






from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
     import importlib_metadata
else:
     import importlib.metadata as importlib_metadata


UpperCAmelCase__							  =						""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):






     class 						lowercase_   (      tr.AbstractTransform  ):






           '''simple docstring'''




           def __init__( self :		List[Any]					,   __UpperCAmelCase :		str = " "    )       ->Union[str, Any]:




            """simple docstring"""


            a						    = sentence_delimiter







           def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->List[Any]:




            """simple docstring"""


            return list(__UpperCAmelCase    )







           def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List[str]    )       ->Optional[Any]:




            """simple docstring"""


            a						    = []
            for sent_idx, sentence in enumerate(__UpperCAmelCase    ):
             chars.extend(self.process_string(__UpperCAmelCase    )    )
             if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__UpperCAmelCase    ) - 1:
              chars.append(self.sentence_delimiter    )
            return chars
     UpperCAmelCase__							  =						tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
else:
     UpperCAmelCase__							  =						tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )


UpperCAmelCase__							  =						"\\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"

UpperCAmelCase__							  =						"\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (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 characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

UpperCAmelCase__							  =						"\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character 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    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"






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






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		List[str]    )       ->Dict:




       """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''',
               '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
           ]					,   )







      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		Optional[int]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Optional[Any]=False    )       ->List[Any]:




       """simple docstring"""


       if concatenate_texts:
        return jiwer.compute_measures(
            __UpperCAmelCase					,   __UpperCAmelCase					,   truth_transform=__UpperCAmelCase					,   hypothesis_transform=__UpperCAmelCase					,   )["wer"]

       a						    = 0
       a						    = 0
       for prediction, reference in zip(__UpperCAmelCase					,   __UpperCAmelCase    ):
        a						    = jiwer.compute_measures(
            __UpperCAmelCase					,   __UpperCAmelCase					,   truth_transform=__UpperCAmelCase					,   hypothesis_transform=__UpperCAmelCase					,   )
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total










import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 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					)				->     Union[str, Any]:
 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						    = Path(a					)

 # VAE DECODER
 a						    = AutoencoderKL.from_pretrained(model_path + '''/vae'''					)
 a						    = vae_decoder.config.latent_channels
 # forward only through the decoder part
 a						    = vae_decoder.decode
 onnx_export(
     a      ,				model_args=(
         torch.randn(1      ,				a      ,				25      ,				25					).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 vae_decoder


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()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")


