




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 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				tuple      ,      SCREAMING_SNAKE_CASE_							:				Path      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				str=False      ,      ):
					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 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				bool = False     ):
					__lowerCAmelCase				   =       torch.floataa if fpaa else torch.floataa
					if fpaa and torch.cuda.is_available():
										__lowerCAmelCase				   =       "cuda"
					elif fpaa and not torch.cuda.is_available():
										raise ValueError("`float16` model export is only supported on GPUs with CUDA"     )
					else:
										__lowerCAmelCase				   =       "cpu"
					__lowerCAmelCase				   =       StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      ,      torch_dtype=SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       Path(SCREAMING_SNAKE_CASE_     )

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

					__lowerCAmelCase				   =       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
					__lowerCAmelCase				   =       OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      ,      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=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""")

					UpperCamelCase__    			=			parser.parse_args()

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






from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
