


import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


UpperCAmelCase__   	=							"""\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

UpperCAmelCase__   	=							"""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

UpperCAmelCase__   	=							"""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""







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





     def      __A					(self       )     ->   int:
      return datasets.MetricInfo(
          description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
              {
                  '''predictions''': datasets.Sequence(datasets.Value('''string'''		,     id='''token'''       )		,     id='''sequence'''       ),
                  '''references''': datasets.Sequence(
                      datasets.Sequence(datasets.Value('''string'''		,     id='''token'''       )		,     id='''sequence'''       )		,     id='''references'''       ),
              }       )		,     codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']		,     reference_urls=[
              '''https://en.wikipedia.org/wiki/BLEU''',
              '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
          ]		,     )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=4		,     UpperCAmelCase=False       )     ->   Tuple:
      _lowercase							      =compute_bleu(
          reference_corpus=lowerCamelCase__		,     translation_corpus=lowerCamelCase__		,     max_order=lowerCamelCase__		,     smooth=lowerCamelCase__       )
      (_lowercase)							      =score
      return {
          "bleu": bleu,
          "precisions": precisions,
          "brevity_penalty": bp,
          "length_ratio": ratio,
          "translation_length": translation_length,
          "reference_length": reference_length,
      }






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE_:List[Any]             =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       Union[str, Any]          =   """cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
    else:
        A :       Any          =   """cpu"""
    A :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   vae_decoder.decode
    onnx_export(
        _lowerCAmelCase      ,     model_args=(
            torch.randn(1      ,     _lowerCAmelCase      ,     25      ,     25		).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 vae_decoder


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_: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""")

     SCREAMING_SNAKE_CASE_:Tuple             =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("""SD: Done: ONNX""")


