




'''simple docstring'''



import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


lowerCamelCase_			=						version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			tuple	,					__A    :			Path	,					__A    :			Dict	,					__A    :			Any	,					__A    :			Optional[Any]	,					__A    :			Optional[int]	,					__A    :			Dict=False	,					)			->			List[Any]:
   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 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str	,					__A    :			int	,					__A    :			bool = False  )			->			Any:
   _SCREAMING_SNAKE_CASE	=				torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      _SCREAMING_SNAKE_CASE	=				"cuda"
   elif fpaa and not torch.cuda.is_available():
      raise ValueError("`float16` model export is only supported on GPUs with CUDA"  )
   else:
      _SCREAMING_SNAKE_CASE	=				"cpu"
   _SCREAMING_SNAKE_CASE	=				StableDiffusionPipeline.from_pretrained(__A	,					torch_dtype=__A  ).to(__A  )
   _SCREAMING_SNAKE_CASE	=				Path(__A  )

   # TEXT ENCODER
   _SCREAMING_SNAKE_CASE	=				pipeline.text_encoder.config.max_position_embeddings
   _SCREAMING_SNAKE_CASE	=				pipeline.text_encoder.config.hidden_size
   _SCREAMING_SNAKE_CASE	=				pipeline.tokenizer(
       "A sample prompt"	,					padding="max_length"	,					max_length=pipeline.tokenizer.model_max_length	,					truncation=__A	,					return_tensors="pt"	,					)
   onnx_export(
       pipeline.text_encoder	,					model_args=(text_input.input_ids.to(device=__A	,					dtype=torch.intaa  ))	,					output_path=output_path / "text_encoder" / "model.onnx"	,					ordered_input_names=["input_ids"]	,					output_names=["last_hidden_state", "pooler_output"]	,					dynamic_axes={
           "input_ids": {0: "batch", 1: "sequence"},
       }	,					opset=__A	,					)
   del pipeline.text_encoder

   # UNET
   _SCREAMING_SNAKE_CASE	=				pipeline.unet.config.in_channels
   _SCREAMING_SNAKE_CASE	=				pipeline.unet.config.sample_size
   _SCREAMING_SNAKE_CASE	=				output_path / "unet" / "model.onnx"
   onnx_export(
       pipeline.unet	,					model_args=(
           torch.randn(2	,					__A	,					__A	,					__A  ).to(device=__A	,					dtype=__A  ),
           torch.randn(2  ).to(device=__A	,					dtype=__A  ),
           torch.randn(2	,					__A	,					__A  ).to(device=__A	,					dtype=__A  ),
           False,
       )	,					output_path=__A	,					ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"]	,					output_names=["out_sample"]	,					dynamic_axes={
           "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
           "timestep": {0: "batch"},
           "encoder_hidden_states": {0: "batch", 1: "sequence"},
       }	,					opset=__A	,					use_external_data_format=__A	,					)
   _SCREAMING_SNAKE_CASE	=				str(unet_path.absolute().as_posix()  )
   _SCREAMING_SNAKE_CASE	=				os.path.dirname(__A  )
   _SCREAMING_SNAKE_CASE	=				onnx.load(__A  )
   # clean up existing tensor files
   shutil.rmtree(__A  )
   os.mkdir(__A  )
   # collate external tensor files into one
   onnx.save_model(
       __A	,					__A	,					save_as_external_data=__A	,					all_tensors_to_one_file=__A	,					location="weights.pb"	,					convert_attribute=__A	,					)
   del pipeline.unet

   # VAE ENCODER
   _SCREAMING_SNAKE_CASE	=				pipeline.vae
   _SCREAMING_SNAKE_CASE	=				vae_encoder.config.in_channels
   _SCREAMING_SNAKE_CASE	=				vae_encoder.config.sample_size
   # need to get the raw tensor output (sample) from the encoder
   _SCREAMING_SNAKE_CASE	=				lambda __A	,					__A  : vae_encoder.encode(__A	,					__A  )[0].sample()
   onnx_export(
       __A	,					model_args=(
           torch.randn(1	,					__A	,					__A	,					__A  ).to(device=__A	,					dtype=__A  ),
           False,
       )	,					output_path=output_path / "vae_encoder" / "model.onnx"	,					ordered_input_names=["sample", "return_dict"]	,					output_names=["latent_sample"]	,					dynamic_axes={
           "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
       }	,					opset=__A	,					)

   # VAE DECODER
   _SCREAMING_SNAKE_CASE	=				pipeline.vae
   _SCREAMING_SNAKE_CASE	=				vae_decoder.config.latent_channels
   _SCREAMING_SNAKE_CASE	=				vae_decoder.config.out_channels
   # forward only through the decoder part
   _SCREAMING_SNAKE_CASE	=				vae_encoder.decode
   onnx_export(
       __A	,					model_args=(
           torch.randn(1	,					__A	,					__A	,					__A  ).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 pipeline.vae

   # SAFETY CHECKER
   if pipeline.safety_checker is not None:
      _SCREAMING_SNAKE_CASE	=				pipeline.safety_checker
      _SCREAMING_SNAKE_CASE	=				safety_checker.config.vision_config.num_channels
      _SCREAMING_SNAKE_CASE	=				safety_checker.config.vision_config.image_size
      _SCREAMING_SNAKE_CASE	=				safety_checker.forward_onnx
      onnx_export(
          pipeline.safety_checker	,					model_args=(
              torch.randn(
                  1	,					__A	,					__A	,					__A	,					).to(device=__A	,					dtype=__A  ),
              torch.randn(1	,					__A	,					__A	,					__A  ).to(device=__A	,					dtype=__A  ),
          )	,					output_path=output_path / "safety_checker" / "model.onnx"	,					ordered_input_names=["clip_input", "images"]	,					output_names=["out_images", "has_nsfw_concepts"]	,					dynamic_axes={
              "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
              "images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
          }	,					opset=__A	,					)
      del pipeline.safety_checker
      _SCREAMING_SNAKE_CASE	=				OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"  )
      _SCREAMING_SNAKE_CASE	=				pipeline.feature_extractor
   else:
      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None

   _SCREAMING_SNAKE_CASE	=				OnnxStableDiffusionPipeline(
       vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"  )	,					vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"  )	,					text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"  )	,					tokenizer=pipeline.tokenizer	,					unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"  )	,					scheduler=pipeline.scheduler	,					safety_checker=__A	,					feature_extractor=__A	,					requires_safety_checker=safety_checker is not None	,					)

   onnx_pipeline.save_pretrained(__A  )
   print("ONNX pipeline saved to"	,					__A  )

   del pipeline
   del onnx_pipeline
   _SCREAMING_SNAKE_CASE	=				OnnxStableDiffusionPipeline.from_pretrained(__A	,					provider="CPUExecutionProvider"  )
   print("ONNX pipeline is loadable"  )


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()

 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_			=						{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2TokenizerFast']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2FeatureExtractor']
 lowerCamelCase_			=						['LayoutLMv2ImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv2ForQuestionAnswering',
     'LayoutLMv2ForSequenceClassification',
     'LayoutLMv2ForTokenClassification',
     'LayoutLMv2Layer',
     'LayoutLMv2Model',
     'LayoutLMv2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

