




import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase								=					'__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase								=					'Dummy User'
UpperCamelCase								=					'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'

UpperCamelCase								=					'https://hub-ci.huggingface.co'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
UpperCamelCase								=					Path('~/.huggingface/hub_ci_token').expanduser()



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     monkeypatch.setattr(
         "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     monkeypatch.setattr("datasets.config.HF_ENDPOINT"       ,    lowerCAmelCase_      )
     monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[Any]      ):
     """simple docstring"""

     HfFolder.save_token(lowerCAmelCase_      )
     yield
     HfFolder.delete_token()



@pytest.fixture(scope="session"      )
def 							_A       (						):
     """simple docstring"""

     return HfApi(endpoint=lowerCAmelCase_      )



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi      ):
     """simple docstring"""

     lowerCAmelCase__          =			HfFolder.get_token()
     HfFolder.save_token(lowerCAmelCase_      )
     yield CI_HUB_USER_TOKEN
     if previous_token is not None:
          HfFolder.save_token(lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     def _cleanup_repo(lowerCAmelCase_					: Optional[Any]      ):
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )

     return _cleanup_repo



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     @contextmanager
     def _temporary_repo(lowerCAmelCase_					: str      ):
          try:
               yield repo_id
          finally:
               cleanup_repo(lowerCAmelCase_      )

     return _temporary_repo



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data/text_data.txt"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_img_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_img_data_






import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Any:
         lowerCAmelCase__          =			hf_hub_download(
             repo_id="nateraw/video-demo"       ,     filename="archery.mp4"       ,     repo_type="dataset"       )
         lowerCAmelCase__          =			VideoClassificationPipeline(model=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       ,     top_k=2       )
         lowerCAmelCase__          =			[
             example_video_filepath,
             "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
         ]
         return video_classifier, examples


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Tuple:
         for example in examples:
              lowerCAmelCase__          =			video_classifier(SCREAMING_SNAKE_CASE__       )

              self.assertEqual(
                  SCREAMING_SNAKE_CASE__       ,     [
                      {"score": ANY(SCREAMING_SNAKE_CASE__       ), "label": ANY(SCREAMING_SNAKE_CASE__       )},
                      {"score": ANY(SCREAMING_SNAKE_CASE__       ), "label": ANY(SCREAMING_SNAKE_CASE__       )},
                  ]       ,     )


    @require_torch
    def        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
         lowerCAmelCase__          =			VideoMAEFeatureExtractor(
             size={"shortest_edge": 10}       ,     crop_size={"height": 10, "width": 10}       )
         lowerCAmelCase__          =			pipeline(
             "video-classification"       ,     model=SCREAMING_SNAKE_CASE__       ,     feature_extractor=SCREAMING_SNAKE_CASE__       ,     frame_sampling_rate=4       )

         lowerCAmelCase__          =			hf_hub_download(repo_id="nateraw/video-demo"       ,     filename="archery.mp4"       ,     repo_type="dataset"       )
         lowerCAmelCase__          =			video_classifier(SCREAMING_SNAKE_CASE__       ,     top_k=2       )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__       ,     decimals=4       )       ,     [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}]       ,     )

         lowerCAmelCase__          =			video_classifier(
             [
                 video_file_path,
                 video_file_path,
             ]       ,     top_k=2       ,     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__       ,     decimals=4       )       ,     [
                 [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
                 [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
             ]       ,     )


    @require_tf
    def        a				(	self					:					Optional[Any]       )				->	Optional[int]:
         pass
