






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							tuple		,		__A    :							Path		,		__A    :							Optional[int]		,		__A    :							List[str]		,		__A    :							Union[str, Any]		,		__A    :							Tuple		,		__A    :							Optional[Any]=False		,		)      ->					Dict:
    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 )      ->					Optional[int]:
    _UpperCAmelCase				:      Tuple			  =				torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _UpperCAmelCase				:      Optional[int]			  =				'''cuda'''
    elif fpaa and not torch.cuda.is_available():
        raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
    else:
        _UpperCAmelCase				:      Optional[Any]			  =				'''cpu'''
    _UpperCAmelCase				:      str			  =				Path(__A )

    # VAE DECODER
    _UpperCAmelCase				:      str			  =				AutoencoderKL.from_pretrained(model_path + '''/vae''' )
    _UpperCAmelCase				:      Any			  =				vae_decoder.config.latent_channels
    # forward only through the decoder part
    _UpperCAmelCase				:      str			  =				vae_decoder.decode
    onnx_export(
        __A		,		model_args=(
            torch.randn(1		,		__A		,		25		,		25 ).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__":
  SCREAMING_SNAKE_CASE 							=						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')

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












import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE 							=						'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class 							A_    (						unittest.TestCase ,   __lowercase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				load_tool('''text-question-answering''')
     self.tool.setup()
     _UpperCAmelCase				:      Any			  =				load_tool('''text-question-answering'''							,  remote=_A)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.tool(_A							,  '''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.remote_tool(_A							,  '''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


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

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.tool(text=_A							,  question='''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				self.remote_tool(text=_A							,  question='''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')




