


'''simple docstring'''



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowercase	:						Optional[Any]       =	version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






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

					# VAE DECODER
					__a			:       Optional[Any]					    =  AutoencoderKL.from_pretrained(model_path + '/vae'   )
					__a			:       Union[str, Any]					    =  vae_decoder.config.latent_channels
					# forward only through the decoder part
					__a			:       Any					    =  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	:						int       =	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	:						Tuple       =	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'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase	:						Union[str, Any]       =	{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Union[str, Any]       =	[
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Dict       =	[
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						str       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



