.get_logger(__name__)
@add_end_docstrings(_lowercase				)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  super().__init__(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  self.check_model_type(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__							,			a__												= {}, {}
  if padding is not None:
   a__												= padding
  if truncation is not None:
   a__												= truncation
  if top_k is not None:
   a__												= top_k
  return preprocess_params, {}, postprocess_params

 def __call__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if isinstance(SCREAMING_SNAKE_CASE		,				(Image.Image, str)      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   a__												= {'''image''': image, '''question''': question}
  else:
   a__												= image
  a__												= super().__call__(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  return results

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False      ) ->			Dict:
  a__												= load_image(inputs['''image''']      )
  a__												= self.tokenizer(
      inputs['''question''']		,				return_tensors=self.framework		,				padding=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors=self.framework      )
  model_inputs.update(SCREAMING_SNAKE_CASE      )
  return model_inputs

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.model(**SCREAMING_SNAKE_CASE      )
  return model_outputs



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=5      ) ->			Optional[Any]:
  if top_k > self.model.config.num_labels:
   a__												= self.model.config.num_labels

  if self.framework == "pt":
   a__												= model_outputs.logits.sigmoid()[0]
   a__							,			a__												= probs.topk(SCREAMING_SNAKE_CASE      )
  else:
   raise ValueError(f"Unsupported framework: {self.framework}"      )

  a__												= scores.tolist()
  a__												= ids.tolist()
  return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )]










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_      :      Any    =     logging.get_logger(__name__)


a_      :      str    =     TypeVar('DatasetType', Dataset, IterableDataset)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = "first_exhausted"							,  ):
 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(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (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(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   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(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )
 else:
  return _interleave_iterable_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = 0							,  ):

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





