

import pytest


_a		       =   '__dummy_dataset1__'

_a		       =   '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'







@pytest.fixture
def _a (       )    ->    List[str]:

 """simple docstring"""






 return DATASET_LOADING_SCRIPT_NAME







@pytest.fixture
def _a (       )    ->    Optional[int]:

 """simple docstring"""






 return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def _a (       SCREAMING_SNAKE_CASE		:			Optional[int]    ,					SCREAMING_SNAKE_CASE		:			Any    ,					SCREAMING_SNAKE_CASE		:			Dict		)    ->    List[str]:

 """simple docstring"""






 __lowerCAmelCase:   Union[str, Any]   	=      dataset_loading_script_name
 __lowerCAmelCase:   Tuple   	=      tmp_path / 'datasets' / script_name
 script_dir.mkdir(parents=__snake_case		)
 __lowerCAmelCase:   Optional[int]   	=      script_dir / f'''{script_name}.py'''
 with open(__snake_case    ,					'w'		) as f:
  f.write(__snake_case		)
 return str(__snake_case		)













"""simple docstring"""



import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:

    class        a     :







        @staticmethod
        def 			UpperCamelCase_      (			*_lowerCamelCase							,		**_lowerCamelCase       ):
          pass

@is_pipeline_test
@require_torch
@require_vision
class        a     (   unittest.TestCase	):
    UpperCAmelCase_ :     Union[str, Any]	     =MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       pipeline('visual-question-answering'							,		model='hf-internal-testing/tiny-vilt-random-vqa'       )
      lowercase							    =       [
          {
              'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       ),
              'question': 'How many cats are there?',
          },
          {
              'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
              'question': 'How many cats are there?',
          },
      ]
      return vqa_pipeline, examples







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       vqa_pipeline(_lowerCamelCase							,		top_k=1       )
      self.assertEqual(
          _lowerCamelCase							,		[
              [{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}],
              [{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}],
          ]							,		)







    @require_torch
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       pipeline('visual-question-answering'							,		model='hf-internal-testing/tiny-vilt-random-vqa'       )
      lowercase							    =       './tests/fixtures/tests_samples/COCO/000000039769.png'
      lowercase							    =       'How many cats are there?'

      lowercase							    =       vqa_pipeline(image=_lowerCamelCase							,		question='How many cats are there?'							,		top_k=2       )
      self.assertEqual(
          _lowerCamelCase							,		[{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}, {'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}]       )

      lowercase							    =       vqa_pipeline({'image': image, 'question': question}							,		top_k=2       )
      self.assertEqual(
          _lowerCamelCase							,		[{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}, {'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}]       )







    @slow
    @require_torch
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       pipeline('visual-question-answering'							,		model='dandelin/vilt-b32-finetuned-vqa'       )
      lowercase							    =       './tests/fixtures/tests_samples/COCO/000000039769.png'
      lowercase							    =       'How many cats are there?'

      lowercase							    =       vqa_pipeline(image=_lowerCamelCase							,		question=_lowerCamelCase							,		top_k=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase							,		decimals=4       )							,		[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]       )

      lowercase							    =       vqa_pipeline({'image': image, 'question': question}							,		top_k=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase							,		decimals=4       )							,		[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]       )

      lowercase							    =       vqa_pipeline(
          [{'image': image, 'question': question}, {'image': image, 'question': question}]							,		top_k=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase							,		decimals=4       )							,		[[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]] * 2							,		)







    @require_tf
    @unittest.skip('Visual question answering not implemented in TF'       )
    def 			UpperCamelCase_      (			self       ):
      pass


