

import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							DebertaTokenizer
    lowerCamelCase			=							True
    lowerCamelCase			=							DebertaTokenizerFast


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        A__          =   [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '[UNK]',
        ]
        A__          =   dict(zip(lowercase_,range(len(lowercase_		)		)		)		)
        A__          =   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        A__          =   {'unk_token': '[UNK]'}

        A__          =   os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file']		)
        A__          =   os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['merges_file']		)
        with open(self.vocab_file,'w',encoding='utf-8'		) as fp:
            fp.write(json.dumps(lowercase_		) + '\n'		)
        with open(self.merges_file,'w',encoding='utf-8'		) as fp:
            fp.write('\n'.join(lowercase_		)		)


    def 						snake_case__     (      self :       Tuple,**lowercase_ :       Dict		)->    Dict:





        '''simple docstring'''







        kwargs.update(self.special_tokens_map		)
        return self.tokenizer_class.from_pretrained(self.tmpdirname,**lowercase_		)


    def 						snake_case__     (      self :       int,lowercase_ :       List[Any]		)->    Any:





        '''simple docstring'''







        A__          =   'lower newer'
        A__          =   'lower newer'
        return input_text, output_text


    def 						snake_case__     (      self :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   self.get_tokenizer()
        A__          =   'lower newer'
        A__          =   ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        A__          =   tokenizer.tokenize(lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)

        A__          =   tokens + [tokenizer.unk_token]
        A__          =   [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_		),lowercase_		)


    def 						snake_case__     (      self :       int		)->    Dict:





        '''simple docstring'''







        A__          =   self.get_tokenizer()
        A__          =   tokenizer('Hello','World'		)
        A__          =   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['token_type_ids'],lowercase_		)


    @slow
    def 						snake_case__     (      self :       List[Any]		)->    Tuple:





        '''simple docstring'''







        A__          =   self.tokenizer_class.from_pretrained('microsoft/deberta-base'		)

        A__          =   tokenizer.encode('sequence builders',add_special_tokens=lowercase_		)
        A__          =   tokenizer.encode('multi-sequence build',add_special_tokens=lowercase_		)

        A__          =   tokenizer.encode(
            'sequence builders',add_special_tokens=lowercase_,add_prefix_space=lowercase_		)
        A__          =   tokenizer.encode(
            'sequence builders','multi-sequence build',add_special_tokens=lowercase_,add_prefix_space=lowercase_		)

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

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode


    @slow
    def 						snake_case__     (      self :       str		)->    List[str]:





        '''simple docstring'''







        A__          =   [self.tokenizer_class]
        if self.test_rust_tokenizer:
            tokenizer_classes.append(self.rust_tokenizer_class		)

        for tokenizer_class in tokenizer_classes:
            A__          =   tokenizer_class.from_pretrained('microsoft/deberta-base'		)

            A__          =   [
                'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                'ALBERT incorporates two parameter reduction techniques',
                'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                ' vocabulary embedding.',
            ]

            A__          =   tokenizer(lowercase_,padding=lowercase_		)
            A__          =   [tokenizer.decode(lowercase_,skip_special_tokens=lowercase_		) for seq in encoding['input_ids']]

            # fmt: off
            A__          =   {
                'input_ids': [
                    [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 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_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
                ],
                'token_type_ids': [
                    [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, 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]
                ],
                'attention_mask': [
                    [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],
                    [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],
                    [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]
                ]
            }
            # fmt: on

            A__          =   [
                'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                'ALBERT incorporates two parameter reduction techniques',
                'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                ' vocabulary embedding.',
            ]

            self.assertDictEqual(encoding.data,lowercase_		)

            for expected, decoded in zip(lowercase_,lowercase_		):
                self.assertEqual(lowercase_,lowercase_		)




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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # 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(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






    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(SCREAMING_SNAKE_CASE__      )

    # 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(
        SCREAMING_SNAKE_CASE__    ,			model_args=(
            torch.randn(1    ,			SCREAMING_SNAKE_CASE__    ,			25    ,			25      ).to(device=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__      ),
            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=SCREAMING_SNAKE_CASE__    ,			)
    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")

