




"""simple docstring"""
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a_				:    int										=     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





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

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


if __name__ == "__main__":
					a_				:    str										=     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''')

					a_				:    List[str]										=     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"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_barthez import BarthezTokenizer
else:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

a_				:    List[Any]										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_				:    int										=     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Union[str, Any]     		= ['input_ids', 'attention_mask']
					__a						:						Optional[Any]     		= BarthezTokenizer
					def __init__(    self						,	__a=None						,	__a=None						,	__a="<s>"						,	__a="</s>"						,	__a="</s>"						,	__a="<s>"						,	__a="<unk>"						,	__a="<pad>"						,	__a="<mask>"						,	**__a						,	):
								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase					:	Optional[Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else mask_token

								super().__init__(
								    __a						,	tokenizer_file=__a						,	bos_token=__a						,	eos_token=__a						,	unk_token=__a						,	sep_token=__a						,	cls_token=__a						,	pad_token=__a						,	mask_token=__a						,	**__a						,	)

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	int	       =				[self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep     ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]





					def     snake_case_				(    self						,	__a						,	__a = None     ):
								if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'     )

								if not os.path.isdir(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[Any]	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(__a     ):
											copyfile(self.vocab_file						,	__a     )

								return (out_vocab_file,)



