
"""simple docstring"""





import argparse
import datetime



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    {
					    '''0''': '''Sunday''',
					    '''1''': '''Monday''',
					    '''2''': '''Tuesday''',
					    '''3''': '''Wednesday''',
					    '''4''': '''Thursday''',
					    '''5''': '''Friday''',
					    '''6''': '''Saturday''',
					}

					_UpperCamelCase									=    {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

					# Validate
					if not 0 < len(__snake_case      ) < 11:
										raise ValueError('''Must be 10 characters long'''      )

					# Get month
					_UpperCamelCase									=    int(date_input[0] + date_input[1]      )
					# Validate
					if not 0 < m < 13:
										raise ValueError('''Month must be between 1 - 12'''      )

					_UpperCamelCase									=    date_input[2]
					# Validate
					if sep_a not in ["-", "/"]:
										raise ValueError('''Date separator must be \'-\' or \'/\''''      )

					# Get day
					_UpperCamelCase									=    int(date_input[3] + date_input[4]      )
					# Validate
					if not 0 < d < 32:
										raise ValueError('''Date must be between 1 - 31'''      )

					# Get second separator
					_UpperCamelCase									=    date_input[5]
					# Validate
					if sep_a not in ["-", "/"]:
										raise ValueError('''Date separator must be \'-\' or \'/\''''      )

					# Get year
					_UpperCamelCase									=    int(date_input[6] + date_input[7] + date_input[8] + date_input[9]      )
					# Arbitrary year range
					if not 45 < y < 85_00:
										raise ValueError(
										    '''Year out of range. There has to be some sort of limit...right?'''      )

					# Get datetime obj for validation
					_UpperCamelCase									=    datetime.date(int(__snake_case      ),	int(__snake_case      ),	int(__snake_case      )      )

					# Start math
					if m <= 2:
										_UpperCamelCase									=    y - 1
										_UpperCamelCase									=    m + 12
					# maths var
					_UpperCamelCase									=    int(str(__snake_case      )[:2]      )
					_UpperCamelCase									=    int(str(__snake_case      )[2:]      )
					_UpperCamelCase									=    int(2.6 * m - 5.39      )
					_UpperCamelCase									=    int(c / 4      )
					_UpperCamelCase									=    int(k / 4      )
					_UpperCamelCase									=    int(d + k      )
					_UpperCamelCase									=    int(t + u + v + x      )
					_UpperCamelCase									=    int(z - (2 * c)      )
					_UpperCamelCase									=    round(w % 7      )
					# End math

					# Validate math
					if f != convert_datetime_days[dt_ck.weekday()]:
										raise AssertionError('''The date was evaluated incorrectly. Contact developer.'''      )

					# Response
					_UpperCamelCase									=    F'''Your date {date_input}, is a {days[str(__snake_case      )]}!'''
					return response


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				_a								=						argparse.ArgumentParser(
				    description=(
				        """Find out what day of the week nearly any date is or was. Enter """
				        """date as a string in the mm-dd-yyyy or mm/dd/yyyy format"""
				    )
				)
				parser.add_argument(
				    """date_input""", type=str, help="""Date as a string (mm-dd-yyyy or mm/dd/yyyy)"""
				)
				_a								=						parser.parse_args()
				zeller(args.date_input)








import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase				=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=False							,   ):
       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	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= 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__":
   __lowerCamelCase				=			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""")

   __lowerCamelCase				=			parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print("""SD: Done: ONNX""")





