

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def __init__(      self						,   a						,   a=7						,   a=3						,   a=18						,   a=30						,   a=400						,   a=True						,   a=None						,   a=True						,   a=False						,   a=True						,   a=True						,   a=[0.5, 0.5, 0.5]						,   a=[0.5, 0.5, 0.5]						,   ):
         lowercase__    :   Union[str, Any]      =       parent
         lowercase__    :   Dict      =       batch_size
         lowercase__    :   Union[str, Any]      =       num_channels
         lowercase__    :   Optional[Any]      =       image_size
         lowercase__    :   Optional[int]      =       min_resolution
         lowercase__    :   Optional[Any]      =       max_resolution
         lowercase__    :   str      =       do_resize
         lowercase__    :   str      =       size if size is not None else {'height': 18, 'width': 20}
         lowercase__    :   Optional[int]      =       do_thumbnail
         lowercase__    :   Tuple      =       do_align_axis
         lowercase__    :   List[str]      =       do_pad
         lowercase__    :   Tuple      =       do_normalize
         lowercase__    :   Optional[int]      =       image_mean
         lowercase__    :   Any      =       image_std





    def       snake_case_				(      self):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_thumbnail": self.do_thumbnail,
             "do_align_long_axis": self.do_align_axis,
             "do_pad": self.do_pad,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }



@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       List[Any]														=     DonutImageProcessor if is_vision_available() else None





    def       snake_case_				(      self):
         lowercase__    :   str      =       DonutImageProcessingTester(self)





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





    def       snake_case_				(      self):
         lowercase__    :   Any      =       self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(a						,   'do_resize'))
         self.assertTrue(hasattr(a						,   'size'))
         self.assertTrue(hasattr(a						,   'do_thumbnail'))
         self.assertTrue(hasattr(a						,   'do_align_long_axis'))
         self.assertTrue(hasattr(a						,   'do_pad'))
         self.assertTrue(hasattr(a						,   'do_normalize'))
         self.assertTrue(hasattr(a						,   'image_mean'))
         self.assertTrue(hasattr(a						,   'image_std'))





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size						,   {'height': 18, 'width': 20})

         lowercase__    :   List[Any]      =       self.image_processing_class.from_dict(self.image_processor_dict						,   size=42)
         self.assertEqual(image_processor.size						,   {'height': 42, 'width': 42})

         # Previous config had dimensions in (width, height) order
         lowercase__    :   int      =       self.image_processing_class.from_dict(self.image_processor_dict						,   size=(42, 84))
         self.assertEqual(image_processor.size						,   {'height': 84, 'width': 42})





    def       snake_case_				(      self):
         pass





    @is_flaky()
    def       snake_case_				(      self):
         # Initialize image_processing
         lowercase__    :   Any      =       self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__    :   List[str]      =       prepare_image_inputs(self.image_processor_tester						,   equal_resolution=a)
         for image in image_inputs:
              self.assertIsInstance(a						,   Image.Image)

         # Test not batched input
         lowercase__    :   List[str]      =       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.size['height'],
                 self.image_processor_tester.size['width'],
             )						,   )

         # Test batched
         lowercase__    :   List[str]      =       image_processing(a						,   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.size['height'],
                 self.image_processor_tester.size['width'],
             )						,   )





    @is_flaky()
    def       snake_case_				(      self):
         # Initialize image_processing
         lowercase__    :   str      =       self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase__    :   List[str]      =       prepare_image_inputs(self.image_processor_tester						,   equal_resolution=a						,   numpify=a)
         for image in image_inputs:
              self.assertIsInstance(a						,   np.ndarray)

         # Test not batched input
         lowercase__    :   Tuple      =       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.size['height'],
                 self.image_processor_tester.size['width'],
             )						,   )

         # Test batched
         lowercase__    :   Union[str, Any]      =       image_processing(a						,   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.size['height'],
                 self.image_processor_tester.size['width'],
             )						,   )





