





'''simple docstring'''





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       UpperCAmelCase							(     _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  Optional[Any]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					set()
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[]

						def parse_line(_lowerCamelCase      :  Any     ):
												for line in fp:
																		if isinstance(_lowerCamelCase			,   _lowerCamelCase     ):
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					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:
																														SCREAMING_SNAKE_CASE__     :				Any   =					"\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:
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					line.strip()
																								buffer.append(_lowerCamelCase     )

						if from_gh:
												for filename in os.listdir(_lowerCamelCase     ):
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					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       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[str]   =					set()

						SCREAMING_SNAKE_CASE__     :				Tuple   =					[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       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]     ):





										'''simple docstring'''


										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 FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe.prepare_image_inputs([canny_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe.prepare_image_inputs([pose_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2