





import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				RoFormerTokenizer
				__A      =				RoFormerTokenizerFast
				__A      =				True
				__A      =				True


				def _snake_case       (		self							):
								super().setUp()


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""   , **UpperCAmelCase_							)


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""   , **UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""永和服装饰品有限公司,今天天气非常好"""
								lowerCamelCase	 ="""永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
								return input_text, output_text


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()
								lowerCamelCase ,  lowerCamelCase	 =self.get_chinese_input_output_texts()
								lowerCamelCase	 =tokenizer.tokenize(UpperCAmelCase_							)

								self.assertListEqual(UpperCAmelCase_   , output_text.split()							)

								lowerCamelCase	 =tokens + [tokenizer.unk_token]
								lowerCamelCase	 =[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_rust_tokenizer()
								lowerCamelCase ,  lowerCamelCase	 =self.get_chinese_input_output_texts()
								lowerCamelCase	 =tokenizer.tokenize(UpperCAmelCase_							)
								self.assertListEqual(UpperCAmelCase_   , output_text.split()							)
								lowerCamelCase	 =tokens + [tokenizer.unk_token]
								lowerCamelCase	 =[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								pass












import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =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__":
							UpperCAmelCase__						:			Union[str, 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=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''')

							UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





