

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


lowercase_			=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     tuple   ,      SCREAMING_SNAKE_CASE__      :     Path   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str=False   ,      )							->				Dict:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__   ,      exist_ok=SCREAMING_SNAKE_CASE__				)
    # 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(
            SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      f=output_path.as_posix()   ,      input_names=SCREAMING_SNAKE_CASE__   ,      output_names=SCREAMING_SNAKE_CASE__   ,      dynamic_axes=SCREAMING_SNAKE_CASE__   ,      do_constant_folding=SCREAMING_SNAKE_CASE__   ,      use_external_data_format=SCREAMING_SNAKE_CASE__   ,      enable_onnx_checker=SCREAMING_SNAKE_CASE__   ,      opset_version=SCREAMING_SNAKE_CASE__   ,      )
    else:
        export(
            SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      f=output_path.as_posix()   ,      input_names=SCREAMING_SNAKE_CASE__   ,      output_names=SCREAMING_SNAKE_CASE__   ,      dynamic_axes=SCREAMING_SNAKE_CASE__   ,      do_constant_folding=SCREAMING_SNAKE_CASE__   ,      opset_version=SCREAMING_SNAKE_CASE__   ,      )
@torch.no_grad()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     bool = False				)							->				int:





    '''simple docstring'''






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

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

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

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

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

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

    A__   				=       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=SCREAMING_SNAKE_CASE__   ,      feature_extractor=SCREAMING_SNAKE_CASE__   ,      requires_safety_checker=safety_checker is not None   ,      )

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

    del pipeline
    del onnx_pipeline
    A__   				=       OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__   ,      provider='CPUExecutionProvider'				)
    print('ONNX pipeline is loadable'				)


if __name__ == "__main__":
     lowercase_			=  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")

     lowercase_			=  parser.parse_args()

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




from jiwer import compute_measures

import datasets


lowercase_			=  "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowercase_			=  "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

lowercase_			=  "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"


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







    """simple docstring"""





    def 						snake_case__					(				self			:	Any		)->   str:





        '''simple docstring'''







        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/jitsi/jiwer/'],reference_urls=[
                'https://en.wikipedia.org/wiki/Word_error_rate',
            ],)


    def 						snake_case__					(				self			:	int,lowercase_			:	Any=None,lowercase_			:	List[str]=None,lowercase_			:	Dict=False		)->   Optional[int]:





        '''simple docstring'''







        if concatenate_texts:
            return compute_measures(lowercase_,lowercase_		)["wer"]
        else:
            A__   				=       0
            A__   				=       0
            for prediction, reference in zip(lowercase_,lowercase_		):
                A__   				=       compute_measures(lowercase_,lowercase_		)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total

