






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







def 			A__							(      _a  :					Optional[Any]		,  _a  :					tuple		,  _a  :					Path		,  _a  :					Optional[Any]		,  _a  :					str		,  _a  :					List[str]		,  _a  :					Optional[Any]		,  _a  :					int=False		,  ):





 '''simple docstring'''







 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 			A__							(      _a  :					str		,  _a  :					str		,  _a  :					int		,  _a  :					bool = False	):





 '''simple docstring'''







 snake_case__  :    Any							       =torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  snake_case__  :    Any							       ="""cuda"""
 elif fpaa and not torch.cuda.is_available():
  raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
 else:
  snake_case__  :    Optional[int]							       ="""cpu"""
 snake_case__  :    Tuple							       =StableDiffusionPipeline.from_pretrained(_a		,  torch_dtype=_a	).to(_a	)
 snake_case__  :    Dict							       =Path(_a	)

 # TEXT ENCODER
 snake_case__  :    List[str]							       =pipeline.text_encoder.config.max_position_embeddings
 snake_case__  :    List[str]							       =pipeline.text_encoder.config.hidden_size
 snake_case__  :    str							       =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
 snake_case__  :    Optional[Any]							       =pipeline.unet.config.in_channels
 snake_case__  :    List[Any]							       =pipeline.unet.config.sample_size
 snake_case__  :    Dict							       =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		,  )
 snake_case__  :    str							       =str(unet_path.absolute().as_posix()	)
 snake_case__  :    int							       =os.path.dirname(_a	)
 snake_case__  :    str							       =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
 snake_case__  :    Optional[int]							       =pipeline.vae
 snake_case__  :    List[Any]							       =vae_encoder.config.in_channels
 snake_case__  :    Union[str, Any]							       =vae_encoder.config.sample_size
 # need to get the raw tensor output (sample) from the encoder
 snake_case__  :    List[Any]							       =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
 snake_case__  :    str							       =pipeline.vae
 snake_case__  :    Optional[int]							       =vae_decoder.config.latent_channels
 snake_case__  :    Tuple							       =vae_decoder.config.out_channels
 # forward only through the decoder part
 snake_case__  :    Tuple							       =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:
  snake_case__  :    List[Any]							       =pipeline.safety_checker
  snake_case__  :    Tuple							       =safety_checker.config.vision_config.num_channels
  snake_case__  :    Dict							       =safety_checker.config.vision_config.image_size
  snake_case__  :    Any							       =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
  snake_case__  :    Dict							       =OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""	)
  snake_case__  :    Union[str, Any]							       =pipeline.feature_extractor
 else:
  snake_case__  :    Any							       =None
  snake_case__  :    List[Any]							       =None

 snake_case__  :    Any							       =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
 snake_case__  :    Optional[Any]							       =OnnxStableDiffusionPipeline.from_pretrained(_a		,  provider="""CPUExecutionProvider"""	)
 print("""ONNX pipeline is loadable"""	)


if __name__ == "__main__":
       __lowerCamelCase	:		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""")

       __lowerCamelCase	:		int										=   parser.parse_args()

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













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Union[str, Any]										=   {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}







class  _lowercase   (   _A						):
      _a							:						int										=							'xlm'
      _a							:						List[Any]										=							{
          'hidden_size': 'emb_dim',
          'num_attention_heads': 'n_heads',
          'num_hidden_layers': 'n_layers',
          'n_words': 'vocab_size',  # For backward compatibility
      }
      def __init__(     self				,			a=3_0_1_4_5				,			a=2_0_4_8				,			a=1_2				,			a=1_6				,			a=0.1				,			a=0.1				,			a=True				,			a=False				,			a=False				,			a=False				,			a=1				,			a=True				,			a=5_1_2				,			a=2_0_4_8**-0.5				,			a=1e-12				,			a=0.02				,			a=0				,			a=1				,			a=2				,			a=3				,			a=5				,			a=True				,			a="first"				,			a=True				,			a=None				,			a=True				,			a=0.1				,			a=5				,			a=5				,			a=0				,			a=0				,			a=2				,			a=0				,			**a				,			):
       snake_case__  :    str							       =vocab_size
       snake_case__  :    List[Any]							       =emb_dim
       snake_case__  :    List[str]							       =n_layers
       snake_case__  :    Any							       =n_heads
       snake_case__  :    Any							       =dropout
       snake_case__  :    Optional[int]							       =attention_dropout
       snake_case__  :    Dict							       =gelu_activation
       snake_case__  :    Tuple							       =sinusoidal_embeddings
       snake_case__  :    Optional[int]							       =causal
       snake_case__  :    Optional[int]							       =asm
       snake_case__  :    Optional[int]							       =n_langs
       snake_case__  :    Union[str, Any]							       =use_lang_emb
       snake_case__  :    Optional[Any]							       =layer_norm_eps
       snake_case__  :    Union[str, Any]							       =bos_index
       snake_case__  :    List[Any]							       =eos_index
       snake_case__  :    Optional[Any]							       =pad_index
       snake_case__  :    Union[str, Any]							       =unk_index
       snake_case__  :    Dict							       =mask_index
       snake_case__  :    str							       =is_encoder
       snake_case__  :    Optional[Any]							       =max_position_embeddings
       snake_case__  :    Any							       =embed_init_std
       snake_case__  :    Optional[Any]							       =init_std
       snake_case__  :    str							       =summary_type
       snake_case__  :    List[Any]							       =summary_use_proj
       snake_case__  :    str							       =summary_activation
       snake_case__  :    Optional[Any]							       =summary_proj_to_labels
       snake_case__  :    Optional[int]							       =summary_first_dropout
       snake_case__  :    Optional[Any]							       =start_n_top
       snake_case__  :    List[Any]							       =end_n_top
       snake_case__  :    Union[str, Any]							       =mask_token_id
       snake_case__  :    Any							       =lang_id

       if "n_words" in kwargs:
        snake_case__  :    int							       =kwargs["""n_words"""]

       super().__init__(pad_token_id=a				,			bos_token_id=a				,			**a   )







class  _lowercase   (   _A						):
      @property
      def 						lowercase__					(     self   ):
       if self.task == "multiple-choice":
        snake_case__  :    Any							       ={0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        snake_case__  :    Union[str, Any]							       ={0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]   )





