
'''simple docstring'''
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


lowercase__											=logging.get_logger(__name__)

lowercase__											={'vocab_file': 'spiece.model'}

lowercase__											={
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowercase__											={'bert_for_seq_generation': 5_12}







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Optional[int]       =    VOCAB_FILES_NAMES
			lowerCamelCase__	:			int       =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__	:			Optional[int]       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__	:			List[int]       =    []
			lowerCamelCase__	:			str       =    ['input_ids', 'attention_mask']




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase="<s>"					,   UpperCAmelCase="</s>"					,   UpperCAmelCase="<unk>"					,   UpperCAmelCase="<pad>"					,   UpperCAmelCase="<::::>"					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				a_      				=						{} if sp_model_kwargs is None else sp_model_kwargs

				# Add extra_ids to the special token list
				super().__init__(
				    bos_token=UpperCAmelCase					,   eos_token=UpperCAmelCase					,   unk_token=UpperCAmelCase					,   pad_token=UpperCAmelCase					,   sep_token=UpperCAmelCase					,   sp_model_kwargs=self.sp_model_kwargs					,   **UpperCAmelCase					,   )

				a_      				=						vocab_file

				a_      				=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(UpperCAmelCase	)




			@property
			def 					lowerCAmelCase__					(       self	):
				return self.sp_model.get_piece_size()




			def 					lowerCAmelCase__					(       self	):
				a_      				=						{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	):
				a_      				=						self.__dict__.copy()
				a_      				=						None
				return state




			def __setstate__(       self					,   UpperCAmelCase	):
				a_      				=						d

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

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				return self.sp_model.encode(UpperCAmelCase					,   out_type=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				return self.sp_model.piece_to_id(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.sp_model.IdToPiece(UpperCAmelCase	)
				return token




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						[]
				a_      				=						""""""
				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
						a_      				=						[]
					else:
						current_sub_tokens.append(UpperCAmelCase	)
				out_string += self.sp_model.decode(UpperCAmelCase	)
				return out_string.strip()






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None	):
				if not os.path.isdir(UpperCAmelCase	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
				a_      				=						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:
						a_      				=						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


lowercase__											=version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__=False  ,      ):
	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__  ,      A__  ,      A__  ,      A__ = False						):
	a_      				=						torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		a_      				=						"""cuda"""
	elif fpaa and not torch.cuda.is_available():
		raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""						)
	else:
		a_      				=						"""cpu"""
	a_      				=						Path(A__						)

	# VAE DECODER
	a_      				=						AutoencoderKL.from_pretrained(model_path + """/vae"""						)
	a_      				=						vae_decoder.config.latent_channels
	# forward only through the decoder part
	a_      				=						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__":
		lowercase__											=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')

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