





import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_UpperCAmelCase             =							{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 13_1072,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
}






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:int   )      ->   List[str]:
 return torch.atana(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ) / math.pi * 2






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   int:
 __lowerCAmelCase					:	Any      =       torch.sin(t * math.pi / 2   ) ** 2
 __lowerCAmelCase					:	str      =       (1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






class        snake_case_    (		__lowercase						):
  pass






class        snake_case_    (		nn.Module						):






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					Optional[int]      )->Any:






   '''simple docstring'''

   super().__init__()

   __lowerCAmelCase					:	Dict      =       DiffusionAttnUnetaD(_snake_case       ,  n_attn_layers=4      )
   __lowerCAmelCase					:	Union[str, Any]      =       deepcopy(self.diffusion      )
   __lowerCAmelCase					:	int      =       torch.quasirandom.SobolEngine(1       ,  scramble=_snake_case      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   List[str]:
 __lowerCAmelCase					:	Optional[Any]      =       MODELS_MAP[model_name]["""url"""]
 os.system(F'''wget {url} ./'''   )

 return F'''./{model_name}.ckpt'''


_UpperCAmelCase             =							{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
_UpperCAmelCase             =							{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
_UpperCAmelCase             =							{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
_UpperCAmelCase             =							{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

_UpperCAmelCase             =							{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

_UpperCAmelCase             =							{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   )      ->   Dict:
 if name.startswith("""skip"""   ):
  return name.replace("""skip"""   ,   RES_CONV_MAP["""skip"""]   )

 # name has to be of format main.{digit}
 if not name.startswith("""main."""   ):
  raise ValueError(F'''ResConvBlock error with {name}'''   )

 return name.replace(name[:6]   ,   RES_CONV_MAP[name[:6]]   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   )      ->   Optional[int]:
 for key, value in ATTN_MAP.items():
  if name.startswith(SCREAMING_SNAKE_CASE   ) and not isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
   return name.replace(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  elif name.startswith(SCREAMING_SNAKE_CASE   ):
   return [name.replace(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ) for v in value]
 raise ValueError(F'''Attn error with {name}'''   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Optional[int]=13   )      ->   Dict:
 __lowerCAmelCase					:	List[Any]      =       input_string

 if string.split("""."""   )[0] == "timestep_embed":
  return string.replace("""timestep_embed"""   ,   """time_proj"""   )

 __lowerCAmelCase					:	Tuple      =       0
 if string.startswith("""net.3."""   ):
  depth += 1
  __lowerCAmelCase					:	Dict      =       string[6:]
 elif string.startswith("""net."""   ):
  __lowerCAmelCase					:	int      =       string[4:]

 while string.startswith("""main.7."""   ):
  depth += 1
  __lowerCAmelCase					:	Tuple      =       string[7:]

 if string.startswith("""main."""   ):
  __lowerCAmelCase					:	List[Any]      =       string[5:]

 # mid block
 if string[:2].isdigit():
  __lowerCAmelCase					:	Optional[Any]      =       string[:2]
  __lowerCAmelCase					:	Union[str, Any]      =       string[2:]
 else:
  __lowerCAmelCase					:	List[str]      =       string[0]
  __lowerCAmelCase					:	Optional[int]      =       string[1:]

 if depth == max_depth:
  __lowerCAmelCase					:	Optional[Any]      =       MID_NUM_TO_LAYER[layer_num]
  __lowerCAmelCase					:	Any      =       """mid_block"""
 elif depth > 0 and int(SCREAMING_SNAKE_CASE   ) < 7:
  __lowerCAmelCase					:	str      =       DOWN_NUM_TO_LAYER[layer_num]
  __lowerCAmelCase					:	Tuple      =       F'''down_blocks.{depth}'''
 elif depth > 0 and int(SCREAMING_SNAKE_CASE   ) > 7:
  __lowerCAmelCase					:	Tuple      =       UP_NUM_TO_LAYER[layer_num]
  __lowerCAmelCase					:	Tuple      =       F'''up_blocks.{max_depth - depth - 1}'''
 elif depth == 0:
  __lowerCAmelCase					:	Optional[Any]      =       DEPTH_0_TO_LAYER[layer_num]
  __lowerCAmelCase					:	List[Any]      =       F'''up_blocks.{max_depth - 1}''' if int(SCREAMING_SNAKE_CASE   ) > 3 else """down_blocks.0"""

 if not string_left.startswith("""."""   ):
  raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''   )

