


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

snake_case__				:	Dict					       =					logging.get_logger(__name__)





@add_end_docstrings(a__						)
class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				*__a				:				Union[str, Any]		,				**__a				:				List[str]							)					->Tuple:
          super().__init__(*__a		,				**__a							)
          self.check_model_type(__a							)



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				Optional[Any]=None		,				__a				:				str=None		,				__a				:				int=None		,				**__a				:				Union[str, Any]							)					->List[str]:
          lowerCamelCase_,	lowerCamelCase_						:   str		= {}, {}
          if padding is not None:
             lowerCamelCase_						:   int		= padding
          if truncation is not None:
             lowerCamelCase_						:   List[str]		= truncation
          if top_k is not None:
             lowerCamelCase_						:   Any		= top_k
          return preprocess_params, {}, postprocess_params



       def __call__(						self				:				Optional[Any]		,				__a				:				Union["Image.Image", str]		,				__a				:				str = None		,				**__a				:				Union[str, Any]							)					->int:
          if isinstance(__a		,				(Image.Image, str)							) and isinstance(__a		,				__a							):
             lowerCamelCase_						:   List[Any]		= {"""image""": image, """question""": question}
          else:
             lowerCamelCase_						:   Tuple		= image
          lowerCamelCase_						:   List[Any]		= super().__call__(__a		,				**__a							)
          return results



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				Any		,				__a				:				str=False		,				__a				:				Dict=False							)					->List[Any]:
          lowerCamelCase_						:   List[Any]		= load_image(inputs["""image"""]							)
          lowerCamelCase_						:   Dict		= self.tokenizer(
              inputs["""question"""]		,				return_tensors=self.framework		,				padding=__a		,				truncation=__a							)
          lowerCamelCase_						:   List[Any]		= self.image_processor(images=__a		,				return_tensors=self.framework							)
          model_inputs.update(__a							)
          return model_inputs



       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				Any							)					->Optional[Any]:
          lowerCamelCase_						:   int		= self.model(**__a							)
          return model_outputs




       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				Optional[Any]		,				__a				:				Union[str, Any]=5							)					->str:
          if top_k > self.model.config.num_labels:
             lowerCamelCase_						:   Union[str, Any]		= self.model.config.num_labels

          if self.framework == "pt":
             lowerCamelCase_						:   Dict		= model_outputs.logits.sigmoid()[0]
             lowerCamelCase_,	lowerCamelCase_						:   str		= probs.topk(__a							)
          else:
             raise ValueError(F'''Unsupported framework: {self.framework}'''							)

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









import os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case__				:	List[Any]					       =					'\nimport os\n'

snake_case__				:	List[str]					       =					'\ndef foo():\n    import os\n    return False\n'

snake_case__				:	List[Any]					       =					'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case__				:	List[str]					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case__				:	Tuple					       =					'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case__				:	str					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case__				:	Any					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Union[str, Any]					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case__				:	Any					       =					[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""						,						A__					)
def    __lowerCamelCase   (      A__						: Dict						,						A__						: int					)     ->    List[Any]:
   lowerCamelCase_						:   List[str]		= os.path.join(A__						,						"""test_file.py"""					)
   with open(A__						,						"""w"""					) as _tmp_file:
      _tmp_file.write(A__					)

   lowerCamelCase_						:   str		= get_imports(A__					)
   assert parsed_imports == ["os"]





