





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort

class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 # FIXME: add fast tests
 pass


@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCAmelCase							(					unittest.TestCase			):
 @property
 def      A_	(      self  :      str	)			->	Optional[Any]:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )
 @property
 def      A_	(      self  :      Optional[Any]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		ort.SessionOptions()
     lowerCamelCase__    :      int					 =		False
     return options
 def      A_	(      self  :      Optional[Any]	)			->	List[str]:
     lowerCamelCase__    :      Optional[int]					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
     lowerCamelCase__    :      int					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
     lowerCamelCase__    :      List[str]					 =		OnnxStableDiffusionInpaintPipeline.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=UpperCAmelCase			,     feature_extractor=UpperCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		'A red cat sitting on a park bench'

     lowerCamelCase__    :      Dict					 =		np.random.RandomState(0	)
     lowerCamelCase__    :      Optional[int]					 =		pipe(
         prompt=UpperCAmelCase			,     image=UpperCAmelCase			,     mask_image=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=UpperCAmelCase			,     output_type='np'			,     )
     lowerCamelCase__    :      Any					 =		output.images
     lowerCamelCase__    :      Union[str, Any]					 =		images[0, 255:258, 255:258, -1]

     assert images.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Union[str, Any]					 =		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3







 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      Any					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
     lowerCamelCase__    :      Optional[Any]					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
     lowerCamelCase__    :      Tuple					 =		LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
     lowerCamelCase__    :      str					 =		OnnxStableDiffusionInpaintPipeline.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=UpperCAmelCase			,     safety_checker=UpperCAmelCase			,     feature_extractor=UpperCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      str					 =		'A red cat sitting on a park bench'

     lowerCamelCase__    :      Tuple					 =		np.random.RandomState(0	)
     lowerCamelCase__    :      int					 =		pipe(
         prompt=UpperCAmelCase			,     image=UpperCAmelCase			,     mask_image=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=UpperCAmelCase			,     output_type='np'			,     )
     lowerCamelCase__    :      str					 =		output.images
     lowerCamelCase__    :      Tuple					 =		images[0, 255:258, 255:258, -1]

     assert images.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Dict					 =		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3












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				:       Optional[int]	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Dict:
    lowerCamelCase__    :      str					 =		set()
    lowerCamelCase__    :      Any					 =		[]

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

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

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

    return selected_warnings


if __name__ == "__main__":


    def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
        return values.split(',' )

    _UpperCAmelCase				:       List[str]	   =				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				:       Union[str, Any]	   =				parser.parse_args()

    _UpperCAmelCase				:       Dict	   =				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				:       Union[str, Any]	   =				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				:       Dict	   =				extract_warnings(args.output_dir, args.targets)
    _UpperCAmelCase				:       Optional[Any]	   =				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)





