

import argparse
import os
import shutil
from pathlib import Path

import onnx
import torch
from packaging import version
from torch.onnx import export

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


lowercase_									=   version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def      a      (			A__	:   List[str]      ,   A__	:   tuple      ,   A__	:   Path      ,   A__	:   Optional[int]      ,   A__	:   Optional[Any]      ,   A__	:   Dict      ,   A__	:   List[str]      ,   A__	:   Union[str, Any]=False      ,   )    ->      Dict:





      """simple docstring"""



      output_path.parent.mkdir(parents=A__      ,   exist_ok=A__     )
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                A__      ,   A__      ,   f=output_path.as_posix()      ,   input_names=A__      ,   output_names=A__      ,   dynamic_axes=A__      ,   do_constant_folding=A__      ,   use_external_data_format=A__      ,   enable_onnx_checker=A__      ,   opset_version=A__      ,   )
      else:
            export(
                A__      ,   A__      ,   f=output_path.as_posix()      ,   input_names=A__      ,   output_names=A__      ,   dynamic_axes=A__      ,   do_constant_folding=A__      ,   opset_version=A__      ,   )
@torch.no_grad()
def      a      (			A__	:   str      ,   A__	:   str      ,   A__	:   int      ,   A__	:   bool = False     )    ->      Any:





      """simple docstring"""



      _lowercase	       =torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            _lowercase	       ='cuda'
      elif fpaa and not torch.cuda.is_available():
            raise ValueError('`float16` model export is only supported on GPUs with CUDA'     )
      else:
            _lowercase	       ='cpu'
      _lowercase	       =StableDiffusionPipeline.from_pretrained(A__      ,   torch_dtype=A__     ).to(A__     )
      _lowercase	       =Path(A__     )

      # TEXT ENCODER
      _lowercase	       =pipeline.text_encoder.config.max_position_embeddings
      _lowercase	       =pipeline.text_encoder.config.hidden_size
      _lowercase	       =pipeline.tokenizer(
          'A sample prompt'      ,   padding='max_length'      ,   max_length=pipeline.tokenizer.model_max_length      ,   truncation=A__      ,   return_tensors='pt'      ,   )
      onnx_export(
          pipeline.text_encoder      ,   model_args=(text_input.input_ids.to(device=A__      ,   dtype=torch.intaa     ))      ,   output_path=output_path / 'text_encoder' / 'model.onnx'      ,   ordered_input_names=['input_ids']      ,   output_names=['last_hidden_state', 'pooler_output']      ,   dynamic_axes={
              'input_ids': {0: 'batch', 1: 'sequence'},
          }      ,   opset=A__      ,   )
      del pipeline.text_encoder

      # UNET
      _lowercase	       =pipeline.unet.config.in_channels
      _lowercase	       =pipeline.unet.config.sample_size
      _lowercase	       =output_path / 'unet' / 'model.onnx'
      onnx_export(
          pipeline.unet      ,   model_args=(
              torch.randn(2      ,   A__      ,   A__      ,   A__     ).to(device=A__      ,   dtype=A__     ),
              torch.randn(2     ).to(device=A__      ,   dtype=A__     ),
              torch.randn(2      ,   A__      ,   A__     ).to(device=A__      ,   dtype=A__     ),
              False,
          )      ,   output_path=A__      ,   ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict']      ,   output_names=['out_sample']      ,   dynamic_axes={
              'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
              'timestep': {0: 'batch'},
              'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
          }      ,   opset=A__      ,   use_external_data_format=A__      ,   )
      _lowercase	       =str(unet_path.absolute().as_posix()     )
      _lowercase	       =os.path.dirname(A__     )
      _lowercase	       =onnx.load(A__     )
      # clean up existing tensor files
      shutil.rmtree(A__     )
      os.mkdir(A__     )
      # collate external tensor files into one
      onnx.save_model(
          A__      ,   A__      ,   save_as_external_data=A__      ,   all_tensors_to_one_file=A__      ,   location='weights.pb'      ,   convert_attribute=A__      ,   )
      del pipeline.unet

      # VAE ENCODER
      _lowercase	       =pipeline.vae
      _lowercase	       =vae_encoder.config.in_channels
      _lowercase	       =vae_encoder.config.sample_size
      # need to get the raw tensor output (sample) from the encoder
      _lowercase	       =lambda A__      ,   A__     : vae_encoder.encode(A__      ,   A__     )[0].sample()
      onnx_export(
          A__      ,   model_args=(
              torch.randn(1      ,   A__      ,   A__      ,   A__     ).to(device=A__      ,   dtype=A__     ),
              False,
          )      ,   output_path=output_path / 'vae_encoder' / 'model.onnx'      ,   ordered_input_names=['sample', 'return_dict']      ,   output_names=['latent_sample']      ,   dynamic_axes={
              'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
          }      ,   opset=A__      ,   )