 __lowerCAmelCase					:	List[Any]      =       string_left[1:]

 if "resnets" in new_layer:
  __lowerCAmelCase					:	Optional[int]      =       convert_resconv_naming(SCREAMING_SNAKE_CASE   )
 elif "attentions" in new_layer:
  __lowerCAmelCase					:	str      =       convert_attn_naming(SCREAMING_SNAKE_CASE   )
  __lowerCAmelCase					:	List[str]      =       new_string_left

 if not isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase					:	List[Any]      =       prefix + """.""" + new_layer + """.""" + string_left
 else:
  __lowerCAmelCase					:	Optional[int]      =       [prefix + """.""" + new_layer + """.""" + s for s in string_left]
 return new_string






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   Optional[Any]:
 __lowerCAmelCase					:	Optional[int]      =       {}
 for k, v in state_dict.items():
  if k.endswith("""kernel"""   ):
   # up- and downsample layers, don't have trainable weights
   continue

  __lowerCAmelCase					:	str      =       rename(SCREAMING_SNAKE_CASE   )

  # check if we need to transform from Conv => Linear for attention
  if isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
   __lowerCAmelCase					:	Optional[int]      =       transform_conv_attns(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  else:
   __lowerCAmelCase					:	List[str]      =       v

 return new_state_dict






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:Any   )      ->   str:
 if len(SCREAMING_SNAKE_CASE   ) == 1:
  if len(v.shape   ) == 3:
   # weight
   __lowerCAmelCase					:	Tuple      =       v[:, :, 0]
  else:
   # bias
   __lowerCAmelCase					:	List[str]      =       v
 else:
  # qkv matrices
  __lowerCAmelCase					:	Tuple      =       v.shape[0]
  __lowerCAmelCase					:	Any      =       trippled_shape // 3
  for i in range(3   ):
   if len(v.shape   ) == 3:
    __lowerCAmelCase					:	str      =       v[i * single_shape : (i + 1) * single_shape, :, 0]
   else:
    __lowerCAmelCase					:	Any      =       v[i * single_shape : (i + 1) * single_shape]
 return new_state_dict






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   List[Any]:
 __lowerCAmelCase					:	int      =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

 __lowerCAmelCase					:	Any      =       args.model_path.split("""/"""   )[-1].split("""."""   )[0]
 if not os.path.isfile(args.model_path   ):
  assert (
      model_name == args.model_path
  ), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
  __lowerCAmelCase					:	Union[str, Any]      =       download(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	int      =       MODELS_MAP[model_name]["""sample_rate"""]
 __lowerCAmelCase					:	Optional[Any]      =       MODELS_MAP[model_name]["""sample_size"""]

 __lowerCAmelCase					:	Dict      =       Object()
 __lowerCAmelCase					:	int      =       sample_size
 __lowerCAmelCase					:	Tuple      =       sample_rate
 __lowerCAmelCase					:	int      =       0

 __lowerCAmelCase					:	Tuple      =       UNetaDModel(sample_size=SCREAMING_SNAKE_CASE   ,   sample_rate=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Tuple      =       diffusers_model.state_dict()

 __lowerCAmelCase					:	Tuple      =       DiffusionUncond(SCREAMING_SNAKE_CASE   )
 orig_model.load_state_dict(torch.load(args.model_path   ,   map_location=SCREAMING_SNAKE_CASE   )["""state_dict"""]   )
 __lowerCAmelCase					:	List[Any]      =       orig_model.diffusion_ema.eval()
 __lowerCAmelCase					:	Optional[Any]      =       orig_model.state_dict()
 __lowerCAmelCase					:	Union[str, Any]      =       rename_orig_weights(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Tuple      =       set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
 __lowerCAmelCase					:	int      =       set(diffusers_state_dict.keys()   ) - set(renamed_state_dict.keys()   )

 assert len(SCREAMING_SNAKE_CASE   ) == 0, F'''Problem with {renamed_minus_diffusers}'''
 assert all(k.endswith("""kernel"""   ) for k in list(SCREAMING_SNAKE_CASE   )   ), F'''Problem with {diffusers_minus_renamed}'''

 for key, value in renamed_state_dict.items():
  assert (
      diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
  ), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
  if key == "time_proj.weight":
   __lowerCAmelCase					:	List[Any]      =       value.squeeze()

