


"""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

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



@add_end_docstrings(a__			)
class 		snake_case_(       a__			):






     def __init__(	self  :					str					,				*UpperCamelCase_  :					Union[str, Any]					,				**UpperCamelCase_  :					Dict				):
           super().__init__(*UpperCamelCase_					,				**UpperCamelCase_				)
           self.check_model_type(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					List[str]=None					,				UpperCamelCase_  :					Tuple=None					,				**UpperCamelCase_  :					Any				):
           lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   {}, {}
           if padding is not None:
                 lowerCAmelCase					:	Optional[int]       =   padding
           if truncation is not None:
                 lowerCAmelCase					:	Optional[Any]       =   truncation
           if top_k is not None:
                 lowerCAmelCase					:	Union[str, Any]       =   top_k
           return preprocess_params, {}, postprocess_params






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






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					List[str]=False					,				UpperCamelCase_  :					str=False				):
           lowerCAmelCase					:	Optional[int]       =   load_image(inputs['''image''']				)
           lowerCAmelCase					:	Any       =   self.tokenizer(
               inputs['''question''']					,				return_tensors=self.framework					,				padding=UpperCamelCase_					,				truncation=UpperCamelCase_				)
           lowerCAmelCase					:	int       =   self.image_processor(images=UpperCamelCase_					,				return_tensors=self.framework				)
           model_inputs.update(UpperCamelCase_				)
           return model_inputs






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	int       =   self.model(**UpperCamelCase_				)
           return model_outputs






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					int=5				):
           if top_k > self.model.config.num_labels:
                 lowerCAmelCase					:	List[Any]       =   self.model.config.num_labels

           if self.framework == "pt":
                 lowerCAmelCase					:	Optional[int]       =   model_outputs.logits.sigmoid()[0]
                 lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   probs.topk(UpperCamelCase_				)
           else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''				)

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






"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


snake_case__		:     Optional[Any]    				=				namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[str]			):
      return (abs(source - target			) / target) < 0.01






@pytest.mark.integration
def      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   _TestCommandArgs(dataset=_snake_case ,   all_configs=_snake_case ,   save_infos=_snake_case			)
      lowerCAmelCase					:	str       =   TestCommand(*_snake_case			)
      test_command.run()
      lowerCAmelCase					:	str       =   os.path.join(_snake_case ,   '''README.md'''			)
      assert os.path.exists(_snake_case			)
      lowerCAmelCase					:	Tuple       =   DatasetInfosDict.from_directory(_snake_case			)
      lowerCAmelCase					:	List[str]       =   DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''			)			),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']			)			),
                          '''langs''': Sequence(Value('''string'''			)			),
                          '''spans''': Sequence(Value('''string'''			)			),
                      }			) ,   splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ] ,   download_size=3940680 ,   dataset_size=2589981 ,   )
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   getattr(dataset_infos['''default'''] ,   _snake_case			), getattr(expected_dataset_infos['''default'''] ,   _snake_case			)
            if key == "num_bytes":
                  assert is_apercent_close(_snake_case ,   _snake_case			)
            elif key == "splits":
                  assert list(_snake_case			) == list(_snake_case			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes			)
            else:
                  result == expected


