


import unittest

from transformers import 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 			_A       (*lowerCAmelCase      ,				**lowerCAmelCase					):
										pass





@is_pipeline_test
@require_vision
class       A    (   unittest.TestCase ):


							@require_torch
							def 			_A       (self					):
									__lowercase=			pipeline(
									    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'      ,				)
									__lowercase=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
									__lowercase=			image_classifier(_lowerCamelCase      ,				candidate_labels=['a', 'b', 'c']					)

									# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
									# python and torch versions.
									self.assertIn(
									    nested_simplify(_lowerCamelCase					)      ,				[
									        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}],
									        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'c'}, {'score': 0.3_33, 'label': 'b'}],
									    ]      ,				)

									__lowercase=			image_classifier([image] * 5      ,				candidate_labels=['A', 'B', 'C']      ,				batch_size=2					)
									self.assertEqual(
									    nested_simplify(_lowerCamelCase					)      ,				[
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									    ]      ,				)


							@require_tf
							def 			_A       (self					):
									__lowercase=			pipeline(
									    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'      ,				framework='tf'					)
									__lowercase=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
									__lowercase=			image_classifier(_lowerCamelCase      ,				candidate_labels=['a', 'b', 'c']					)

									self.assertEqual(
									    nested_simplify(_lowerCamelCase					)      ,				[{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}]      ,				)

									__lowercase=			image_classifier([image] * 5      ,				candidate_labels=['A', 'B', 'C']      ,				batch_size=2					)
									self.assertEqual(
									    nested_simplify(_lowerCamelCase					)      ,				[
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									        [
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									            {'score': 0.3_33, 'label': ANY(_lowerCamelCase					)},
									        ],
									    ]      ,				)


							@slow
							@require_torch
							def 			_A       (self					):
									__lowercase=			pipeline(
									    task='zero-shot-image-classification'      ,				model='openai/clip-vit-base-patch32'      ,				)
									# This is an image of 2 cats with remotes and no planes
									__lowercase=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
									__lowercase=			image_classifier(_lowerCamelCase      ,				candidate_labels=['cat', 'plane', 'remote']					)

									self.assertEqual(
									    nested_simplify(_lowerCamelCase					)      ,				[
									        {'score': 0.5_11, 'label': 'remote'},
									        {'score': 0.4_85, 'label': 'cat'},
									        {'score': 0.0_04, 'label': 'plane'},
									    ]      ,				)

									__lowercase=			image_classifier([image] * 5      ,				candidate_labels=['cat', 'plane', 'remote']      ,				batch_size=2					)
									self.assertEqual(
									    nested_simplify(_lowerCamelCase					)      ,				[
									        [
									            {'score': 0.5_11, 'label': 'remote'},
									            {'score': 0.4_85, 'label': 'cat'},
									            {'score': 0.0_04, 'label': 'plane'},
									        ],
									    ]
									    * 5      ,				)







							@slow
							@require_tf
							def 			_A       (self					):
									__lowercase=			pipeline(
									    task='zero-shot-image-classification'      ,				model='openai/clip-vit-base-patch32'      ,				framework='tf'					)
									# This is an image of 2 cats with remotes and no planes
									__lowercase=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
									__lowercase=			image_classifier(_lowerCamelCase      ,				candidate_labels=['cat', 'plane', 'remote']					)
									self.assertEqual(
									    nested_simplify(_lowerCamelCase					)      ,				[
									        {'score': 0.5_11, 'label': 'remote'},
									        {'score': 0.4_85, 'label': 'cat'},
									        {'score': 0.0_04, 'label': 'plane'},
									    ]      ,				)

									__lowercase=			image_classifier([image] * 5      ,				candidate_labels=['cat', 'plane', 'remote']      ,				batch_size=2					)
									self.assertEqual(
									    nested_simplify(_lowerCamelCase					)      ,				[
									        [
									            {'score': 0.5_11, 'label': 'remote'},
									            {'score': 0.4_85, 'label': 'cat'},
									            {'score': 0.0_04, 'label': 'plane'},
									        ],
									    ]
									    * 5      ,				)














"""simple docstring"""



import argparse
import json

from tqdm import tqdm
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser()

  # Required parameters
  parser.add_argument(
      '--src_path'		,					type=__snake_case		,					default='biencoder-nq-dev.json'		,					help='Path to raw DPR training data'		,					)
  parser.add_argument(
      '--evaluation_set'		,					type=__snake_case		,					help='where to store parsed evaluation_set file'		,					)
  parser.add_argument(
      '--gold_data_path'		,					type=__snake_case		,					help='where to store parsed gold_data_path file'		,					)
  lowercase							    =       parser.parse_args()

  with open(args.src_path		,					'r'    ) as src_file, open(args.evaluation_set		,					'w'    ) as eval_file, open(
      args.gold_data_path		,					'w'    ) as gold_file:
    lowercase							    =       json.load(__snake_case    )
    for dpr_record in tqdm(__snake_case    ):
      lowercase							    =       dpr_record['question']
      lowercase							    =       [context['title'] for context in dpr_record['positive_ctxs']]
      eval_file.write(question + '\n'    )
      gold_file.write('\t'.join(__snake_case    ) + '\n'    )


if __name__ == "__main__":
    main()


