"""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


SCREAMING_SNAKE_CASE__:List[str]			=	version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a ,  a=False ,  ):
       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 					_lowerCamelCase(					a ,  a ,  a ,  a = False							):
       __a     				=  torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              __a     				=  "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"							)
       else:
              __a     				=  "cpu"
       __a     				=  StableDiffusionPipeline.from_pretrained(a ,  torch_dtype=a							).to(a							)
       __a     				=  Path(a							)

       # TEXT ENCODER
       __a     				=  pipeline.text_encoder.config.max_position_embeddings
       __a     				=  pipeline.text_encoder.config.hidden_size
       __a     				=  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
       __a     				=  pipeline.unet.config.in_channels
       __a     				=  pipeline.unet.config.sample_size
       __a     				=  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 ,  )
       __a     				=  str(unet_path.absolute().as_posix()							)
       __a     				=  os.path.dirname(a							)
       __a     				=  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
       __a     				=  pipeline.vae
       __a     				=  vae_encoder.config.in_channels
       __a     				=  vae_encoder.config.sample_size
       # need to get the raw tensor output (sample) from the encoder
       __a     				=  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
       __a     				=  pipeline.vae
       __a     				=  vae_decoder.config.latent_channels
       __a     				=  vae_decoder.config.out_channels
       # forward only through the decoder part
       __a     				=  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:
              __a     				=  pipeline.safety_checker
              __a     				=  safety_checker.config.vision_config.num_channels
              __a     				=  safety_checker.config.vision_config.image_size
              __a     				=  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
              __a     				=  OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"							)
              __a     				=  pipeline.feature_extractor
       else:
              __a     				=  None
              __a     				=  None

       __a     				=  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
       __a     				=  OnnxStableDiffusionPipeline.from_pretrained(a ,  provider="CPUExecutionProvider"							)
       print("ONNX pipeline is loadable"							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]			=	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=14,
       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""")

   SCREAMING_SNAKE_CASE__:List[str]			=	parser.parse_args()

   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
"""simple docstring"""



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     __a     				=  mf_knapsack(i - 1 ,  a ,  a ,  a							)
              else:
                     __a     				=  max(
                         mf_knapsack(i - 1 ,  a ,  a ,  a							) ,  mf_knapsack(i - 1 ,  a ,  a ,  j - wt[i - 1]							) + val[i - 1] ,  )
              __a     				=  val
       return f[i][j]



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  [[0] * (w + 1) for _ in range(n + 1							)]

       for i in range(1 ,  n + 1							):
              for w_ in range(1 ,  w + 1							):
                     if wt[i - 1] <= w_:
                            __a     				=  max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,  dp[i - 1][w_]							)
                     else:
                            __a     				=  dp[i - 1][w_]

       return dp[n][w_], dp



def 					_lowerCamelCase(					a ,  a ,  a							):
       if not (isinstance(a ,  (list, tuple)							) and isinstance(a ,  (list, tuple)							)):
              raise ValueError(
                  "Both the weights and values vectors must be either lists or tuples"							)

       __a     				=  len(a							)
       if num_items != len(a							):
              __a     				=  (
                  "The number of weights must be the same as the number of values.\n"
                  F"But got {num_items} weights and {len(a							)} values"
              )
              raise ValueError(a							)
       for i in range(a							):
              if not isinstance(wt[i] ,  a							):
                     __a     				=  (
                         "All weights must be integers but got weight of "
                         F"type {type(wt[i]							)} at index {i}"
                     )
                     raise TypeError(a							)

       __a				, __a     				=  knapsack(a ,  a ,  a ,  a							)
       __a     				=  set()
       _construct_solution(a ,  a ,  a ,  a ,  a							)

       return optimal_val, example_optional_set



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a							):
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(a ,  a ,  i - 1 ,  a ,  a							)
              else:
                     optimal_set.add(a							)
                     _construct_solution(a ,  a ,  i - 1 ,  j - wt[i - 1] ,  a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Tuple			=	[3, 2, 4, 4]
   SCREAMING_SNAKE_CASE__:List[str]			=	[4, 3, 2, 3]
   SCREAMING_SNAKE_CASE__:List[str]			=	4
   SCREAMING_SNAKE_CASE__:List[str]			=	6
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[int]			=	knapsack(w, wt, val, n)
   print(optimal_solution)
   print(mf_knapsack(n, wt, val, w))  # switched the n and w

   # testing the dynamic programming problem with example
   # the optimal subset for the above example are items 3 and 4
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[Any]			=	knapsack_with_example_solution(w, wt, val)
   assert optimal_solution == 8
   assert optimal_subset == {3, 4}
   print("""optimal_value = """, optimal_solution)
   print("""An optimal subset corresponding to the optimal value""", optimal_subset)
