



import time
from contextlib import contextmanager
from pathlib import Path

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


SCREAMING_SNAKE_CASE__   			=   '''__DUMMY_TRANSFORMERS_USER__'''
SCREAMING_SNAKE_CASE__   			=   '''Dummy User'''
SCREAMING_SNAKE_CASE__   			=   '''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'''

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


@pytest.fixture
def A    (							__UpperCamelCase       )    ->   str:
			monkeypatch.setattr(
			    'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'			,						__UpperCamelCase       )


@pytest.fixture
def A    (							__UpperCamelCase       )    ->   Optional[int]:
			monkeypatch.setattr('datasets.config.HF_ENDPOINT'			,						__UpperCamelCase       )
			monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'			,						__UpperCamelCase       )


@pytest.fixture
def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'			,						__UpperCamelCase       )


@pytest.fixture
def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   str:
			HfFolder.save_token(__UpperCamelCase       )
			yield
			HfFolder.delete_token()


@pytest.fixture(scope='session'       )
def A    (							)    ->   Tuple:
			return HfApi(endpoint=__UpperCamelCase       )


@pytest.fixture(scope='session'       )
def A    (							__UpperCamelCase       )    ->   List[str]:
			A__	  =     HfFolder.get_token()
			HfFolder.save_token(__UpperCamelCase       )
			yield CI_HUB_USER_TOKEN
			if previous_token is not None:
						HfFolder.save_token(__UpperCamelCase       )


@pytest.fixture
def A    (							__UpperCamelCase       )    ->   int:
			def _cleanup_repo(__UpperCamelCase       ):
						hf_api.delete_repo(__UpperCamelCase			,						token=__UpperCamelCase			,						repo_type='dataset'       )

			return _cleanup_repo


@pytest.fixture
def A    (							__UpperCamelCase       )    ->   List[Any]:
			@contextmanager
			def _temporary_repo(__UpperCamelCase       ):
						try:
									yield repo_id
						finally:
									cleanup_repo(__UpperCamelCase       )

			return _temporary_repo


@pytest.fixture(scope='session'       )
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     f'''repo_txt_data-{int(time.time() * 10E3       )}'''
			A__	  =     f'''{CI_HUB_USER}/{repo_name}'''
			hf_api.create_repo(__UpperCamelCase			,						token=__UpperCamelCase			,						repo_type='dataset'			,						private=__UpperCamelCase       )
			hf_api.upload_file(
			    token=__UpperCamelCase			,						path_or_fileobj=str(__UpperCamelCase       )			,						path_in_repo='data/text_data.txt'			,						repo_id=__UpperCamelCase			,						repo_type='dataset'			,						)
			yield repo_id
			try:
						hf_api.delete_repo(__UpperCamelCase			,						token=__UpperCamelCase			,						repo_type='dataset'       )
			except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
						pass


@pytest.fixture()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			return hf_private_dataset_repo_txt_data_


@pytest.fixture(scope='session'       )
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     f'''repo_zipped_txt_data-{int(time.time() * 10E3       )}'''
			A__	  =     f'''{CI_HUB_USER}/{repo_name}'''
			hf_api.create_repo(__UpperCamelCase			,						token=__UpperCamelCase			,						repo_type='dataset'			,						private=__UpperCamelCase       )
			hf_api.upload_file(
			    token=__UpperCamelCase			,						path_or_fileobj=str(__UpperCamelCase       )			,						path_in_repo='data.zip'			,						repo_id=__UpperCamelCase			,						repo_type='dataset'			,						)
			yield repo_id
			try:
						hf_api.delete_repo(__UpperCamelCase			,						token=__UpperCamelCase			,						repo_type='dataset'       )
			except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
						pass


@pytest.fixture()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			return hf_private_dataset_repo_zipped_txt_data_


@pytest.fixture(scope='session'       )
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[Any]:
			A__	  =     f'''repo_zipped_img_data-{int(time.time() * 10E3       )}'''
			A__	  =     f'''{CI_HUB_USER}/{repo_name}'''
			hf_api.create_repo(__UpperCamelCase			,						token=__UpperCamelCase			,						repo_type='dataset'			,						private=__UpperCamelCase       )
			hf_api.upload_file(
			    token=__UpperCamelCase			,						path_or_fileobj=str(__UpperCamelCase       )			,						path_in_repo='data.zip'			,						repo_id=__UpperCamelCase			,						repo_type='dataset'			,						)
			yield repo_id
			try:
						hf_api.delete_repo(__UpperCamelCase			,						token=__UpperCamelCase			,						repo_type='dataset'       )
			except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
						pass






@pytest.fixture()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			return hf_private_dataset_repo_zipped_img_data_










'''simple docstring'''
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					@require_torch
					def     __snake_case						(	self     :						str)    ->							Optional[int]:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='hf-internal-testing/tiny-clap-htsat-unfused')
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= audio_classifier(_lowercase      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [{'score': 0.5_01, 'label': 'Sound of a dog'}, {'score': 0.4_99, 'label': 'Sound of vaccum cleaner'}]      ,       )



					@unittest.skip('No models are available in TF')
					def     __snake_case						(	self     :						Tuple)    ->							str:
								pass



					@slow
					@require_torch
					def     __snake_case						(	self     :						Optional[Any])    ->							Tuple:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='laion/clap-htsat-unfused'      ,       )
								# This is an audio of a dog
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= audio_classifier(_lowercase      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])

								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        {'score': 0.9_99, 'label': 'Sound of a dog'},
								        {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								    ]      ,       )

								A_									= audio_classifier([audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )
								A_									= audio_classifier(
								    [audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']      ,       batch_size=5)
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )



					@unittest.skip('No models are available in TF')
					def     __snake_case						(	self     :						List[str])    ->							str:
								pass




