


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase					:       Dict			    =     get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
    from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCamelCase					:       Optional[Any]			    =     25_0004
UpperCamelCase					:       Dict			    =     25_0020



@require_sentencepiece
@require_tokenizers
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		MBartaaTokenizer
  _lowercase									=		MBartaaTokenizerFast
  _lowercase									=		True
  _lowercase									=		True

  def    _UpperCamelCase( self			:  str       ):
        super().setUp()

        # We have a SentencePiece fixture for testing
        a__	:  Dict     =   MBartaaTokenizer(lowerCamelCase__				,				src_lang="en_XX"				,				tgt_lang="ro_RO"				,				keep_accents=lowerCamelCase__       )
        tokenizer.save_pretrained(self.tmpdirname       )

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  List[str]     =   "<s>"
        a__	:  Union[str, Any]     =   0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__       )				,				lowerCamelCase__       )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__       )				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[str]     =   list(self.get_tokenizer().get_vocab().keys()       )

        self.assertEqual(vocab_keys[0]				,				"<s>"       )
        self.assertEqual(vocab_keys[1]				,				"<pad>"       )
        self.assertEqual(vocab_keys[-1]				,				"<mask>"       )
        self.assertEqual(len(lowerCamelCase__       )				,				1_054       )

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        self.assertEqual(self.get_tokenizer().vocab_size				,				1_054       )

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  str     =   MBartaaTokenizer(lowerCamelCase__				,				src_lang="en_XX"				,				tgt_lang="ro_RO"				,				keep_accents=lowerCamelCase__       )

        a__	:  Any     =   tokenizer.tokenize("This is a test"       )
        self.assertListEqual(lowerCamelCase__				,				["▁This", "▁is", "▁a", "▁t", "est"]       )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCamelCase__       )				,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,				)

        a__	:  Tuple     =   tokenizer.tokenize("I was born in 92000, and this is falsé."       )
        self.assertListEqual(
            lowerCamelCase__				,				[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]				,				)
        a__	:  str     =   tokenizer.convert_tokens_to_ids(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]				,				)

        a__	:  Optional[Any]     =   tokenizer.convert_ids_to_tokens(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]				,				)

  @slow
  def    _UpperCamelCase( self			:  Optional[int]       ):
        # fmt: off
        a__	:  Any     =   {"input_ids": [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase__				,				model_name="facebook/mbart-large-50"				,				revision="d3913889c59cd5c9e456b269c376325eabad57e2"				,				)

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

        a__	:  str     =   (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                    a__	:  Optional[Any]     =   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__				,				**lowerCamelCase__       )
                    a__	:  List[str]     =   self.tokenizer_class.from_pretrained(lowerCamelCase__				,				**lowerCamelCase__       )

                    a__	:  Optional[Any]     =   tempfile.mkdtemp()

                    a__	:  Tuple     =   tokenizer_r.save_pretrained(lowerCamelCase__       )
                    a__	:  List[str]     =   tokenizer_p.save_pretrained(lowerCamelCase__       )

                    # Checks it save with the same files + the tokenizer.json file for the fast one
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )
                    a__	:  List[Any]     =   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f       )
                    self.assertSequenceEqual(lowerCamelCase__				,				lowerCamelCase__       )

                    # Checks everything loads correctly in the same way
                    a__	:  str     =   tokenizer_r.from_pretrained(lowerCamelCase__       )
                    a__	:  int     =   tokenizer_p.from_pretrained(lowerCamelCase__       )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(lowerCamelCase__				,				lowerCamelCase__       )       )
                          # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                          # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                    shutil.rmtree(lowerCamelCase__       )

                    # Save tokenizer rust, legacy_format=True
                    a__	:  int     =   tempfile.mkdtemp()

                    a__	:  List[str]     =   tokenizer_r.save_pretrained(lowerCamelCase__				,				legacy_format=lowerCamelCase__       )
                    a__	:  Dict     =   tokenizer_p.save_pretrained(lowerCamelCase__       )

                    # Checks it save with the same files
                    self.assertSequenceEqual(lowerCamelCase__				,				lowerCamelCase__       )

                    # Checks everything loads correctly in the same way
                    a__	:  Union[str, Any]     =   tokenizer_r.from_pretrained(lowerCamelCase__       )
                    a__	:  Optional[int]     =   tokenizer_p.from_pretrained(lowerCamelCase__       )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(lowerCamelCase__				,				lowerCamelCase__       )       )

                    shutil.rmtree(lowerCamelCase__       )

                    # Save tokenizer rust, legacy_format=False
                    a__	:  Tuple     =   tempfile.mkdtemp()

                    a__	:  List[str]     =   tokenizer_r.save_pretrained(lowerCamelCase__				,				legacy_format=lowerCamelCase__       )
                    a__	:  Any     =   tokenizer_p.save_pretrained(lowerCamelCase__       )

                    # Checks it saved the tokenizer.json file
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )

                    # Checks everything loads correctly in the same way
                    a__	:  Optional[int]     =   tokenizer_r.from_pretrained(lowerCamelCase__       )
                    a__	:  Dict     =   tokenizer_p.from_pretrained(lowerCamelCase__       )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(lowerCamelCase__				,				lowerCamelCase__       )       )

                    shutil.rmtree(lowerCamelCase__       )




