





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 PoolFormerImageProcessor



class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  def __init__(							self					:						int   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						List[Any]=7   ,    lowerCAmelCase_					:						Optional[int]=3   ,    lowerCAmelCase_					:						List[Any]=3_0   ,    lowerCAmelCase_					:						List[str]=4_0_0   ,    lowerCAmelCase_					:						int=True   ,    lowerCAmelCase_					:						str=None   ,    lowerCAmelCase_					:						List[Any]=0.9   ,    lowerCAmelCase_					:						Any=None   ,    lowerCAmelCase_					:						Dict=True   ,    lowerCAmelCase_					:						Optional[Any]=[0.5, 0.5, 0.5]   ,    lowerCAmelCase_					:						List[str]=[0.5, 0.5, 0.5]   ,    )				->		List[str]:
     __lowerCAmelCase				      =      size if size is not None else {'shortest_edge': 3_0}
     __lowerCAmelCase				      =      crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      min_resolution
     __lowerCAmelCase				      =      max_resolution
     __lowerCAmelCase				      =      do_resize_and_center_crop
     __lowerCAmelCase				      =      size
     __lowerCAmelCase				      =      crop_pct
     __lowerCAmelCase				      =      crop_size
     __lowerCAmelCase				      =      do_normalize
     __lowerCAmelCase				      =      image_mean
     __lowerCAmelCase				      =      image_std






  def 							lowercase						(							self					:						str	)				->		Tuple:
     return {
         "size": self.size,
         "do_resize_and_center_crop": self.do_resize_and_center_crop,
         "crop_pct": self.crop_pct,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }





@require_torch
@require_vision
class 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					PoolFormerImageProcessor if is_vision_available() else None






  def 							lowercase						(							self					:						int	)				->		str:
     __lowerCAmelCase				      =      PoolFormerImageProcessingTester(self	)






  @property
  def 							lowercase						(							self					:						List[Any]	)				->		Any:
     return self.image_processor_tester.prepare_image_processor_dict()






  def 							lowercase						(							self					:						List[str]	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_resize_and_center_crop'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'size'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'crop_pct'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_normalize'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'image_mean'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'image_std'	)	)






  def 							lowercase						(							self					:						Optional[Any]	)				->		List[str]:
     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 3_0}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 3_0, 'width': 3_0}	)

     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict   ,    size=4_2   ,    crop_size=8_4	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 4_2}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 8_4, 'width': 8_4}	)






  def 							lowercase						(							self					:						List[Any]	)				->		str:
     pass






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

     # Test not batched input
     __lowerCAmelCase				      =      image_processing(image_inputs[0]   ,    return_tensors='pt'	).pixel_values

     self.assertEqual(
         encoded_images.shape   ,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )

     # Test batched
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )






  def 							lowercase						(							self					:						Tuple	)				->		Optional[int]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_   ,    numpify=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    np.ndarray	)

     # Test not batched input
     __lowerCAmelCase				      =      image_processing(image_inputs[0]   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )

     # Test batched
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )






  def 							lowercase						(							self					:						Dict	)				->		Optional[Any]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_   ,    torchify=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    torch.Tensor	)

     # Test not batched input
     __lowerCAmelCase				      =      image_processing(image_inputs[0]   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )

     # Test batched
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape   ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )








import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 				_UpperCAmelCase			(    A				,							A=7     ):




   '''simple docstring'''

   UpperCAmelCase__						=None
   if token is not None:
      UpperCAmelCase__						={"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}

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

   UpperCAmelCase__						=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}"""

   UpperCAmelCase__						=requests.get(A				,							headers=A     ).json()

   return result["workflow_runs"]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_daily_ci_runs(A     )
   UpperCAmelCase__						=None
   for workflow_run in workflow_runs:
      if workflow_run["status"] == "completed":
         UpperCAmelCase__						=workflow_run["id"]
         break

   return workflow_run_id






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_last_daily_ci_runs(A     )
   if workflow_run_id is not None:
      UpperCAmelCase__						=get_artifacts_links(worflow_run_id=A				,							token=A     )
      for artifact_name in artifact_names:
         if artifact_name in artifacts_links:
            UpperCAmelCase__						=artifacts_links[artifact_name]
            download_artifact(
                artifact_name=A				,							artifact_url=A				,							output_dir=A				,							token=A     )






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   get_last_daily_ci_artifacts(A				,							A				,							A     )

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

   return results



