






'''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


__A    	=   version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       tuple  ,		A  :       Path  ,		A  :       List[Any]  ,		A  :       List[str]  ,		A  :       List[str]  ,		A  :       Dict  ,		A  :       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        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       str  ,		A  :       int  ,		A  :       bool = False  )    -> int:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      __snake_case				:      Union[str, Any]     							= 'cuda'
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('`float16` model export is only supported on GPUs with CUDA'  )
   else:
      __snake_case				:      int     							= 'cpu'
   __snake_case				:      Any     							= StableDiffusionPipeline.from_pretrained(A  ,		torch_dtype=A  ).to(A  )
   __snake_case				:      Optional[int]     							= Path(A  )

   # TEXT ENCODER
   __snake_case				:      Any     							= pipeline.text_encoder.config.max_position_embeddings
   __snake_case				:      Tuple     							= pipeline.text_encoder.config.hidden_size
   __snake_case				:      str     							= 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
   __snake_case				:      List[str]     							= pipeline.unet.config.in_channels
   __snake_case				:      str     							= pipeline.unet.config.sample_size
   __snake_case				:      List[str]     							= 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  ,		)
   __snake_case				:      Optional[int]     							= str(unet_path.absolute().as_posix()  )
   __snake_case				:      List[Any]     							= os.path.dirname(A  )
   __snake_case				:      List[str]     							= 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
   __snake_case				:      Any     							= pipeline.vae
   __snake_case				:      Optional[Any]     							= vae_encoder.config.in_channels
   __snake_case				:      List[Any]     							= vae_encoder.config.sample_size
   # need to get the raw tensor output (sample) from the encoder
   __snake_case				:      Dict     							= 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
   __snake_case				:      List[str]     							= pipeline.vae
   __snake_case				:      Any     							= vae_decoder.config.latent_channels
   __snake_case				:      Optional[Any]     							= vae_decoder.config.out_channels
   # forward only through the decoder part
   __snake_case				:      List[str]     							= 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:
      __snake_case				:      int     							= pipeline.safety_checker
      __snake_case				:      List[str]     							= safety_checker.config.vision_config.num_channels
      __snake_case				:      str     							= safety_checker.config.vision_config.image_size
      __snake_case				:      Tuple     							= 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
      __snake_case				:      Any     							= OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'  )
      __snake_case				:      Optional[Any]     							= pipeline.feature_extractor
   else:
      __snake_case				:      List[str]     							= None
      __snake_case				:      List[str]     							= None

   __snake_case				:      Dict     							= 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
   __snake_case				:      Any     							= OnnxStableDiffusionPipeline.from_pretrained(A  ,		provider='CPUExecutionProvider'  )
   print('ONNX pipeline is loadable'  )


if __name__ == "__main__":
      __A    	=   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''')

      __A    	=   parser.parse_args()

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







'''simple docstring'''






from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload




class a_    :







   def __init__(self					,   __a = None)			->      None:
      """simple docstring"""






      if components is None:
         __snake_case				:      List[str]     							= []
      __snake_case				:      Optional[int]     							= list(__a)







   def __len__(self)			->      int:
      """simple docstring"""






      return len(self.__components)







   def __str__(self)			->      str:
      """simple docstring"""






      return "(" + ",".join(map(__a					,   self.__components)) + ")"







   def __add__(self					,   __a)			->      Vector:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= len(self)
      if size == len(__a):
         __snake_case				:      Optional[int]     							= [self.__components[i] + other.component(__a) for i in range(__a)]
         return Vector(__a)
      else:
         raise Exception('must have the same size')







   def __sub__(self					,   __a)			->      Vector:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= len(self)
      if size == len(__a):
         __snake_case				:      Optional[int]     							= [self.__components[i] - other.component(__a) for i in range(__a)]
         return Vector(__a)
      else:  # error case
         raise Exception('must have the same size')







   @overload
   def __mul__(self					,   __a)			->      Vector:
      """simple docstring"""






      ...







   @overload
   def __mul__(self					,   __a)			->      float:
      """simple docstring"""






      ...







   def __mul__(self					,   __a)			->      float | Vector:
      """simple docstring"""






      if isinstance(__a					,   (float, int)):
         __snake_case				:      str     							= [c * other for c in self.__components]
         return Vector(__a)
      elif isinstance(__a					,   __a) and len(self) == len(__a):
         __snake_case				:      List[Any]     							= len(self)
         __snake_case				:      Dict     							= [self.__components[i] * other.component(__a) for i in range(__a)]
         return sum(__a)
      else:  # error case
         raise Exception('invalid operand!')







   def     SCREAMING_SNAKE_CASE__					(self)			->      Vector:
      """simple docstring"""






      return Vector(self.__components)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      float:
      """simple docstring"""






      if isinstance(__a					,   __a) and -len(self.__components) <= i < len(self.__components):
         return self.__components[i]
      else:
         raise Exception('index out of range')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      None:
      """simple docstring"""






      assert -len(self.__components) <= pos < len(self.__components)
      __snake_case				:      int     							= value







   def     SCREAMING_SNAKE_CASE__					(self)			->      float:
      """simple docstring"""






      if len(self.__components) == 0:
         raise Exception('Vector is empty')
      __snake_case				:      Tuple     							= [c**2 for c in self.__components]
      return math.sqrt(sum(__a))







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = False)			->      float:
      """simple docstring"""