@require_torch
@require_sentencepiece
@require_tokenizers
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		'facebook/mbart-large-50-one-to-many-mmt'
  _lowercase									=		[
      ' UN Chief Says There Is No Military Solution in Syria',
      ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
  ]
  _lowercase									=		[
      'Şeful ONU declară că nu există o soluţie militară în Siria',
      'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
      ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
      ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
  ]
  _lowercase									=		[EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]

  @classmethod
  def    _UpperCamelCase( cls			:  List[Any]       ):
        a__	:  MBartaaTokenizer     =   MBartaaTokenizer.from_pretrained(
            cls.checkpoint_name				,				src_lang="en_XX"				,				tgt_lang="ro_RO"       )
        a__	:  List[Any]     =   1
        return cls

  def    _UpperCamelCase( self			:  Dict       ):
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]				,				250_001       )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]				,				250_004       )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]				,				250_020       )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]				,				250_038       )

  def    _UpperCamelCase( self			:  int       ):
        a__	:  int     =   self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
        self.assertListEqual(self.expected_src_tokens				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        self.assertIn(lowerCamelCase__				,				self.tokenizer.all_special_ids       )
        a__	:  Dict     =   [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
        a__	:  Any     =   self.tokenizer.decode(lowerCamelCase__				,				skip_special_tokens=lowerCamelCase__       )
        a__	:  Union[str, Any]     =   self.tokenizer.decode(generated_ids[1:]				,				skip_special_tokens=lowerCamelCase__       )
        self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )
        self.assertNotIn(self.tokenizer.eos_token				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  str       ):
        a__	:  Union[str, Any]     =   ["this is gunna be a long sentence " * 20]
        assert isinstance(src_text[0]				,				lowerCamelCase__       )
        a__	:  Union[str, Any]     =   10
        a__	:  List[Any]     =   self.tokenizer(lowerCamelCase__				,				max_length=lowerCamelCase__				,				truncation=lowerCamelCase__       ).input_ids[0]
        self.assertEqual(ids[0]				,				lowerCamelCase__       )
        self.assertEqual(ids[-1]				,				2       )
        self.assertEqual(len(lowerCamelCase__       )				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple       ):
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )				,				[250_053, 250_001]       )

  def    _UpperCamelCase( self			:  str       ):
        a__	:  Tuple     =   tempfile.mkdtemp()
        a__	:  Optional[Any]     =   self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(lowerCamelCase__       )
        a__	:  Any     =   MBartaaTokenizer.from_pretrained(lowerCamelCase__       )
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,				lowerCamelCase__       )

  @require_torch
  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Any     =   self.tokenizer(self.src_text				,				text_target=self.tgt_text				,				padding=lowerCamelCase__				,				return_tensors="pt"       )
        a__	:  List[str]     =   shift_tokens_right(batch["labels"]				,				self.tokenizer.pad_token_id       )

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        assert batch.input_ids[1][0] == EN_CODE
        assert batch.input_ids[1][-1] == 2
        assert batch.labels[1][0] == RO_CODE
        assert batch.labels[1][-1] == 2
        assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

  @require_torch
  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Optional[Any]     =   self.tokenizer(
            self.src_text				,				text_target=self.tgt_text				,				padding=lowerCamelCase__				,				truncation=lowerCamelCase__				,				max_length=len(self.expected_src_tokens       )				,				return_tensors="pt"				,				)
        a__	:  Union[str, Any]     =   shift_tokens_right(batch["labels"]				,				self.tokenizer.pad_token_id       )

        self.assertIsInstance(lowerCamelCase__				,				lowerCamelCase__       )

        self.assertEqual((2, 14)				,				batch.input_ids.shape       )
        self.assertEqual((2, 14)				,				batch.attention_mask.shape       )
        a__	:  Optional[int]     =   batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens				,				lowerCamelCase__       )
        self.assertEqual(2				,				batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens				,				[EN_CODE]       )
        self.assertEqual(self.tokenizer.suffix_tokens				,				[self.tokenizer.eos_token_id]       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  int     =   self.tokenizer(self.src_text				,				padding=lowerCamelCase__				,				truncation=lowerCamelCase__				,				max_length=3				,				return_tensors="pt"       )
        a__	:  Tuple     =   self.tokenizer(
            text_target=self.tgt_text				,				padding=lowerCamelCase__				,				truncation=lowerCamelCase__				,				max_length=10				,				return_tensors="pt"       )
        a__	:  str     =   targets["input_ids"]
        a__	:  Dict     =   shift_tokens_right(lowerCamelCase__				,				self.tokenizer.pad_token_id       )

        self.assertEqual(batch.input_ids.shape[1]				,				3       )
        self.assertEqual(batch.decoder_input_ids.shape[1]				,				10       )

  @require_torch
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[Any]     =   self.tokenizer._build_translation_inputs(
            "A test"				,				return_tensors="pt"				,				src_lang="en_XX"				,				tgt_lang="ar_AR"       )

        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				{
                # en_XX, A, test, EOS
                "input_ids": [[250_004, 62, 3_034, 2]],
                "attention_mask": [[1, 1, 1, 1]],
                # ar_AR
                "forced_bos_token_id": 250_001,
            }				,				)




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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







def 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=False	,):


       """simple docstring"""




       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 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = 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(__A		)

       # 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(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).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__":
    UpperCamelCase__		 =     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__		 =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('''SD: Done: ONNX''')

