


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__    							= version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A,  __A,  __A=False,  )					->    int:







      '''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        lowerCAmelCase_			(					__A,  __A,  __A,  __A = False							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            UpperCAmelCase__								=  "cuda"
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("`float16` model export is only supported on GPUs with CUDA"							)
      else:
            UpperCAmelCase__								=  "cpu"
      UpperCAmelCase__								=  StableDiffusionPipeline.from_pretrained(__A,  torch_dtype=__A							).to(__A							)
      UpperCAmelCase__								=  Path(__A							)

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

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


if __name__ == "__main__":
      UpperCamelCase__    							= 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=1_4,
          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__    							= parser.parse_args()

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




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      UpperCamelCase__    							= None


UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= '▁'

UpperCamelCase__    							= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__    							= {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

UpperCamelCase__    							= {
    'google/pegasus-xsum': 5_1_2,
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			str              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			Any              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			List[Any]              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Union[str, Any]              =      PegasusTokenizer
   __UpperCAmelCase			:			Any              =      ['input_ids', 'attention_mask']


   def __init__(self					:						Optional[int]	,  __UpperCAmelCase					:						List[str]=None	,  __UpperCAmelCase					:						Any=None	,  __UpperCAmelCase					:						Union[str, Any]="<pad>"	,  __UpperCAmelCase					:						List[str]="</s>"	,  __UpperCAmelCase					:						Union[str, Any]="<unk>"	,  __UpperCAmelCase					:						int="<mask_2>"	,  __UpperCAmelCase					:						Optional[Any]="<mask_1>"	,  __UpperCAmelCase					:						Union[str, Any]=None	,  __UpperCAmelCase					:						str=1_0_3	,  **__UpperCAmelCase					:						str	,  )						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  offset

         if additional_special_tokens is not None:
               if not isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
                     raise TypeError(
                         f"""additional_special_tokens should be of type {type(__UpperCAmelCase		)}, but is"""
                         f""" {type(__UpperCAmelCase		)}"""		)

               UpperCAmelCase__								=  (
                   ([mask_token_sent] + additional_special_tokens)
                   if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                   else additional_special_tokens
               )
               # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
               additional_special_tokens_extended += [
                   f"""<unk_{i}>""" for i in range(len(__UpperCAmelCase		)	,  self.offset - 1		)
               ]

               if len(set(__UpperCAmelCase		)		) != len(__UpperCAmelCase		):
                     raise ValueError(
                         "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                         f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""		)
               UpperCAmelCase__								=  additional_special_tokens_extended
         else:
               UpperCAmelCase__								=  [mask_token_sent] if mask_token_sent is not None else []
               additional_special_tokens += [f"""<unk_{i}>""" for i in range(2	,  self.offset		)]

         super().__init__(
             __UpperCAmelCase	,  tokenizer_file=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  eos_token=__UpperCAmelCase	,  unk_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  mask_token_sent=__UpperCAmelCase	,  offset=__UpperCAmelCase	,  additional_special_tokens=__UpperCAmelCase	,  **__UpperCAmelCase	,  )
         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  False if not self.vocab_file else True


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  set(self.all_special_ids		)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens		) + 3		)		):
               raise ValueError(
                   "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                   f""" {len(self.additional_special_tokens		)} additional_special_tokens, but got {all_special_ids}"""		)

         return [1 if x in all_special_ids else 0 for x in seq]


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List	,  __UpperCAmelCase					:						Optional[List] = None	,  __UpperCAmelCase					:						bool = False		)						->    List[int]:
         """simple docstring"""



         if already_has_special_tokens:
               return self._special_token_mask(__UpperCAmelCase		)
         elif token_ids_a is None:
               return self._special_token_mask(__UpperCAmelCase		) + [1]
         else:
               return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Optional[Any]=None		)						->    List[int]:
         """simple docstring"""



         if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    Tuple[str]:
         """simple docstring"""



         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

         if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCAmelCase		):
               copyfile(self.vocab_file	,  __UpperCAmelCase		)

         return (out_vocab_file,)



