




"""simple docstring"""

import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				List[Any]	   =		{}

	A_  :				Dict	   =		job['''started_at''']
	A_  :				Optional[Any]	   =		job['''completed_at''']

	A_  :				Optional[Any]	   =		date_parser.parse(_UpperCAmelCase	)
	A_  :				str	   =		date_parser.parse(_UpperCAmelCase	)

	A_  :				Union[str, Any]	   =		round((end_datetime - start_datetime).total_seconds() / 60.0	)

	A_  :				Optional[int]	   =		start
	A_  :				Optional[int]	   =		end
	A_  :				Any	   =		duration_in_min

	return job_info

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=None	):




	"""simple docstring"""






	A_  :				List[Any]	   =		None
	if token is not None:
		A_  :				Dict	   =		{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

	A_  :				List[str]	   =		f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
	A_  :				Tuple	   =		requests.get(_UpperCAmelCase  ,      headers=_UpperCAmelCase	).json()
	A_  :				Optional[int]	   =		{}

	try:
		job_time.update({job['''name''']: extract_time_from_single_job(_UpperCAmelCase	) for job in result['''jobs''']}	)
		A_  :				int	   =		math.ceil((result['''total_count'''] - 100) / 100	)

		for i in range(_UpperCAmelCase	):
			A_  :				Any	   =		requests.get(url + f"""&page={i + 2}"""  ,      headers=_UpperCAmelCase	).json()
			job_time.update({job['''name''']: extract_time_from_single_job(_UpperCAmelCase	) for job in result['''jobs''']}	)

		return job_time
	except Exception:
		print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""	)

	return {}


if __name__ == "__main__":
		_lowerCamelCase  :			Optional[int]        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
		_lowerCamelCase  :			Union[str, Any]        =       parser.parse_args()

		_lowerCamelCase  :			Union[str, Any]        =       get_job_time(args.workflow_run_id)
		_lowerCamelCase  :			List[Any]        =       dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

		for k, v in job_time.items():
				print(f'{k}: {v["duration"]}')











"""simple docstring"""

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		_lowerCamelCase  :			Optional[int]        =       argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
		)
		# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
		parser.add_argument(
		    '--original_config_file',
		    default=None,
		    type=str,
		    help='The YAML config file corresponding to the original architecture.',
		)
		parser.add_argument(
		    '--num_in_channels',
		    default=None,
		    type=int,
		    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
		)
		parser.add_argument(
		    '--scheduler_type',
		    default='pndm',
		    type=str,
		    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
		)
		parser.add_argument(
		    '--pipeline_type',
		    default=None,
		    type=str,
		    help=(
		        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
		        '. If `None` pipeline will be automatically inferred.'
		    ),
		)
		parser.add_argument(
		    '--image_size',
		    default=None,
		    type=int,
		    help=(
		        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
		        ' Base. Use 768 for Stable Diffusion v2.'
		    ),
		)
		parser.add_argument(
		    '--prediction_type',
		    default=None,
		    type=str,
		    help=(
		        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
		        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
		    ),
		)
		parser.add_argument(
		    '--extract_ema',
		    action='store_true',
		    help=(
		        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
		        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
		        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
		    ),
		)
		parser.add_argument(
		    '--upcast_attention',
		    action='store_true',
		    help=(
		        'Whether the attention computation should always be upcasted. This is necessary when running stable'
		        ' diffusion 2.1.'
		    ),
		)
		parser.add_argument(
		    '--from_safetensors',
		    action='store_true',
		    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
		)
		parser.add_argument(
		    '--to_safetensors',
		    action='store_true',
		    help='Whether to store pipeline in safetensors format or not.',
		)
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
		parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
		parser.add_argument(
		    '--stable_unclip',
		    type=str,
		    default=None,
		    required=False,
		    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
		)
		parser.add_argument(
		    '--stable_unclip_prior',
		    type=str,
		    default=None,
		    required=False,
		    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
		)
		parser.add_argument(
		    '--clip_stats_path',
		    type=str,
		    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
		    required=False,
		)
		parser.add_argument(
		    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
		)
		parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
		parser.add_argument(
		    '--vae_path',
		    type=str,
		    default=None,
		    required=False,
		    help='Set to a path, hub id to an already converted vae to not convert it again.',
		)
		_lowerCamelCase  :			Optional[Any]        =       parser.parse_args()

		_lowerCamelCase  :			Optional[Any]        =       download_from_original_stable_diffusion_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    prediction_type=args.prediction_type,
		    model_type=args.pipeline_type,
		    extract_ema=args.extract_ema,
		    scheduler_type=args.scheduler_type,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    stable_unclip=args.stable_unclip,
		    stable_unclip_prior=args.stable_unclip_prior,
		    clip_stats_path=args.clip_stats_path,
		    controlnet=args.controlnet,
		    vae_path=args.vae_path,
		)

		if args.half:
				pipe.to(torch_dtype=torch.floataa)

		if args.controlnet:
				# only save the controlnet model
				pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
		else:
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