      # VAE DECODER
      _lowercase	       =pipeline.vae
      _lowercase	       =vae_decoder.config.latent_channels
      _lowercase	       =vae_decoder.config.out_channels
      # forward only through the decoder part
      _lowercase	       =vae_encoder.decode
      onnx_export(
          A__      ,   model_args=(
              torch.randn(1      ,   A__      ,   A__      ,   A__     ).to(device=A__      ,   dtype=A__     ),
              False,
          )      ,   output_path=output_path / 'vae_decoder' / 'model.onnx'      ,   ordered_input_names=['latent_sample', 'return_dict']      ,   output_names=['sample']      ,   dynamic_axes={
              'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
          }      ,   opset=A__      ,   )
      del pipeline.vae

      # SAFETY CHECKER
      if pipeline.safety_checker is not None:
            _lowercase	       =pipeline.safety_checker
            _lowercase	       =safety_checker.config.vision_config.num_channels
            _lowercase	       =safety_checker.config.vision_config.image_size
            _lowercase	       =safety_checker.forward_onnx
            onnx_export(
                pipeline.safety_checker      ,   model_args=(
                    torch.randn(
                        1      ,   A__      ,   A__      ,   A__      ,   ).to(device=A__      ,   dtype=A__     ),
                    torch.randn(1      ,   A__      ,   A__      ,   A__     ).to(device=A__      ,   dtype=A__     ),
                )      ,   output_path=output_path / 'safety_checker' / 'model.onnx'      ,   ordered_input_names=['clip_input', 'images']      ,   output_names=['out_images', 'has_nsfw_concepts']      ,   dynamic_axes={
                    'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
                    'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
                }      ,   opset=A__      ,   )
            del pipeline.safety_checker
            _lowercase	       =OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'     )
            _lowercase	       =pipeline.feature_extractor
      else:
            _lowercase	       =None
            _lowercase	       =None

      _lowercase	       =OnnxStableDiffusionPipeline(
          vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder'     )      ,   vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder'     )      ,   text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder'     )      ,   tokenizer=pipeline.tokenizer      ,   unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet'     )      ,   scheduler=pipeline.scheduler      ,   safety_checker=A__      ,   feature_extractor=A__      ,   requires_safety_checker=safety_checker is not None      ,   )

      onnx_pipeline.save_pretrained(A__     )
      print('ONNX pipeline saved to'      ,   A__     )

      del pipeline
      del onnx_pipeline
      _lowercase	       =OnnxStableDiffusionPipeline.from_pretrained(A__      ,   provider='CPUExecutionProvider'     )
      print('ONNX pipeline is loadable'     )


if __name__ == "__main__":
  lowercase_									=   argparse.ArgumentParser()

  parser.add_argument(
      '--model_path',
      type=str,
      required=True,
      help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
  )

  parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')

  parser.add_argument(
      '--opset',
      default=1_4,
      type=int,
      help='The version of the ONNX operator set to use.',
  )
  parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')

  lowercase_									=   parser.parse_args()

  convert_models(args.model_path, args.output_path, args.opset, args.fpaa)





import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def      a      (			A__	:   Dict     )    ->      str:





      """simple docstring"""



      _lowercase	       ={}

      _lowercase	       =job['started_at']
      _lowercase	       =job['completed_at']

      _lowercase	       =date_parser.parse(A__     )
      _lowercase	       =date_parser.parse(A__     )

      _lowercase	       =round((end_datetime - start_datetime).total_seconds() / 60.0     )

      _lowercase	       =start
      _lowercase	       =end
      _lowercase	       =duration_in_min

      return job_info
def      a      (			A__	:   Dict      ,   A__	:   str=None     )    ->      Tuple:





      """simple docstring"""



      _lowercase	       =None
      if token is not None:
            _lowercase	       ={'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

      _lowercase	       =F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
      _lowercase	       =requests.get(A__      ,   headers=A__     ).json()
      _lowercase	       ={}

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

            for i in range(A__     ):
                  _lowercase	       =requests.get(url + F'''&page={i + 2}'''      ,   headers=A__     ).json()
                  job_time.update({job['name']: extract_time_from_single_job(A__     ) 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__":
  lowercase_									=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
  lowercase_									=   parser.parse_args()

  lowercase_									=   get_job_time(args.workflow_run_id)
  lowercase_									=   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']}")


