





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


lowercase    : Tuple    	=  logging.get_logger(__name__)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						set()
       _UpperCamelCase              =						[]

       def parse_line(lowerCAmelCase					):
              for line in fp:
                     if isinstance(lowerCAmelCase    ,       lowerCAmelCase					):
                            _UpperCamelCase              =						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(lowerCAmelCase					) > 0:
                                   _UpperCamelCase              =						'''\n'''.join(lowerCAmelCase					)
                                   # Only keep the warnings specified in `targets`
                                   if any(f''': {x}: ''' in warning for x in targets					):
                                          selected_warnings.add(lowerCAmelCase					)
                                   buffer.clear()
                            continue
                     else:
                            _UpperCamelCase              =						line.strip()
                            buffer.append(lowerCAmelCase					)

       if from_gh:
              for filename in os.listdir(lowerCAmelCase					):
                     _UpperCamelCase              =						os.path.join(lowerCAmelCase    ,       lowerCAmelCase					)
                     if not os.path.isdir(lowerCAmelCase					):
                            # read the file
                            if filename != "warnings.txt":
                                   continue
                            with open(lowerCAmelCase					) as fp:
                                   parse_line(lowerCAmelCase					)
       else:
              try:
                     with zipfile.ZipFile(lowerCAmelCase					) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(lowerCAmelCase					):
                                          # read the file
                                          if filename != "warnings.txt":
                                                 continue
                                          with z.open(lowerCAmelCase					) as fp:
                                                 parse_line(lowerCAmelCase					)
              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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						set()

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

       return selected_warnings


if __name__ == "__main__":


       def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
              return values.split(''','''					)

       lowercase    : Union[str, Any]    	=  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.""",
       )

       lowercase    : List[str]    	=  parser.parse_args()

       lowercase    : Union[str, Any]    	=  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
              lowercase    : List[str]    	=  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
       lowercase    : Any    	=  extract_warnings(args.output_dir, args.targets)
       lowercase    : List[str]    	=  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)













import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard

@slow
@require_flax
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            super().tearDown()
            gc.collect()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                '''stabilityai/stable-diffusion-2''',       revision='''bf16''',       dtype=jnp.bfloataa,       )

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''stabilityai/stable-diffusion-2'''
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxDPMSolverMultistepScheduler.from_pretrained(A,       subfolder='''scheduler'''      )
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                A,       scheduler=A,       revision='''bf16''',       dtype=jnp.bfloataa,       )
            _UpperCamelCase              =						scheduler_params

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2






