
import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   List[Any]=7  ):
						_SCREAMING_SNAKE_CASE				= None
						if token is not None:
												_SCREAMING_SNAKE_CASE				= {"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}

						# The id of a workflow (not of a workflow run)
						_SCREAMING_SNAKE_CASE				= "636036"

						_SCREAMING_SNAKE_CASE				= 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}'

						_SCREAMING_SNAKE_CASE				= requests.get(__SCREAMING_SNAKE_CASE	,			headers=__SCREAMING_SNAKE_CASE  ).json()

						return result["workflow_runs"]



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= get_daily_ci_runs(__SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		_SCREAMING_SNAKE_CASE				= workflow_run["id"]
																		break

						return workflow_run_id



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= get_last_daily_ci_runs(__SCREAMING_SNAKE_CASE  )
						if workflow_run_id is not None:
												_SCREAMING_SNAKE_CASE				= get_artifacts_links(worflow_run_id=__SCREAMING_SNAKE_CASE	,			token=__SCREAMING_SNAKE_CASE  )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								_SCREAMING_SNAKE_CASE				= artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=__SCREAMING_SNAKE_CASE	,			artifact_url=__SCREAMING_SNAKE_CASE	,			output_dir=__SCREAMING_SNAKE_CASE	,			token=__SCREAMING_SNAKE_CASE  )




def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   int  ):
						get_last_daily_ci_artifacts(__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE  )

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

						return results

'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import GLPNImageProcessor


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=True					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= min_resolution
								_SCREAMING_SNAKE_CASE				= max_resolution
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size_divisor
								_SCREAMING_SNAKE_CASE				= do_rescale




		def     lowercase       (				self      ):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					GLPNImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GLPNImageProcessingTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_processor_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_rescale"      )      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						Image.Image      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						numpify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						np.ndarray      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						torchify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						torch.Tensor      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )