
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


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






def   lowerCamelCase__							(					a		,		a		,		a		,		a		,		a		,		a		,		a		,		a=False		,		)      ->   Optional[int]:
   output_path.parent.mkdir(parents=__lowerCamelCase		,		exist_ok=__lowerCamelCase      )
   # 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(
          __lowerCamelCase		,		__lowerCamelCase		,		f=output_path.as_posix()		,		input_names=__lowerCamelCase		,		output_names=__lowerCamelCase		,		dynamic_axes=__lowerCamelCase		,		do_constant_folding=__lowerCamelCase		,		use_external_data_format=__lowerCamelCase		,		enable_onnx_checker=__lowerCamelCase		,		opset_version=__lowerCamelCase		,		)
   else:
      export(
          __lowerCamelCase		,		__lowerCamelCase		,		f=output_path.as_posix()		,		input_names=__lowerCamelCase		,		output_names=__lowerCamelCase		,		dynamic_axes=__lowerCamelCase		,		do_constant_folding=__lowerCamelCase		,		opset_version=__lowerCamelCase		,		)






@torch.no_grad()
def   lowerCamelCase__							(					a		,		a		,		a		,		a = False      )      ->   List[str]:
   _A:  Dict					=       torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      _A:  Optional[int]					=       """cuda"""
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''      )
   else:
      _A:  Any					=       """cpu"""
   _A:  Tuple					=       StableDiffusionPipeline.from_pretrained(__lowerCamelCase		,		torch_dtype=__lowerCamelCase      ).to(__lowerCamelCase      )
   _A:  int					=       Path(__lowerCamelCase      )

   # TEXT ENCODER
   _A:  List[Any]					=       pipeline.text_encoder.config.max_position_embeddings
   _A:  str					=       pipeline.text_encoder.config.hidden_size
   _A:  Union[str, Any]					=       pipeline.tokenizer(
       '''A sample prompt'''		,		padding='''max_length'''		,		max_length=pipeline.tokenizer.model_max_length		,		truncation=__lowerCamelCase		,		return_tensors='''pt'''		,		)
   onnx_export(
       pipeline.text_encoder		,		model_args=(text_input.input_ids.to(device=__lowerCamelCase		,		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=__lowerCamelCase		,		)
   del pipeline.text_encoder

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

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

   # VAE DECODER
   _A:  Any					=       pipeline.vae
   _A:  List[str]					=       vae_decoder.config.latent_channels
   _A:  str					=       vae_decoder.config.out_channels
   # forward only through the decoder part
   _A:  Optional[int]					=       vae_encoder.decode
   onnx_export(
       __lowerCamelCase		,		model_args=(
           torch.randn(1		,		__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase      ).to(device=__lowerCamelCase		,		dtype=__lowerCamelCase      ),
           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=__lowerCamelCase		,		)
   del pipeline.vae

   # SAFETY CHECKER
   if pipeline.safety_checker is not None:
      _A:  Tuple					=       pipeline.safety_checker
      _A:  Dict					=       safety_checker.config.vision_config.num_channels
      _A:  int					=       safety_checker.config.vision_config.image_size
      _A:  str					=       safety_checker.forward_onnx
      onnx_export(
          pipeline.safety_checker		,		model_args=(
              torch.randn(
                  1		,		__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		).to(device=__lowerCamelCase		,		dtype=__lowerCamelCase      ),
              torch.randn(1		,		__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase      ).to(device=__lowerCamelCase		,		dtype=__lowerCamelCase      ),
          )		,		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=__lowerCamelCase		,		)
      del pipeline.safety_checker
      _A:  Union[str, Any]					=       OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''      )
      _A:  Union[str, Any]					=       pipeline.feature_extractor
   else:
      _A:  Dict					=       None
      _A:  str					=       None

   _A:  Optional[int]					=       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=__lowerCamelCase		,		feature_extractor=__lowerCamelCase		,		requires_safety_checker=safety_checker is not None		,		)

   onnx_pipeline.save_pretrained(__lowerCamelCase      )
   print('''ONNX pipeline saved to'''		,		__lowerCamelCase      )

   del pipeline
   del onnx_pipeline
   _A:  Optional[Any]					=       OnnxStableDiffusionPipeline.from_pretrained(__lowerCamelCase		,		provider='''CPUExecutionProvider'''      )
   print('''ONNX pipeline is loadable'''      )


if __name__ == "__main__":
   UpperCAmelCase__		:     str     			=      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')

