

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			):
  """simple docstring"""






  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[str]=3	,							lowerCAmelCase			:   List[Any]=18	,							lowerCAmelCase			:   Dict=30	,							lowerCAmelCase			:   Optional[Any]=4_00	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=None	,							lowerCAmelCase			:   Dict=True	,							lowerCAmelCase			:   Dict=False	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   List[str]=[0.5, 0.5, 0.5]	,							lowerCAmelCase			:   Optional[int]=[0.5, 0.5, 0.5]	,							)							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						parent
     __lowerCAmelCase  :      Optional[Any]    =						batch_size
     __lowerCAmelCase  :      str    =						num_channels
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      List[Any]    =						min_resolution
     __lowerCAmelCase  :      Union[str, Any]    =						max_resolution
     __lowerCAmelCase  :      Union[str, Any]    =						do_resize
     __lowerCAmelCase  :      str    =						size if size is not None else {'''height''': 18, '''width''': 20}
     __lowerCAmelCase  :      List[str]    =						do_thumbnail
     __lowerCAmelCase  :      str    =						do_align_axis
     __lowerCAmelCase  :      Optional[Any]    =						do_pad
     __lowerCAmelCase  :      Tuple    =						do_normalize
     __lowerCAmelCase  :      List[Any]    =						image_mean
     __lowerCAmelCase  :      List[str]    =						image_std







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[Any]:






     """simple docstring"""







     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     (  lowerCamelCase__       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Tuple										=DonutImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						DonutImageProcessingTester(self   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Any:






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""size"""   )   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""do_thumbnail"""   )   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""do_align_long_axis"""   )   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""do_pad"""   )   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""do_normalize"""   )   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""image_mean"""   )   )
     self.assertTrue(hasattr(__lowerCamelCase	,							"""image_std"""   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size	,							{"""height""": 18, """width""": 20}   )

     __lowerCAmelCase  :      int    =						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
     __lowerCAmelCase  :      Union[str, Any]    =						self.image_processing_class.from_dict(self.image_processor_dict	,							size=(42, 84)   )
     self.assertEqual(image_processor.size	,							{"""height""": 84, """width""": 42}   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @is_flaky()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Dict    =						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  :      Optional[Any]    =						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
     __lowerCAmelCase  :      Optional[int]    =						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.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )	,							)







  @is_flaky()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowerCAmelCase  :      Any    =						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  :      Union[str, Any]    =						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
     __lowerCAmelCase  :      int    =						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.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )	,							)







  @is_flaky()
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowerCAmelCase  :      int    =						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  :      Any    =						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
     __lowerCAmelCase  :      Dict    =						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.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


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     int:
   __lowerCAmelCase  :      str    =						set()
   __lowerCAmelCase  :      int    =						[]

   def parse_line(__A					:						List[Any]					):
      for line in fp:
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      str    =						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(__A					) > 0:
               __lowerCAmelCase  :      Tuple    =						"""\n""".join(__A					)
               # Only keep the warnings specified in `targets`
               if any(f''': {x}: ''' in warning for x in targets					):
                  selected_warnings.add(__A					)
               buffer.clear()
            continue
         else:
            __lowerCAmelCase  :      Optional[int]    =						line.strip()
            buffer.append(__A					)

   if from_gh:
      for filename in os.listdir(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						os.path.join(__A ,       __A					)
         if not os.path.isdir(__A					):
            # read the file
            if filename != "warnings.txt":
               continue
            with open(__A					) as fp:
               parse_line(__A					)
   else:
      try:
         with zipfile.ZipFile(__A					) as z:
            for filename in z.namelist():
               if not os.path.isdir(__A					):
                  # read the file
                  if filename != "warnings.txt":
                     continue
                  with z.open(__A					) as fp:
                     parse_line(__A					)
      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_				(__A					:						Dict ,       __A					:						Union[str, Any]					)      ->     Dict:
   __lowerCAmelCase  :      Any    =						set()

   __lowerCAmelCase  :      Optional[int]    =						[os.path.join(__A ,       __A					) for p in os.listdir(__A					) if (p.endswith(""".zip"""					) or from_gh)]
   for p in paths:
      selected_warnings.update(extract_warnings_from_single_artifact(__A ,       __A					)					)

   return selected_warnings


if __name__ == "__main__":





   def      snake_case_				(__A					:						int					)      ->     Tuple:
      return values.split(""","""					)

   __UpperCAmelCase								=			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.""",
   )

   __UpperCAmelCase								=			parser.parse_args()

   __UpperCAmelCase								=			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
      __UpperCAmelCase								=			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
   __UpperCAmelCase								=			extract_warnings(args.output_dir, args.targets)
   __UpperCAmelCase								=			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)


