


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

UpperCamelCase														=      logging.get_logger(__name__)






@add_end_docstrings(lowerCamelCase_     )
class     _lowerCamelCase					(   lowerCamelCase_     ):




       """simple docstring"""



       def __init__( self							,			*_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE						)->Union[str, Any]:




         '''simple docstring'''





         super().__init__(*snake_case__							,			**snake_case__						)
         self.check_model_type(snake_case__						)



       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE=None							,			_SCREAMING_SNAKE_CASE=None							,			_SCREAMING_SNAKE_CASE=None							,			**_SCREAMING_SNAKE_CASE						)->List[Any]:




         '''simple docstring'''





         A_ :							Union[str, Any]							=   {}, {}
         if padding is not None:
           A_ :							Any							=   padding
         if truncation is not None:
           A_ :							Tuple							=   truncation
         if top_k is not None:
           A_ :							int							=   top_k
         return preprocess_params, {}, postprocess_params



       def __call__( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE = None							,			**_SCREAMING_SNAKE_CASE						)->str:




         '''simple docstring'''





         if isinstance(snake_case__							,			(Image.Image, str)						) and isinstance(snake_case__							,			snake_case__						):
           A_ :							Optional[Any]							=   {'image': image, 'question': question}
         else:
           A_ :							Optional[int]							=   image
         A_ :							List[Any]							=   super().__call__(snake_case__							,			**snake_case__						)
         return results



       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE=False							,			_SCREAMING_SNAKE_CASE=False						)->Union[str, Any]:




         '''simple docstring'''





         A_ :							List[str]							=   load_image(inputs['''image''']						)
         A_ :							Dict							=   self.tokenizer(
             inputs['''question''']							,			return_tensors=self.framework							,			padding=snake_case__							,			truncation=snake_case__						)
         A_ :							Any							=   self.image_processor(images=snake_case__							,			return_tensors=self.framework						)
         model_inputs.update(snake_case__						)
         return model_inputs



       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE						)->Dict:




         '''simple docstring'''





         A_ :							Optional[int]							=   self.model(**snake_case__						)
         return model_outputs







       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE=5						)->Optional[int]:




         '''simple docstring'''





         if top_k > self.model.config.num_labels:
           A_ :							Union[str, Any]							=   self.model.config.num_labels

         if self.framework == "pt":
           A_ :							Any							=   model_outputs.logits.sigmoid()[0]
           A_ :							Dict							=   probs.topk(snake_case__						)
         else:
           raise ValueError(F'''Unsupported framework: {self.framework}'''						)

         A_ :							Optional[int]							=   scores.tolist()
         A_ :							Union[str, Any]							=   ids.tolist()
         return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__							,			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


UpperCamelCase__ :   Optional[Any]						=							logging.get_logger(__name__)


UpperCamelCase__ :   int						=							TypeVar('''DatasetType''', Dataset, IterableDataset)





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[DatasetType]  ,							lowerCamelCase_ :     Optional[List[float]] = None  ,							lowerCamelCase_ :     Optional[int] = None  ,							lowerCamelCase_ :     Optional[DatasetInfo] = None  ,							lowerCamelCase_ :     Optional[NamedSplit] = None  ,							lowerCamelCase_ :     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(lowerCamelCase_  ):
						if not isinstance(lowerCamelCase_  ,							(Dataset, IterableDataset)  ):
									if isinstance(lowerCamelCase_  ,							(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(lowerCamelCase_  )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_  )  )}\']'  )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_  ).__name__}.'  )
						if i == 0:
									SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      (
									    (Dataset, IterableDataset) if isinstance(lowerCamelCase_  ,							lowerCamelCase_  ) else (IterableDataset, Dataset)
									)
						elif not isinstance(lowerCamelCase_  ,							lowerCamelCase_  ):
									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(
						    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							stopping_strategy=lowerCamelCase_  )
			else:
						return _interleave_iterable_datasets(
						    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							stopping_strategy=lowerCamelCase_  )





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







			"""simple docstring"""





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



