

"""simple docstring"""

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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__=False					,					)			->						List[Any]:
 output_path.parent.mkdir(parents=lowercase__					,					exist_ok=lowercase__   )
 # 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(
      lowercase__					,					lowercase__					,					f=output_path.as_posix()					,					input_names=lowercase__					,					output_names=lowercase__					,					dynamic_axes=lowercase__					,					do_constant_folding=lowercase__					,					use_external_data_format=lowercase__					,					enable_onnx_checker=lowercase__					,					opset_version=lowercase__					,					)
 else:
  export(
      lowercase__					,					lowercase__					,					f=output_path.as_posix()					,					input_names=lowercase__					,					output_names=lowercase__					,					dynamic_axes=lowercase__					,					do_constant_folding=lowercase__					,					opset_version=lowercase__					,					)



@torch.no_grad()
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__ = False   )			->						Optional[Any]:
 lowerCAmelCase__   :				Any 			=				torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  lowerCAmelCase__   :				Optional[int] 			=				"cuda"
 elif fpaa and not torch.cuda.is_available():
  raise ValueError("`float16` model export is only supported on GPUs with CUDA"   )
 else:
  lowerCAmelCase__   :				Any 			=				"cpu"
 lowerCAmelCase__   :				Dict 			=				Path(lowercase__   )

 # VAE DECODER
 lowerCAmelCase__   :				Optional[int] 			=				AutoencoderKL.from_pretrained(model_path + "/vae"   )
 lowerCAmelCase__   :				Dict 			=				vae_decoder.config.latent_channels
 # forward only through the decoder part
 lowerCAmelCase__   :				Optional[Any] 			=				vae_decoder.decode
 onnx_export(
     lowercase__					,					model_args=(
         torch.randn(1					,					lowercase__					,					2_5					,					2_5   ).to(device=lowercase__					,					dtype=lowercase__   ),
         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=lowercase__					,					)
 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=1_4,
        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""")









"""simple docstring"""

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizer
       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizerFast
       __UpperCamelCase					:      str    =						True
       __UpperCamelCase					:      Any    =						True






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        super().setUp()

        lowerCAmelCase__   :				Tuple 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				Dict 			=				os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES["vocab_file"]   )
        with open(self.vocab_file			,					"w"			,					encoding="utf-8"   ) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				"UNwant\u00E9d,running"
        lowerCAmelCase__   :				Union[str, Any] 			=				"unwanted, running"
        return input_text, output_text






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.tokenizer_class(self.vocab_file   )

        lowerCAmelCase__   :				List[Any] 			=				tokenizer.tokenize("UNwant\u00E9d,running"   )
        self.assertListEqual(snake_case			,					["un", "##want", "##ed", ",", "runn", "##ing"]   )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )			,					[7, 4, 5, 10, 8, 9]   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        if not self.test_rust_tokenizer:
         return

        lowerCAmelCase__   :				int 			=				self.get_tokenizer()
        lowerCAmelCase__   :				List[str] 			=				self.get_rust_tokenizer()

        lowerCAmelCase__   :				Optional[Any] 			=				"I was born in 92000, and this is falsé."

        lowerCAmelCase__   :				int 			=				tokenizer.tokenize(snake_case   )
        lowerCAmelCase__   :				int 			=				rust_tokenizer.tokenize(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				str 			=				tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				rust_tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				self.get_rust_tokenizer()
        lowerCAmelCase__   :				Tuple 			=				tokenizer.encode(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				rust_tokenizer.encode(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )






