"""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


UpperCAmelCase__  		=						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase=False   ,):







  """simple docstring"""
  output_path.parent.mkdir(parents=lowercase   ,exist_ok=lowercase		)
  # 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(
        lowercase   ,lowercase   ,f=output_path.as_posix()   ,input_names=lowercase   ,output_names=lowercase   ,dynamic_axes=lowercase   ,do_constant_folding=lowercase   ,use_external_data_format=lowercase   ,enable_onnx_checker=lowercase   ,opset_version=lowercase   ,)
  else:
    export(
        lowercase   ,lowercase   ,f=output_path.as_posix()   ,input_names=lowercase   ,output_names=lowercase   ,dynamic_axes=lowercase   ,do_constant_folding=lowercase   ,opset_version=lowercase   ,)






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase = False		):







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

  # TEXT ENCODER
  _UpperCAmelCase						 =      pipeline.text_encoder.config.max_position_embeddings
  _UpperCAmelCase						 =      pipeline.text_encoder.config.hidden_size
  _UpperCAmelCase						 =      pipeline.tokenizer(
      """A sample prompt"""   ,padding="""max_length"""   ,max_length=pipeline.tokenizer.model_max_length   ,truncation=lowercase   ,return_tensors="""pt"""   ,)
  onnx_export(
      pipeline.text_encoder   ,model_args=(text_input.input_ids.to(device=lowercase   ,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=lowercase   ,)
  del pipeline.text_encoder

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

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

  # VAE DECODER
  _UpperCAmelCase						 =      pipeline.vae
  _UpperCAmelCase						 =      vae_decoder.config.latent_channels
  _UpperCAmelCase						 =      vae_decoder.config.out_channels
  # forward only through the decoder part
  _UpperCAmelCase						 =      vae_encoder.decode
  onnx_export(
      lowercase   ,model_args=(
          torch.randn(1   ,lowercase   ,lowercase   ,lowercase		).to(device=lowercase   ,dtype=lowercase		),
          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=lowercase   ,)
  del pipeline.vae

  # SAFETY CHECKER
  if pipeline.safety_checker is not None:
    _UpperCAmelCase						 =      pipeline.safety_checker
    _UpperCAmelCase						 =      safety_checker.config.vision_config.num_channels
    _UpperCAmelCase						 =      safety_checker.config.vision_config.image_size
    _UpperCAmelCase						 =      safety_checker.forward_onnx
    onnx_export(
        pipeline.safety_checker   ,model_args=(
            torch.randn(
                1   ,lowercase   ,lowercase   ,lowercase   ,).to(device=lowercase   ,dtype=lowercase		),
            torch.randn(1   ,lowercase   ,lowercase   ,lowercase		).to(device=lowercase   ,dtype=lowercase		),
        )   ,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=lowercase   ,)
    del pipeline.safety_checker
    _UpperCAmelCase						 =      OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""		)
    _UpperCAmelCase						 =      pipeline.feature_extractor
  else:
    _UpperCAmelCase						 =      None
    _UpperCAmelCase						 =      None

  _UpperCAmelCase						 =      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=lowercase   ,feature_extractor=lowercase   ,requires_safety_checker=safety_checker is not None   ,)

  onnx_pipeline.save_pretrained(lowercase		)
  print("""ONNX pipeline saved to"""   ,lowercase		)

  del pipeline
  del onnx_pipeline
  _UpperCAmelCase						 =      OnnxStableDiffusionPipeline.from_pretrained(lowercase   ,provider="""CPUExecutionProvider"""		)
  print("""ONNX pipeline is loadable"""		)


if __name__ == "__main__":
 UpperCAmelCase__  		=						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""")

 UpperCAmelCase__  		=						parser.parse_args()

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

"""simple docstring"""


import unittest

from knapsack import knapsack as k


class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)

      _UpperCAmelCase						 =      [60]
      _UpperCAmelCase						 =      [10]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      [1, 2, 3]
      _UpperCAmelCase						 =      [3, 2, 1]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				5					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      50
      _UpperCAmelCase						 =      [60, 100, 120]
      _UpperCAmelCase						 =      [10, 20, 30]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				220					)


if __name__ == "__main__":
    unittest.main()
