
'''simple docstring'''







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 :  List[Any]    							=       version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: tuple      ,      __SCREAMING_SNAKE_CASE					: Path      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=False      ,      ):



						"""simple docstring"""





						output_path.parent.mkdir(parents=__SCREAMING_SNAKE_CASE      ,      exist_ok=__SCREAMING_SNAKE_CASE							)
						# 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(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      f=output_path.as_posix()      ,      input_names=__SCREAMING_SNAKE_CASE      ,      output_names=__SCREAMING_SNAKE_CASE      ,      dynamic_axes=__SCREAMING_SNAKE_CASE      ,      do_constant_folding=__SCREAMING_SNAKE_CASE      ,      use_external_data_format=__SCREAMING_SNAKE_CASE      ,      enable_onnx_checker=__SCREAMING_SNAKE_CASE      ,      opset_version=__SCREAMING_SNAKE_CASE      ,      )
						else:
												export(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      f=output_path.as_posix()      ,      input_names=__SCREAMING_SNAKE_CASE      ,      output_names=__SCREAMING_SNAKE_CASE      ,      dynamic_axes=__SCREAMING_SNAKE_CASE      ,      do_constant_folding=__SCREAMING_SNAKE_CASE      ,      opset_version=__SCREAMING_SNAKE_CASE      ,      )
@torch.no_grad()
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: bool = False							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												lowercase_	:     Tuple													=       '''cuda'''
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''							)
						else:
												lowercase_	:     Any													=       '''cpu'''
						lowercase_	:     List[Any]													=       StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      ,      torch_dtype=__SCREAMING_SNAKE_CASE							).to(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     int													=       Path(__SCREAMING_SNAKE_CASE							)

						# TEXT ENCODER
						lowercase_	:     Any													=       pipeline.text_encoder.config.max_position_embeddings
						lowercase_	:     Union[str, Any]													=       pipeline.text_encoder.config.hidden_size
						lowercase_	:     Any													=       pipeline.tokenizer(
						    '''A sample prompt'''      ,      padding='''max_length'''      ,      max_length=pipeline.tokenizer.model_max_length      ,      truncation=__SCREAMING_SNAKE_CASE      ,      return_tensors='''pt'''      ,      )
						onnx_export(
						    pipeline.text_encoder      ,      model_args=(text_input.input_ids.to(device=__SCREAMING_SNAKE_CASE      ,      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=__SCREAMING_SNAKE_CASE      ,      )
						del pipeline.text_encoder

						# UNET
						lowercase_	:     str													=       pipeline.unet.config.in_channels
						lowercase_	:     str													=       pipeline.unet.config.sample_size
						lowercase_	:     List[Any]													=       output_path / '''unet''' / '''model.onnx'''
						onnx_export(
						    pipeline.unet      ,      model_args=(
						        torch.randn(2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        torch.randn(2							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        torch.randn(2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        False,
						    )      ,      output_path=__SCREAMING_SNAKE_CASE      ,      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=__SCREAMING_SNAKE_CASE      ,      use_external_data_format=__SCREAMING_SNAKE_CASE      ,      )
						lowercase_	:     str													=       str(unet_path.absolute().as_posix()							)
						lowercase_	:     Optional[Any]													=       os.path.dirname(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     List[str]													=       onnx.load(__SCREAMING_SNAKE_CASE							)
						# clean up existing tensor files
						shutil.rmtree(__SCREAMING_SNAKE_CASE							)
						os.mkdir(__SCREAMING_SNAKE_CASE							)
						# collate external tensor files into one
						onnx.save_model(
						    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      save_as_external_data=__SCREAMING_SNAKE_CASE      ,      all_tensors_to_one_file=__SCREAMING_SNAKE_CASE      ,      location='''weights.pb'''      ,      convert_attribute=__SCREAMING_SNAKE_CASE      ,      )
						del pipeline.unet

						# VAE ENCODER
						lowercase_	:     Optional[int]													=       pipeline.vae
						lowercase_	:     Optional[Any]													=       vae_encoder.config.in_channels
						lowercase_	:     int													=       vae_encoder.config.sample_size
						# need to get the raw tensor output (sample) from the encoder
						lowercase_	:     Union[str, Any]													=       lambda __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							: vae_encoder.encode(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)[0].sample()
						onnx_export(
						    __SCREAMING_SNAKE_CASE      ,      model_args=(
						        torch.randn(1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        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=__SCREAMING_SNAKE_CASE      ,      )

						# VAE DECODER
						lowercase_	:     List[Any]													=       pipeline.vae
						lowercase_	:     Any													=       vae_decoder.config.latent_channels
						lowercase_	:     str													=       vae_decoder.config.out_channels
						# forward only through the decoder part
						lowercase_	:     Dict													=       vae_encoder.decode
						onnx_export(
						    __SCREAMING_SNAKE_CASE      ,      model_args=(
						        torch.randn(1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        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=__SCREAMING_SNAKE_CASE      ,      )
						del pipeline.vae

						# SAFETY CHECKER
						if pipeline.safety_checker is not None:
												lowercase_	:     Dict													=       pipeline.safety_checker
												lowercase_	:     List[Any]													=       safety_checker.config.vision_config.num_channels
												lowercase_	:     Tuple													=       safety_checker.config.vision_config.image_size
												lowercase_	:     Tuple													=       safety_checker.forward_onnx
												onnx_export(
												    pipeline.safety_checker      ,      model_args=(
												        torch.randn(
												            1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      ).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
												        torch.randn(1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
												    )      ,      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=__SCREAMING_SNAKE_CASE      ,      )
												del pipeline.safety_checker
												lowercase_	:     List[str]													=       OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''							)
												lowercase_	:     Optional[int]													=       pipeline.feature_extractor
						else:
												lowercase_	:     int													=       None
												lowercase_	:     str													=       None

						lowercase_	:     Optional[int]													=       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=__SCREAMING_SNAKE_CASE      ,      feature_extractor=__SCREAMING_SNAKE_CASE      ,      requires_safety_checker=safety_checker is not None      ,      )

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

						del pipeline
						del onnx_pipeline
						lowercase_	:     Tuple													=       OnnxStableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      ,      provider='''CPUExecutionProvider'''							)
						print('''ONNX pipeline is loadable'''							)


if __name__ == "__main__":
							_lowercase :  Optional[int]    							=       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 :  Union[str, Any]    							=       parser.parse_args()

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




"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




