



import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def A    (							__UpperCamelCase			,						__UpperCamelCase=7       )    ->   Optional[int]:
			A__	  =     None
			if token is not None:
						A__	  =     {'Accept': 'application/vnd.github+json', 'Authorization': f'''Bearer {token}'''}

			# The id of a workflow (not of a workflow run)
			A__	  =     '636036'

			A__	  =     f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
			# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
			url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

			A__	  =     requests.get(__UpperCamelCase			,						headers=__UpperCamelCase       ).json()

			return result["workflow_runs"]


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     get_daily_ci_runs(__UpperCamelCase       )
			A__	  =     None
			for workflow_run in workflow_runs:
						if workflow_run["status"] == "completed":
									A__	  =     workflow_run['id']
									break

			return workflow_run_id


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   str:
			A__	  =     get_last_daily_ci_runs(__UpperCamelCase       )
			if workflow_run_id is not None:
						A__	  =     get_artifacts_links(worflow_run_id=__UpperCamelCase			,						token=__UpperCamelCase       )
						for artifact_name in artifact_names:
									if artifact_name in artifacts_links:
												A__	  =     artifacts_links[artifact_name]
												download_artifact(
												    artifact_name=__UpperCamelCase			,						artifact_url=__UpperCamelCase			,						output_dir=__UpperCamelCase			,						token=__UpperCamelCase       )






def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			get_last_daily_ci_artifacts(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

			A__	  =     {}
			for artifact_name in artifact_names:
						A__	  =     os.path.join(__UpperCamelCase			,						f'''{artifact_name}.zip'''       )
						if os.path.isfile(__UpperCamelCase       ):
									A__	  =     {}
									with zipfile.ZipFile(__UpperCamelCase       ) as z:
												for filename in z.namelist():
															if not os.path.isdir(__UpperCamelCase       ):
																		# read the file
																		with z.open(__UpperCamelCase       ) as f:
																					A__	  =     f.read().decode('UTF-8'       )

			return results









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   __lowerCAmelCase  :
												"""simple docstring"""






												@staticmethod
												def      _a				(  *_snake_case       :							Any					,       **_snake_case       :							Optional[int]       ):






															"""simple docstring"""


															pass


@is_pipeline_test
@require_torch
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='hf-internal-testing/tiny-vilt-random-vqa'       )
								A__	  =     [
								    {
								        '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      _a				(  self       :							Optional[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								A__	  =     vqa_pipeline(_snake_case					,       top_k=1       )
								self.assertEqual(
								    _snake_case					,       [
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								    ]					,       )



					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


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

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

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



					@slow
					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='dandelin/vilt-b32-finetuned-vqa'       )
								A__	  =     './tests/fixtures/tests_samples/COCO/000000039769.png'
								A__	  =     'How many cats are there?'

								A__	  =     vqa_pipeline(image=_snake_case					,       question=_snake_case					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline({'image': image, 'question': question}					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline(
								    [{'image': image, 'question': question}, {'image': image, 'question': question}]					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2					,       )



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






								"""simple docstring"""


								pass




