



'''simple docstring'''

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_A				:    Optional[Any]     						=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')







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







@torch.no_grad()
def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase = False					) ->			List[str]:
	_lowercase					:       Optional[Any]       =		torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		_lowercase					:       int       =		'cuda'
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('`float16` model export is only supported on GPUs with CUDA'					)
	else:
		_lowercase					:       List[str]       =		'cpu'
	_lowercase					:       Optional[Any]       =		Path(_lowercase					)

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


if __name__ == "__main__":
		_A				:    List[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=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				:    int     						=parser.parse_args()
		print(args.output_path)
		convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
		print('''SD: Done: ONNX''')





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

	# one length string
	_lowercase					:       Union[str, Any]       =		0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			_lowercase					:       Any       =		single_char_strings[ch]
			_lowercase					:       int       =		my_str / all_sum
			my_fir_sum += prob * math.loga(_lowercase					)  # entropy formula.

    # print entropy
	print(f'''{round(-1 * my_fir_sum					):.1f}'''					)

	# two len string
	_lowercase					:       str       =		sum(two_char_strings.values()					)
	_lowercase					:       str       =		0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			_lowercase					:       Optional[Any]       =		cha + cha
			if sequence in two_char_strings:
				_lowercase					:       int       =		two_char_strings[sequence]
				_lowercase					:       Optional[int]       =		int(_lowercase					) / all_sum
				my_sec_sum += prob * math.loga(_lowercase					)

    # print second entropy
	print(f'''{round(-1 * my_sec_sum					):.1f}'''					)

	# print the difference between them
	print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'''					)







def 						__UpperCamelCase				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       List[Any]       =		Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0,				len(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
		main()