   UpperCAmelCase__		:     Any     			=      parser.parse_args()

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











import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


a		:       Optional[Any]			     = {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

a		:       Optional[Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	'maskformer'
      a  : Dict							       =	{'hidden_size': 'mask_feature_size'}
      a  : Optional[Any]							       =	['resnet', 'swin']
      a  : List[Any]							       =	['detr']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int = 256		,      __lowercase					:			int = 256		,      __lowercase					:			float = 0.1		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			float = 0.02		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 20.0		,      __lowercase					:			Optional[bool] = None		,      **__lowercase					:			Tuple		,      )							->      str:
        if backbone_config is None:
          # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
          __UpperCAmelCase							:	List[str]           =  SwinConfig(
              image_size=384		,      in_channels=3		,      patch_size=4		,      embed_dim=128		,      depths=[2, 2, 18, 2]		,      num_heads=[4, 8, 16, 32]		,      window_size=12		,      drop_path_rate=0.3		,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		,      )

        if isinstance(__lowercase		,      __lowercase					):
          __UpperCAmelCase							:	Dict           =  backbone_config.pop("""model_type"""					)
          __UpperCAmelCase							:	Tuple           =  CONFIG_MAPPING[backbone_model_type]
          __UpperCAmelCase							:	List[str]           =  config_class.from_dict(__lowercase					)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
          logger.warning_once(
              f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
              f"""Supported model types: {",".join(self.backbones_supported					)}"""					)

        if decoder_config is None:
          # fall back to https://huggingface.co/facebook/detr-resnet-50
          __UpperCAmelCase							:	List[Any]           =  DetrConfig()
        else:
          # verify that the decoder is supported
          __UpperCAmelCase							:	List[Any]           =  (
              decoder_config.pop("""model_type"""					) if isinstance(__lowercase		,      __lowercase					) else decoder_config.model_type
          )
          if decoder_type not in self.decoders_supported:
            raise ValueError(
                f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                f""" {",".join(self.decoders_supported					)}"""					)
          if isinstance(__lowercase		,      __lowercase					):
            __UpperCAmelCase							:	Dict           =  CONFIG_MAPPING[decoder_type]
            __UpperCAmelCase							:	Union[str, Any]           =  config_class.from_dict(__lowercase					)

        __UpperCAmelCase							:	Optional[Any]           =  backbone_config
        __UpperCAmelCase							:	List[str]           =  decoder_config
        # main feature dimension for the model
        __UpperCAmelCase							:	Union[str, Any]           =  fpn_feature_size
        __UpperCAmelCase							:	Optional[Any]           =  mask_feature_size
        # initializer
        __UpperCAmelCase							:	int           =  init_std
        __UpperCAmelCase							:	Any           =  init_xavier_std
        # Hungarian matcher && loss
        __UpperCAmelCase							:	Any           =  cross_entropy_weight
        __UpperCAmelCase							:	Optional[Any]           =  dice_weight
        __UpperCAmelCase							:	List[str]           =  mask_weight
        __UpperCAmelCase							:	Union[str, Any]           =  use_auxiliary_loss
        __UpperCAmelCase							:	int           =  no_object_weight
        __UpperCAmelCase							:	int           =  output_auxiliary_logits

        __UpperCAmelCase							:	Optional[Any]           =  self.decoder_config.encoder_attention_heads
        __UpperCAmelCase							:	Dict           =  self.decoder_config.num_hidden_layers
        super().__init__(**__lowercase					)
      @classmethod
      def 	UpperCAmelCase      (      cls					:			int		,      __lowercase					:			PretrainedConfig		,      __lowercase					:			PretrainedConfig		,      **__lowercase					:			str					)							->      Tuple:
        return cls(
            backbone_config=__lowercase		,      decoder_config=__lowercase		,      **__lowercase		,      )




      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Dict[str, any]:
        __UpperCAmelCase							:	Optional[Any]           =  copy.deepcopy(self.__dict__					)
        __UpperCAmelCase							:	Optional[int]           =  self.backbone_config.to_dict()
        __UpperCAmelCase							:	Any           =  self.decoder_config.to_dict()
        __UpperCAmelCase							:	Tuple           =  self.__class__.model_type
        return output


