




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 				_A  (		lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   tuple  ,							lowerCAmelCase_	:   Path  ,							lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   Union[str, Any]=False  ,							):
     """simple docstring"""

     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 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   int  ,							lowerCAmelCase_	:   bool = False     ):
     """simple docstring"""

     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(lowerCAmelCase_  ,							torch_dtype=lowerCAmelCase_     ).to(lowerCAmelCase_     )
     lowerCAmelCase__            =					Path(lowerCAmelCase_     )

     # 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=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
     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  ,							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_  ,							)
     lowerCAmelCase__            =					str(unet_path.absolute().as_posix()     )
     lowerCAmelCase__            =					os.path.dirname(lowerCAmelCase_     )
     lowerCAmelCase__            =					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
     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 lowerCAmelCase_  ,							lowerCAmelCase_     : 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
     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(
         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:
          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  ,							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
          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=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
     lowerCAmelCase__            =					OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_  ,							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=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								=	parser.parse_args()

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




import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCAmelCase:						Union[str, Any]   				=       '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

_lowerCAmelCase:						Dict   				=       '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

_lowerCAmelCase:						List[Any]   				=       '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION   )
class       lowercase_					(datasets.Metric   ):





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return datasets.MetricInfo(
											    description=_DESCRIPTION       ,  citation=_CITATION       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
											        {
											            'predictions': datasets.Value('string'       ,  id='sequence'),
											            'references': datasets.Value('string'       ,  id='sequence'),
											        })       ,  codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']       ,  reference_urls=[
											        'https://en.wikipedia.org/wiki/ROUGE_(metric)',
											        'https://github.com/google-research/google-research/tree/master/rouge',
											    ]       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=False)  -> Any:
											if rouge_types is None:
																		a__      =['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

											a__      =rouge_scorer.RougeScorer(rouge_types=lowercase_       ,  use_stemmer=lowercase_)
											if use_aggregator:
																		a__      =scoring.BootstrapAggregator()
											else:
																		a__      =[]

											for ref, pred in zip(lowercase_       ,  lowercase_):
																		a__      =scorer.score(lowercase_       ,  lowercase_)
																		if use_aggregator:
																									aggregator.add_scores(lowercase_)
																		else:
																									scores.append(lowercase_)

											if use_aggregator:
																		a__      =aggregator.aggregate()
											else:
																		a__      ={}
																		for key in scores[0]:
																									a__      =[score[key] for score in scores]

											return result

