



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=False     ,				):
    output_path.parent.mkdir(parents=_UpperCAmelCase     ,				exist_ok=_UpperCAmelCase       )
    # 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(
            _UpperCAmelCase     ,				_UpperCAmelCase     ,				f=output_path.as_posix()     ,				input_names=_UpperCAmelCase     ,				output_names=_UpperCAmelCase     ,				dynamic_axes=_UpperCAmelCase     ,				do_constant_folding=_UpperCAmelCase     ,				use_external_data_format=_UpperCAmelCase     ,				enable_onnx_checker=_UpperCAmelCase     ,				opset_version=_UpperCAmelCase     ,				)
    else:
        export(
            _UpperCAmelCase     ,				_UpperCAmelCase     ,				f=output_path.as_posix()     ,				input_names=_UpperCAmelCase     ,				output_names=_UpperCAmelCase     ,				dynamic_axes=_UpperCAmelCase     ,				do_constant_folding=_UpperCAmelCase     ,				opset_version=_UpperCAmelCase     ,				)





@torch.no_grad()
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase = False       ):
    __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(_UpperCAmelCase       )

    # 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(
        _UpperCAmelCase     ,				model_args=(
            torch.randn(1     ,				_UpperCAmelCase     ,				25     ,				25       ).to(device=_UpperCAmelCase     ,				dtype=_UpperCAmelCase       ),
            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=_UpperCAmelCase     ,				)
    del vae_decoder


if __name__ == "__main__":
 __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''')

 __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 os
import sys
import unittest


__snake_case		:Union[str, Any]   	=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__snake_case		:List[str]   	=  os.path.join(git_repo_path, '''src''', '''transformers''')

__snake_case		:Any   	=  '''
{0} = None
'''

__snake_case		:Dict   	=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__snake_case		:str   	=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''

class 					_A (			unittest.TestCase					):



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")''')
           self.assertIsNone(__SCREAMING_SNAKE_CASE)

           __a		 =       find_backend('''    if not is_tokenizers_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tokenizers''')

           __a		 =       find_backend('''    if not is_tensorflow_text_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tensorflow_text''')

           __a		 =       find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tensorflow_text''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers_and_vision''')



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn('''torch'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''tensorflow_text'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''sentencepiece_and_tokenizers'''  ,	__SCREAMING_SNAKE_CASE)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertModel'''  ,	objects['''tf'''])
           self.assertIn('''FlaxBertModel'''  ,	objects['''flax'''])
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertTokenizer'''  ,	objects['''tensorflow_text'''])
           self.assertIn('''convert_slow_tokenizer'''  ,	objects['''sentencepiece_and_tokenizers'''])



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       create_dummy_object('''CONSTANT'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''\nCONSTANT = None\n''')

           __a		 =       create_dummy_object('''function'''  ,	'''\'torch\'''')
           self.assertEqual(
               __SCREAMING_SNAKE_CASE  ,	'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n''')

           __a		 =       '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
           __a		 =       create_dummy_object('''FakeClass'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
           __a		 =       create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']})
           self.assertEqual(dummy_files['''torch''']  ,	__SCREAMING_SNAKE_CASE)





