import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_:   str   =      logging.get_logger(__name__)


A_:   Any   =      '''▁'''

A_:   Any   =      {'''vocab_file''': '''spiece.model'''}

A_:   int   =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

A_:   Union[str, Any]   =      {
    '''google/reformer-crime-and-punishment''': 52_4288,
}


class _lowercase       (							_UpperCAmelCase    ):





							"""simple docstring"""




							lowerCAmelCase__							=       VOCAB_FILES_NAMES
							lowerCAmelCase__							=       PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__							=       ['input_ids', 'attention_mask']



							def __init__(     self  , UpperCAmelCase  , UpperCAmelCase="</s>"  , UpperCAmelCase="<unk>"  , UpperCAmelCase=[]  , UpperCAmelCase = None  , **UpperCAmelCase  , ):







														'''simple docstring'''






														_lowercase					=						{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    eos_token=UpperCamelCase_  , unk_token=UpperCamelCase_  , additional_special_tokens=UpperCamelCase_  , sp_model_kwargs=self.sp_model_kwargs  , **UpperCamelCase_  , )

														_lowercase					=						vocab_file
														_lowercase					=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
														self.sp_model.Load(UpperCamelCase_	)



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														return self.sp_model.get_piece_size()



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						{self.convert_ids_to_tokens(UpperCamelCase_	): i for i in range(self.vocab_size	)}
														vocab.update(self.added_tokens_encoder	)
														return vocab



							def __getstate__(     self	):







														'''simple docstring'''






														_lowercase					=						self.__dict__.copy()
														_lowercase					=						None
														return state



							def __setstate__(     self  , UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						d

														# for backward compatibility
														if not hasattr(self  , """sp_model_kwargs"""	):
																					_lowercase					=						{}

														_lowercase					=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
														self.sp_model.Load(self.vocab_file	)



							def      _UpperCAmelCase							(     self  , UpperCAmelCase	):







														'''simple docstring'''






														return self.sp_model.encode(UpperCamelCase_  , out_type=UpperCamelCase_	)



							def      _UpperCAmelCase							(     self  , UpperCAmelCase	):







														'''simple docstring'''






														return self.sp_model.piece_to_id(UpperCamelCase_	)



							def      _UpperCAmelCase							(     self  , UpperCAmelCase	):







														'''simple docstring'''






														if index < self.sp_model.get_piece_size():
																					_lowercase					=						self.sp_model.IdToPiece(UpperCamelCase_	)
														return token



							def      _UpperCAmelCase							(     self  , UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						[]
														_lowercase					=						""""""
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(UpperCamelCase_	) + token
																												_lowercase					=						[]
																					else:
																												current_sub_tokens.append(UpperCamelCase_	)
														out_string += self.sp_model.decode(UpperCamelCase_	)
														return out_string.strip()



							def      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase = None	):







														'''simple docstring'''






														if not os.path.isdir(UpperCamelCase_	):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
																					return
														_lowercase					=						os.path.join(
														    UpperCamelCase_  , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

														if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCamelCase_	) and os.path.isfile(self.vocab_file	):
																					copyfile(self.vocab_file  , UpperCamelCase_	)
														elif not os.path.isfile(self.vocab_file	):
																					with open(UpperCamelCase_  , """wb"""	) as fi:
																												_lowercase					=						self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase_	)

														return (out_vocab_file,)









'''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[int]          =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




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

	# VAE DECODER
	snake_case__	       =   AutoencoderKL.from_pretrained(model_path + '/vae'							)
	snake_case__	       =   vae_decoder.config.latent_channels
	# forward only through the decoder part
	snake_case__	       =   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__":
			a__					:			Optional[int]          =     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''')



