




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
       UpperCAmelCase_									=	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.',
       )
       UpperCAmelCase_									=	parser.parse_args()

       UpperCAmelCase_									=	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)







import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


UpperCAmelCase_									=	TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('', '|', '|'),
    datarow=DataRow('', '|', '|'),
    padding=1,
    with_header_hide=None,
)


UpperCAmelCase_									=	[]
UpperCAmelCase_									=	[]

UpperCAmelCase_									=	{'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}

UpperCAmelCase_									=	[
    {
        'type': 'header',
        'text': {
            'type': 'plain_text',
            'text': f"""🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results""",
            'emoji': True,
        },
    }
]

UpperCAmelCase_									=	0
for log in Path().glob('*.log'):
       UpperCAmelCase_									=	0
       with open(log, 'r') as f:
              for line in f:
                     UpperCAmelCase_									=	json.loads(line)
                     if line.get('nodeid', '') != "":
                            UpperCAmelCase_									=	line['nodeid']
                            if line.get('duration', None) is not None:
                                   UpperCAmelCase_									=	f"""{line["duration"]:.4f}"""
                                   if line.get('outcome', '') == "failed":
                                          section_num_failed += 1
                                          failed.append([test, duration, log.name.split('_')[0]])
                                          total_num_failed += 1
       group_info.append([str(log), section_num_failed, failed])
       UpperCAmelCase_									=	[]
       log.unlink()

UpperCAmelCase_									=	''
UpperCAmelCase_									=	[]
if total_num_failed > 0:
       for name, num_failed, failed_tests in group_info:
              if num_failed > 0:
                     if num_failed == 1:
                            message += f"*{name[1:]}: {num_failed} failed test*\n"
                     else:
                            message += f"*{name[1:]}: {num_failed} failed tests*\n"
                     UpperCAmelCase_									=	[]
                     UpperCAmelCase_									=	{}
                     for test in failed_tests:
                            UpperCAmelCase_									=	test[0].split('::')
                            UpperCAmelCase_									=	data[0].split('/')[-1]
                            if data[0] not in filesafailed:
                                   UpperCAmelCase_									=	[data[1:]]
                            else:
                                   filesafailed[data[0]] += [data[1:]]
                            failed_table.append(data)

                     UpperCAmelCase_									=	[test[0] for test in failed_table]
                     UpperCAmelCase_									=	list(set(files))
                     # Count number of instances in failed_tests
                     UpperCAmelCase_									=	[]
                     for file in individual_files:
                            table.append([file, len(filesafailed[file])])

                     UpperCAmelCase_									=	tabulate(
                         table,
                         headers=['Test Location', 'Num Failed'],
                         tablefmt=hf_table_format,
                         stralign='right',
                     )
                     message += f"\n```\n{failed_table}\n```"
                     all_filesafailed.append(filesafailed)
       if len(message) > 3_000:
              UpperCAmelCase_									=	'Too many failed tests, please see the full report in the Action results.'
              UpperCAmelCase_									=	len(err) + 10
              UpperCAmelCase_									=	message[: 3_000 - offset] + f"""\n...\n```\n{err}"""
       print(f"""### {message}""")
else:
       UpperCAmelCase_									=	'No failed tests! 🤗'
       print(f"""## {message}""")
       payload.append(no_error_payload)

if os.environ.get('TEST_TYPE', '') != "":
       from slack_sdk import WebClient

       UpperCAmelCase_									=	WebClient(token=os.environ['SLACK_API_TOKEN'])
       if message != "No failed tests! 🤗":
              UpperCAmelCase_									=	{
                  'type': 'section',
                  'text': {
                      'type': 'mrkdwn',
                      'text': message,
                  },
              }
              payload.append(md_report)
              UpperCAmelCase_									=	{
                  'type': 'section',
                  'text': {
                      'type': 'mrkdwn',
                      'text': '*For more details:*',
                  },
                  'accessory': {
                      'type': 'button',
                      'text': {
                          'type': 'plain_text',
                          'text': 'Check Action results',
                          'emoji': True,
                      },
                      'url': f"""https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
                  },
              }
              payload.append(action_button)
              UpperCAmelCase_									=	{
                  'type': 'context',
                  'elements': [
                      {
                          'type': 'plain_text',
                          'text': f"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
                      }
                  ],
              }
              payload.append(date_report)
       UpperCAmelCase_									=	client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
       UpperCAmelCase_									=	response.data['ts']
       for failed_file in all_filesafailed:
              for test_location, test_failures in failed_file.items():
                     # Keep only the first instance of the test name
                     UpperCAmelCase_									=	''
                     for i, row in enumerate(test_failures):
                            if row[0] != test_class:
                                   UpperCAmelCase_									=	row[0]
                            else:
                                   UpperCAmelCase_									=	''

                     UpperCAmelCase_									=	{
                         'type': 'section',
                         'text': {
                             'type': 'mrkdwn',
                             'text': f"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```""",
                         },
                     }

                     client.chat_postMessage(
                         channel='#accelerate-ci-daily',
                         thread_ts=ts,
                         blocks=[payload],
                     )

