





from __future__ import annotations

class       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase ,  lowerCamelCase	 =text, pattern
								lowerCamelCase ,  lowerCamelCase	 =len(UpperCAmelCase_							), len(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):

								for i in range(self.patLen - 1   , -1   , -1							):
												if char == self.pattern[i]:
																return i
								return -1


				def _snake_case       (		self   , UpperCAmelCase_							):

								for i in range(self.patLen - 1   , -1   , -1							):
												if self.pattern[i] != self.text[current_pos + i]:
																return current_pos + i
								return -1


				def _snake_case       (		self							):
								# searches pattern in text and returns index positions
								lowerCamelCase	 =[]
								for i in range(self.textLen - self.patLen + 1							):
												lowerCamelCase	 =self.mismatch_in_text(UpperCAmelCase_							)
												if mismatch_index == -1:
																positions.append(UpperCAmelCase_							)
												else:
																lowerCamelCase	 =self.match_in_pattern(self.text[mismatch_index]							)
																lowerCamelCase	 =(
																    mismatch_index - match_index
																)  # shifting index lgtm [py/multiple-definition]
								return positions




UpperCAmelCase__						:			Union[str, Any]									='''ABAABA'''
UpperCAmelCase__						:			Optional[int]									='''AB'''
UpperCAmelCase__						:			Any									=BoyerMooreSearch(text, pattern)
UpperCAmelCase__						:			int									=bms.bad_character_heuristic()

if len(positions) == 0:
							print('''No match found''')
else:
							print('''Pattern found in following positions: ''')
							print(positions)












import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# 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(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				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	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        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=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=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__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