    @is_flaky()
    def       snake_case_				(      self):
         # Initialize image_processing
         lowercase__    :   int      =       self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase__    :   Dict      =       prepare_image_inputs(self.image_processor_tester						,   equal_resolution=a						,   torchify=a)
         for image in image_inputs:
              self.assertIsInstance(a						,   torch.Tensor)

         # Test not batched input
         lowercase__    :   str      =       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.size['height'],
                 self.image_processor_tester.size['width'],
             )						,   )

         # Test batched
         lowercase__    :   List[str]      =       image_processing(a						,   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.size['height'],
                 self.image_processor_tester.size['width'],
             )						,   )




import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


snake_case_			  = logging.get_logger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       set()
     lowercase__    :   List[str]      =       []

     def parse_line(SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):
          for line in fp:
               if isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[int]      =       line.decode('UTF-8'			)
               if "warnings summary (final)" in line:
                    continue
               # This means we are outside the body of a warning
               elif not line.startswith(' '			):
                    # process a single warning and move it to `selected_warnings`.
                    if len(SCREAMING_SNAKE_CASE_			) > 0:
                         lowercase__    :   Optional[Any]      =       '\n'.join(SCREAMING_SNAKE_CASE_			)
                         # Only keep the warnings specified in `targets`
                         if any(f""": {x}: """ in warning for x in targets			):
                              selected_warnings.add(SCREAMING_SNAKE_CASE_			)
                         buffer.clear()
                    continue
               else:
                    lowercase__    :   Optional[Any]      =       line.strip()
                    buffer.append(SCREAMING_SNAKE_CASE_			)

     if from_gh:
          for filename in os.listdir(SCREAMING_SNAKE_CASE_			):
               lowercase__    :   int      =       os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
               if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                    # read the file
                    if filename != "warnings.txt":
                         continue
                    with open(SCREAMING_SNAKE_CASE_			) as fp:
                         parse_line(SCREAMING_SNAKE_CASE_			)
     else:
          try:
               with zipfile.ZipFile(SCREAMING_SNAKE_CASE_			) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                              # read the file
                              if filename != "warnings.txt":
                                   continue
                              with z.open(SCREAMING_SNAKE_CASE_			) as fp:
                                   parse_line(SCREAMING_SNAKE_CASE_			)
          except Exception:
               logger.warning(
                   f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""			)

     return selected_warnings

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       set()

     lowercase__    :   List[str]      =       [os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			) for p in os.listdir(SCREAMING_SNAKE_CASE_			) if (p.endswith('.zip'			) or from_gh)]
     for p in paths:
          selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)			)

     return selected_warnings


if __name__ == "__main__":

       def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any			):



            '''simple docstring'''





            return values.split(','			)

       snake_case_			  = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
       parser.add_argument(
           '''--output_dir''',
           type=str,
           required=True,
           help='''Where to store the downloaded artifacts and other result files.''',
       )
       parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
       # optional parameters
       parser.add_argument(
           '''--targets''',
           default='''DeprecationWarning,UserWarning,FutureWarning''',
           type=list_str,
           help='''Comma-separated list of target warning(s) which we want to extract.''',
       )
       parser.add_argument(
           '''--from_gh''',
           action='''store_true''',
           help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
       )

       snake_case_			  = parser.parse_args()

       snake_case_			  = args.from_gh
       if from_gh:
              # The artifacts have to be downloaded using `actions/download-artifact@v3`
              pass
       else:
              os.makedirs(args.output_dir, exist_ok=True)

              # get download links
              snake_case_			  = get_artifacts_links(args.workflow_run_id, token=args.token)
              with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
                     json.dump(artifacts, fp, ensure_ascii=False, indent=4)

              # download artifacts
              for idx, (name, url) in enumerate(artifacts.items()):
                     print(name)
                     print(url)
                     print('''=''' * 80)
                     download_artifact(name, url, args.output_dir, args.token)
                     # Be gentle to GitHub
                     time.sleep(1)

    # extract warnings from artifacts
       snake_case_			  = extract_warnings(args.output_dir, args.targets)
       snake_case_			  = sorted(selected_warnings)
       with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
              json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

