

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_UpperCAmelCase	:     Union[str, Any]		       = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=False     ,							):







							'''simple docstring'''






							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   ):







							'''simple docstring'''






							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__":
					_UpperCAmelCase	:     List[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	:     str		       = parser.parse_args()
					print(args.output_path)
					convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
					print("""SD: Done: ONNX""")







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




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

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											snake_case_           =					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', 'l à</w>']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['merges_file'] )

											with open(self.vocab_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		for token in vocab_tokens:
																									fp.write(F'''{token} {vocab_tokens[token]}\n''' )
											with open(self.merges_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		fp.write('\n'.join(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case )   ,     snake_case )




