






'''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


__A							:							int  							=		version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")






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






@torch.no_grad()
def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = False):
 lowerCamelCase__  =     torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  lowerCamelCase__  =     'cuda'
 elif fpaa and not torch.cuda.is_available():
  raise ValueError("`float16` model export is only supported on GPUs with CUDA")
 else:
  lowerCamelCase__  =     'cpu'
 lowerCamelCase__  =     StableDiffusionPipeline.from_pretrained(lowercase__				,     torch_dtype=lowercase__).to(lowercase__)
 lowerCamelCase__  =     Path(lowercase__)

 # TEXT ENCODER
 lowerCamelCase__  =     pipeline.text_encoder.config.max_position_embeddings
 lowerCamelCase__  =     pipeline.text_encoder.config.hidden_size
 lowerCamelCase__  =     pipeline.tokenizer(
     "A sample prompt"				,     padding="max_length"				,     max_length=pipeline.tokenizer.model_max_length				,     truncation=lowercase__				,     return_tensors="pt"				,     )
 onnx_export(
     pipeline.text_encoder				,     model_args=(text_input.input_ids.to(device=lowercase__				,     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=lowercase__				,     )
 del pipeline.text_encoder

 # UNET
 lowerCamelCase__  =     pipeline.unet.config.in_channels
 lowerCamelCase__  =     pipeline.unet.config.sample_size
 lowerCamelCase__  =     output_path / 'unet' / 'model.onnx'
 onnx_export(
     pipeline.unet				,     model_args=(
         torch.randn(2				,     lowercase__				,     lowercase__				,     lowercase__).to(device=lowercase__				,     dtype=lowercase__),
         torch.randn(2).to(device=lowercase__				,     dtype=lowercase__),
         torch.randn(2				,     lowercase__				,     lowercase__).to(device=lowercase__				,     dtype=lowercase__),
         False,
     )				,     output_path=lowercase__				,     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=lowercase__				,     use_external_data_format=lowercase__				,     )
 lowerCamelCase__  =     str(unet_path.absolute().as_posix())
 lowerCamelCase__  =     os.path.dirname(lowercase__)
 lowerCamelCase__  =     onnx.load(lowercase__)
 # clean up existing tensor files
 shutil.rmtree(lowercase__)
 os.mkdir(lowercase__)
 # collate external tensor files into one
 onnx.save_model(
     lowercase__				,     lowercase__				,     save_as_external_data=lowercase__				,     all_tensors_to_one_file=lowercase__				,     location="weights.pb"				,     convert_attribute=lowercase__				,     )
 del pipeline.unet

 # VAE ENCODER
 lowerCamelCase__  =     pipeline.vae
 lowerCamelCase__  =     vae_encoder.config.in_channels
 lowerCamelCase__  =     vae_encoder.config.sample_size
 # need to get the raw tensor output (sample) from the encoder
 lowerCamelCase__  =     lambda lowercase__				,     lowercase__: vae_encoder.encode(lowercase__				,     lowercase__)[0].sample()
 onnx_export(
     lowercase__				,     model_args=(
         torch.randn(1				,     lowercase__				,     lowercase__				,     lowercase__).to(device=lowercase__				,     dtype=lowercase__),
         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=lowercase__				,     )

 # VAE DECODER
 lowerCamelCase__  =     pipeline.vae
 lowerCamelCase__  =     vae_decoder.config.latent_channels
 lowerCamelCase__  =     vae_decoder.config.out_channels
 # forward only through the decoder part
 lowerCamelCase__  =     vae_encoder.decode
 onnx_export(
     lowercase__				,     model_args=(
         torch.randn(1				,     lowercase__				,     lowercase__				,     lowercase__).to(device=lowercase__				,     dtype=lowercase__),
         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=lowercase__				,     )
 del pipeline.vae

 # SAFETY CHECKER
 if pipeline.safety_checker is not None:
  lowerCamelCase__  =     pipeline.safety_checker
  lowerCamelCase__  =     safety_checker.config.vision_config.num_channels
  lowerCamelCase__  =     safety_checker.config.vision_config.image_size
  lowerCamelCase__  =     safety_checker.forward_onnx
  onnx_export(
      pipeline.safety_checker				,     model_args=(
          torch.randn(
              1				,     lowercase__				,     lowercase__				,     lowercase__				,     ).to(device=lowercase__				,     dtype=lowercase__),
          torch.randn(1				,     lowercase__				,     lowercase__				,     lowercase__).to(device=lowercase__				,     dtype=lowercase__),
      )				,     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=lowercase__				,     )
  del pipeline.safety_checker
  lowerCamelCase__  =     OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
  lowerCamelCase__  =     pipeline.feature_extractor
 else:
  lowerCamelCase__  =     None
  lowerCamelCase__  =     None

 lowerCamelCase__  =     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=lowercase__				,     feature_extractor=lowercase__				,     requires_safety_checker=safety_checker is not None				,     )

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

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


if __name__ == "__main__":
 __A							:							Tuple  							=		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""")

 __A							:							Optional[Any]  							=		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,
)


__A							:							Union[str, Any]  							=		{
    """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:
 __A							:							Optional[Any]  							=		["""LayoutLMv2TokenizerFast"""]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							List[Any]  							=		["""LayoutLMv2FeatureExtractor"""]
 __A							:							List[Any]  							=		["""LayoutLMv2ImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Any  							=		[
     """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

 __A							:							Tuple  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

