





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				XLMTokenizer
				__A      =				False


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

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase	 =[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								lowerCamelCase	 =dict(zip(A__   , range(len(A__							)							)							)							)
								lowerCamelCase	 =["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								lowerCamelCase	 =os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase	 =os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["""merges_file"""]							)
								with open(self.vocab_file   , """w"""							) as fp:
												fp.write(json.dumps(A__							)							)
								with open(self.merges_file   , """w"""							) as fp:
												fp.write("""\n""".join(A__							)							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 ="""lower newer"""
								lowerCamelCase	 ="""lower newer"""
								return input_text, output_text


				def _snake_case       (		self							):
								lowerCamelCase	 =XLMTokenizer(self.vocab_file   , self.merges_file							)

								lowerCamelCase	 ="""lower"""
								lowerCamelCase	 =["""low""", """er</w>"""]
								lowerCamelCase	 =tokenizer.tokenize(A__							)
								self.assertListEqual(A__   , A__							)

								lowerCamelCase	 =tokens + ["""<unk>"""]
								lowerCamelCase	 =[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A__							)   , A__							)


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""							)

								lowerCamelCase	 =tokenizer.encode("""sequence builders"""   , add_special_tokens=A__							)
								lowerCamelCase	 =tokenizer.encode("""multi-sequence build"""   , add_special_tokens=A__							)

								lowerCamelCase	 =tokenizer.build_inputs_with_special_tokens(A__							)
								lowerCamelCase	 =tokenizer.build_inputs_with_special_tokens(A__   , A__							)

								assert encoded_sentence == [0] + text + [1]
								assert encoded_pair == [0] + text + [1] + text_a + [1]












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''')