  __lowerCAmelCase					:	Optional[int]      =       value

 diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	str      =       100
 __lowerCAmelCase					:	int      =       33

 __lowerCAmelCase					:	Tuple      =       IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       torch.manual_seed(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       torch.randn([1, 2, config.sample_size]   ,   generator=SCREAMING_SNAKE_CASE   ).to(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Any      =       torch.linspace(1   ,   0   ,   steps + 1   ,   device=SCREAMING_SNAKE_CASE   )[:-1]
 __lowerCAmelCase					:	List[Any]      =       get_crash_schedule(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE   ,   scheduler=SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       torch.manual_seed(33   )
 __lowerCAmelCase					:	Any      =       pipe(num_inference_steps=SCREAMING_SNAKE_CASE   ,   generator=SCREAMING_SNAKE_CASE   ).audios

 __lowerCAmelCase					:	Union[str, Any]      =       sampling.iplms_sample(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   {}   )
 __lowerCAmelCase					:	Union[str, Any]      =       generated.clamp(-1   ,   1   )

 __lowerCAmelCase					:	List[Any]      =       (generated - audio).abs().sum()
 __lowerCAmelCase					:	Optional[int]      =       (generated - audio).abs().max()

 if args.save:
  pipe.save_pretrained(args.checkpoint_path   )

 print("""Diff sum"""   ,   SCREAMING_SNAKE_CASE   )
 print("""Diff max"""   ,   SCREAMING_SNAKE_CASE   )

 assert diff_max < 1e-3, F'''Diff max: {diff_max} is too much :-/'''

 print(F'''Conversion for {model_name} successful!'''   )


if __name__ == "__main__":
      _UpperCAmelCase             =							argparse.ArgumentParser()

      parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
      parser.add_argument(
          '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
      )
      parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
      _UpperCAmelCase             =							parser.parse_args()

      main(args)






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_UpperCAmelCase             =							'\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_UpperCAmelCase             =							'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

_UpperCAmelCase             =							'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'






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






  def 				UpperCAmelCase__						(							self			:					Tuple      )->str:






   '''simple docstring'''

   if version.parse(scb.__version__      ) < version.parse("""1.4.12"""      ):
    raise ImportWarning(
        """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
        """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""      )
   return datasets.MetricInfo(
       description=_DESCRIPTION       ,  citation=_CITATION       ,  homepage="""http://www.cs.umd.edu/~snover/tercom/"""       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
           {
               """predictions""": datasets.Value("""string"""       ,  id="""sequence"""      ),
               """references""": datasets.Sequence(datasets.Value("""string"""       ,  id="""sequence"""      )       ,  id="""references"""      ),
           }      )       ,  codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""]       ,  reference_urls=[
           """https://github.com/jhclark/tercom""",
       ]       ,  )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					List[Any]       ,  _snake_case			:					int       ,  _snake_case			:					bool = False       ,  _snake_case			:					bool = False       ,  _snake_case			:					bool = False       ,  _snake_case			:					bool = False       ,  )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       len(references[0]      )
   if any(len(_snake_case      ) != references_per_prediction for refs in references      ):
    raise ValueError("""Sacrebleu requires the same number of references for each prediction"""      )
   __lowerCAmelCase					:	str      =       [[refs[i] for refs in references] for i in range(_snake_case      )]

   __lowerCAmelCase					:	Tuple      =       TER(
       normalized=_snake_case       ,  no_punct=_snake_case       ,  asian_support=_snake_case       ,  case_sensitive=_snake_case       ,  )
   __lowerCAmelCase					:	List[Any]      =       sb_ter.corpus_score(_snake_case       ,  _snake_case      )

   return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}