



from collections.abc import Generator
from math import sin





def      __snake_case	(			_UpperCAmelCase				):
    if len(_UpperCAmelCase				) != 32:
        raise ValueError('''Input must be of length 32'''				)

    __a						       =				b''''''
    for i in [3, 2, 1, 0]:
        little_endian += string_aa[8 * i : 8 * i + 8]
    return little_endian





def      __snake_case	(			_UpperCAmelCase				):
    if i < 0:
        raise ValueError('''Input must be non-negative'''				)

    __a						       =				format(_UpperCAmelCase   , '''08x'''				)[-8:]
    __a						       =				b''''''
    for i in [3, 2, 1, 0]:
        little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''				)
    return little_endian_hex





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				b''''''
    for char in message:
        bit_string += format(_UpperCAmelCase   , '''08b'''				).encode('''utf-8'''				)
    __a						       =				format(len(_UpperCAmelCase				)   , '''064b'''				).encode('''utf-8'''				)

    # Pad bit_string to a multiple of 512 chars
    bit_string += b"1"
    while len(_UpperCAmelCase				) % 512 != 448:
        bit_string += b"0"
    bit_string += to_little_endian(start_len[32:]				) + to_little_endian(start_len[:32]				)

    return bit_string





def      __snake_case	(			_UpperCAmelCase				):
    if len(_UpperCAmelCase				) % 512 != 0:
        raise ValueError('''Input must have length that\'s a multiple of 512'''				)

    for pos in range(0   , len(_UpperCAmelCase				)   , 512				):
        __a						       =				bit_string[pos : pos + 512]
        __a						       =				[]
        for i in range(0   , 512   , 32				):
            block_words.append(int(to_little_endian(block[i : i + 32]				)   , 2				)				)
        yield block_words





def      __snake_case	(			_UpperCAmelCase				):
    if i < 0:
        raise ValueError('''Input must be non-negative'''				)

    __a						       =				format(_UpperCAmelCase   , '''032b'''				)
    __a						       =				''''''
    for c in i_str:
        new_str += "1" if c == "0" else "0"
    return int(_UpperCAmelCase   , 2				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    return (a + b) % 2**32





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    if i < 0:
        raise ValueError('''Input must be non-negative'''				)
    if shift < 0:
        raise ValueError('''Shift must be non-negative'''				)
    return ((i << shift) ^ (i >> (32 - shift))) % 2**32





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				preprocess(_UpperCAmelCase				)

    __a						       =				[int(2**32 * abs(sin(i + 1				)				)				) for i in range(64				)]

    # Starting states
    __a						       =				0X67_452_301
    __a						       =				0Xef_cda_b89
    __a						       =				0X98_bad_cfe
    __a						       =				0X10_325_476

    __a						       =				[
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
    ]

    # Process bit string in chunks, each with 16 32-char words
    for block_words in get_block_words(_UpperCAmelCase				):
        __a						       =				aa
        __a						       =				ba
        __a						       =				ca
        __a						       =				da

        # Hash current chunk
        for i in range(64				):
            if i <= 15:
                # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                __a						       =				d ^ (b & (c ^ d))
                __a						       =				i
            elif i <= 31:
                # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                __a						       =				c ^ (d & (b ^ c))
                __a						       =				(5 * i + 1) % 16
            elif i <= 47:
                __a						       =				b ^ c ^ d
                __a						       =				(3 * i + 5) % 16
            else:
                __a						       =				c ^ (b | not_aa(_UpperCAmelCase				))
                __a						       =				(7 * i) % 16
            __a						       =				(f + a + added_consts[i] + block_words[g]) % 2**32
            __a						       =				d
            __a						       =				c
            __a						       =				b
            __a						       =				sum_aa(_UpperCAmelCase   , left_rotate_aa(_UpperCAmelCase   , shift_amounts[i]				)				)

        # Add hashed chunk to running total
        __a						       =				sum_aa(_UpperCAmelCase   , _UpperCAmelCase				)
        __a						       =				sum_aa(_UpperCAmelCase   , _UpperCAmelCase				)
        __a						       =				sum_aa(_UpperCAmelCase   , _UpperCAmelCase				)
        __a						       =				sum_aa(_UpperCAmelCase   , _UpperCAmelCase				)

    __a						       =				reformat_hex(_UpperCAmelCase				) + reformat_hex(_UpperCAmelCase				) + reformat_hex(_UpperCAmelCase				) + reformat_hex(_UpperCAmelCase				)
    return digest


if __name__ == "__main__":
 import doctest

 doctest.testmod()










import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """simple docstring"""






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




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       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:
        A :       Any          =   """cpu"""
    A :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   vae_decoder.decode
    onnx_export(
        _lowerCAmelCase      ,     model_args=(
            torch.randn(1      ,     _lowerCAmelCase      ,     25      ,     25		).to(device=_lowerCAmelCase      ,     dtype=_lowerCAmelCase		),
            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=_lowerCAmelCase      ,     )
    del vae_decoder


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Tuple             =						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_:Tuple             =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("""SD: Done: ONNX""")


