






"""simple docstring"""



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 			UpperCamelCase_      (			*_lowerCamelCase							,		**_lowerCamelCase       ):
          pass

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







    @require_torch
    def 			UpperCamelCase_      (			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_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}],
              [{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'c'}, {'score': 0.3_3_3, 'label': 'b'}],
          ]							,		)

      lowercase							    =       image_classifier([image] * 5							,		candidate_labels=['A', 'B', 'C']							,		batch_size=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase       )							,		[
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
          ]							,		)







    @require_tf
    def 			UpperCamelCase_      (			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_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}]							,		)

      lowercase							    =       image_classifier([image] * 5							,		candidate_labels=['A', 'B', 'C']							,		batch_size=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase       )							,		[
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
              [
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
                  {'score': 0.3_3_3, 'label': ANY(_lowerCamelCase       )},
              ],
          ]							,		)







    @slow
    @require_torch
    def 			UpperCamelCase_      (			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_1_1, 'label': 'remote'},
              {'score': 0.4_8_5, 'label': 'cat'},
              {'score': 0.0_0_4, 'label': 'plane'},
          ]							,		)

      lowercase							    =       image_classifier([image] * 5							,		candidate_labels=['cat', 'plane', 'remote']							,		batch_size=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase       )							,		[
              [
                  {'score': 0.5_1_1, 'label': 'remote'},
                  {'score': 0.4_8_5, 'label': 'cat'},
                  {'score': 0.0_0_4, 'label': 'plane'},
              ],
          ]
          * 5							,		)







    @slow
    @require_tf
    def 			UpperCamelCase_      (			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_1_1, 'label': 'remote'},
              {'score': 0.4_8_5, 'label': 'cat'},
              {'score': 0.0_0_4, 'label': 'plane'},
          ]							,		)

      lowercase							    =       image_classifier([image] * 5							,		candidate_labels=['cat', 'plane', 'remote']							,		batch_size=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase       )							,		[
              [
                  {'score': 0.5_1_1, 'label': 'remote'},
                  {'score': 0.4_8_5, 'label': 'cat'},
                  {'score': 0.0_0_4, 'label': 'plane'},
              ],
          ]
          * 5							,		)










"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


