


"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class    lowerCAmelCase      (   unittest.TestCase       ):




		'''simple docstring'''







		@require_torch
		def 				__A		(      self							)	->				int:
								SCREAMING_SNAKE_CASE						=   pipeline(
								    task='zero-shot-audio-classification'		,			model='hf-internal-testing/tiny-clap-htsat-unfused'							)
								SCREAMING_SNAKE_CASE						=   load_dataset('ashraq/esc50'							)
								SCREAMING_SNAKE_CASE						=   dataset["train"]["audio"][-1]["array"]
								SCREAMING_SNAKE_CASE						=   audio_classifier(snake_case__		,			candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']							)
								self.assertEqual(
								    nested_simplify(snake_case__							)		,			[{'score': 0.5_01, 'label': 'Sound of a dog'}, {'score': 0.4_99, 'label': 'Sound of vaccum cleaner'}]		,			)







		@unittest.skip('No models are available in TF'							)
		def 				__A		(      self							)	->				List[Any]:
								pass







		@slow
		@require_torch
		def 				__A		(      self							)	->				int:
								SCREAMING_SNAKE_CASE						=   pipeline(
								    task='zero-shot-audio-classification'		,			model='laion/clap-htsat-unfused'		,			)
								# This is an audio of a dog
								SCREAMING_SNAKE_CASE						=   load_dataset('ashraq/esc50'							)
								SCREAMING_SNAKE_CASE						=   dataset["train"]["audio"][-1]["array"]
								SCREAMING_SNAKE_CASE						=   audio_classifier(snake_case__		,			candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']							)

								self.assertEqual(
								    nested_simplify(snake_case__							)		,			[
								        {'score': 0.9_99, 'label': 'Sound of a dog'},
								        {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								    ]		,			)

								SCREAMING_SNAKE_CASE						=   audio_classifier([audio] * 5		,			candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']							)
								self.assertEqual(
								    nested_simplify(snake_case__							)		,			[
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5		,			)
								SCREAMING_SNAKE_CASE						=   audio_classifier(
								    [audio] * 5		,			candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']		,			batch_size=5							)
								self.assertEqual(
								    nested_simplify(snake_case__							)		,			[
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5		,			)







		@unittest.skip('No models are available in TF'							)
		def 				__A		(      self							)	->				Optional[int]:
								pass




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase				=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=False							,   ):
       output_path.parent.mkdir(parents=__lowerCamelCase							,   exist_ok=__lowerCamelCase       )
       # 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(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   use_external_data_format=__lowerCamelCase							,   enable_onnx_checker=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )
       else:
              export(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )


@torch.no_grad()
def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= vae_decoder.decode
       onnx_export(
           __lowerCamelCase							,   model_args=(
               torch.randn(1							,   __lowerCamelCase							,   25							,   25       ).to(device=__lowerCamelCase							,   dtype=__lowerCamelCase       ),
               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=__lowerCamelCase							,   )
       del vae_decoder


if __name__ == "__main__":
   __lowerCamelCase				=			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""")

   __lowerCamelCase				=			parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print("""SD: Done: ONNX""")