      __snake_case				:      Tuple     							= self * other
      __snake_case				:      Optional[int]     							= self.euclidean_length() * other.euclidean_length()
      if deg:
         return math.degrees(math.acos(num / den))
      else:
         return math.acos(num / den)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Vector:







   """simple docstring"""
   assert isinstance(A  ,		A  )
   return Vector([0] * dimension  )




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  )    -> Vector:







   """simple docstring"""
   assert isinstance(A  ,		A  ) and (isinstance(A  ,		A  ))
   __snake_case				:      Any     							= [0] * dimension
   __snake_case				:      int     							= 1
   return Vector(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       Vector  ,		A  :       Vector  )    -> Vector:







   """simple docstring"""
   assert (
       isinstance(A  ,		A  )
       and isinstance(A  ,		A  )
       and (isinstance(A  ,		(int, float)  ))
   )
   return x * scalar + y




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  ,		A  :       int  )    -> Vector:







   """simple docstring"""
   random.seed(A  )
   __snake_case				:      List[Any]     							= [random.randint(A  ,		A  ) for _ in range(A  )]
   return Vector(A  )




class a_    :







   def __init__(self					,   __a					,   __a					,   __a)			->      None:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= matrix
      __snake_case				:      int     							= w
      __snake_case				:      str     							= h







   def __str__(self)			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= ''
      for i in range(self.__height):
         ans += "|"
         for j in range(self.__width):
            if j < self.__width - 1:
               ans += str(self.__matrix[i][j]) + ","
            else:
               ans += str(self.__matrix[i][j]) + "|\n"
      return ans







   def __add__(self					,   __a)			->      Matrix:
      """simple docstring"""






      if self.__width == other.width() and self.__height == other.height():
         __snake_case				:      Tuple     							= []
         for i in range(self.__height):
            __snake_case				:      List[Any]     							= [
                self.__matrix[i][j] + other.component(__a					,   __a)
                for j in range(self.__width)
            ]
            matrix.append(__a)
         return Matrix(__a					,   self.__width					,   self.__height)
      else:
         raise Exception('matrix must have the same dimension!')







   def __sub__(self					,   __a)			->      Matrix:
      """simple docstring"""






      if self.__width == other.width() and self.__height == other.height():
         __snake_case				:      str     							= []
         for i in range(self.__height):
            __snake_case				:      List[str]     							= [
                self.__matrix[i][j] - other.component(__a					,   __a)
                for j in range(self.__width)
            ]
            matrix.append(__a)
         return Matrix(__a					,   self.__width					,   self.__height)
      else:
         raise Exception('matrices must have the same dimension!')







   @overload
   def __mul__(self					,   __a)			->      Matrix:
      """simple docstring"""






      ...







   @overload
   def __mul__(self					,   __a)			->      Vector:
      """simple docstring"""






      ...







   def __mul__(self					,   __a)			->      Vector | Matrix:
      """simple docstring"""






      if isinstance(__a					,   __a):  # matrix-vector
         if len(__a) == self.__width:
            __snake_case				:      Tuple     							= zero_vector(self.__height)
            for i in range(self.__height):
               __snake_case				:      Union[str, Any]     							= [
                   self.__matrix[i][j] * other.component(__a)
                   for j in range(self.__width)
               ]
               ans.change_component(__a					,   sum(__a))
            return ans
         else:
            raise Exception(
                'vector must have the same size as the '
                'number of columns of the matrix!')
      elif isinstance(__a					,   (int, float)):  # matrix-scalar
         __snake_case				:      str     							= [
             [self.__matrix[i][j] * other for j in range(self.__width)]
             for i in range(self.__height)
         ]
         return Matrix(__a					,   self.__width					,   self.__height)
      return None







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return self.__height







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return self.__width







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if 0 <= x < self.__height and 0 <= y < self.__width:
         return self.__matrix[x][y]
      else:
         raise Exception('change_component: indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      None:
      """simple docstring"""






      if 0 <= x < self.__height and 0 <= y < self.__width:
         __snake_case				:      List[Any]     							= value
      else:
         raise Exception('change_component: indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      __snake_case				:      List[Any]     							= self.__matrix[:x] + self.__matrix[x + 1 :]
      for i in range(len(__a)):
         __snake_case				:      Tuple     							= minor[i][:y] + minor[i][y + 1 :]
      return Matrix(__a					,   self.__width - 1					,   self.__height - 1).determinant()







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      if 0 <= x < self.__height and 0 <= y < self.__width:
         return (-1) ** (x + y) * self.minor(__a					,   __a)
      else:
         raise Exception('Indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      if self.__height < 1:
         raise Exception('Matrix has no element')
      elif self.__height == 1:
         return self.__matrix[0][0]
      elif self.__height == 2:
         return (
             self.__matrix[0][0] * self.__matrix[1][1]
             - self.__matrix[0][1] * self.__matrix[1][0]
         )
      else:
         __snake_case				:      Any     							= [
             self.__matrix[0][y] * self.cofactor(0					,   __a) for y in range(self.__width)
         ]
         return sum(__a)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Matrix:







   """simple docstring"""
   __snake_case				:      list[list[float]]     							= [[0] * n for _ in range(A  )]
   return Matrix(A  ,		A  ,		A  )





def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  ,		A  :       int  ,		A  :       int  )    -> Matrix:







   """simple docstring"""
   random.seed(A  )
   __snake_case				:      list[list[float]]     							= [
       [random.randint(A  ,		A  ) for _ in range(A  )] for _ in range(A  )
   ]
   return Matrix(A  ,		A  ,		A  )