






import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__snake_case      :      Union[str, Any]             =       logging.get_logger("""transformers.models.encodec""")

__snake_case      :      Union[str, Any]             =       {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
__snake_case      :      Dict             =       {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
__snake_case      :      List[str]             =       {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
__snake_case      :      List[str]             =       {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
__snake_case      :      Any             =       {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
__snake_case      :      Optional[Any]             =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case      :      List[Any]             =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case      :      int             =       []
__snake_case      :      List[str]             =       []







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Optional[Any]						)					->				Dict:

      """simple docstring"""



      for attribute in key.split('.'						):
            lowerCAmelCase__        =  getattr(__a      ,    __a						)

      if weight_type is not None:
            lowerCAmelCase__        =  getattr(__a      ,    __a						).shape
      else:
            lowerCAmelCase__        =  hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
                F" {value.shape} for {full_name}"						)

      if weight_type == "weight":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_g":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_v":
            lowerCAmelCase__        =  value
      elif weight_type == "bias":
            lowerCAmelCase__        =  value
      elif weight_type == "running_mean":
            lowerCAmelCase__        =  value
      elif weight_type == "running_var":
            lowerCAmelCase__        =  value
      elif weight_type == "num_batches_tracked":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_ih_l0":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_hh_l0":
            lowerCAmelCase__        =  value
      elif weight_type == "bias_ih_l0":
            lowerCAmelCase__        =  value
      elif weight_type == "bias_hh_l0":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_ih_l1":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_hh_l1":
            lowerCAmelCase__        =  value
      elif weight_type == "bias_ih_l1":
            lowerCAmelCase__        =  value
      elif weight_type == "bias_hh_l1":
            lowerCAmelCase__        =  value
      else:
            lowerCAmelCase__        =  value

      logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Any						)					->				Optional[int]:

      """simple docstring"""



      for key in ignore_keys:
            if key.endswith('.*'						):
                  if name.startswith(key[:-1]						):
                        return True
            elif ".*." in key:
                  lowerCAmelCase__        =  key.split('.*.'						)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Union[str, Any]						)					->				Optional[int]:

      """simple docstring"""



      lowerCAmelCase__        =  []

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowerCAmelCase__        =  MAPPING_24K
      elif model_name == "encodec_48khz":
            lowerCAmelCase__        =  MAPPING_48K
      else:
            raise ValueError(F"Unsupported model: {model_name}"						)

      for name, value in orig_dict.items():
            if should_ignore(__a      ,    __a						):
                  logger.info(F"{name} was ignored"						)
                  continue

            lowerCAmelCase__        =  False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowerCAmelCase__        =  key.split('.*.'						)
                        if prefix in name and suffix in name:
                              lowerCAmelCase__        =  suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith('embed'						) and name.endswith('embed_avg'						):
                              continue

                        lowerCAmelCase__        =  True
                        if "*" in mapped_key:
                              lowerCAmelCase__        =  name.split(__a						)[0].split('.'						)[-2]
                              lowerCAmelCase__        =  mapped_key.replace('*'      ,    __a						)
                        if "weight_g" in name:
                              lowerCAmelCase__        =  'weight_g'
                        elif "weight_v" in name:
                              lowerCAmelCase__        =  'weight_v'
                        elif "weight_ih_l0" in name:
                              lowerCAmelCase__        =  'weight_ih_l0'
                        elif "weight_hh_l0" in name:
                              lowerCAmelCase__        =  'weight_hh_l0'
                        elif "bias_ih_l0" in name:
                              lowerCAmelCase__        =  'bias_ih_l0'
                        elif "bias_hh_l0" in name:
                              lowerCAmelCase__        =  'bias_hh_l0'
                        elif "weight_ih_l1" in name:
                              lowerCAmelCase__        =  'weight_ih_l1'
                        elif "weight_hh_l1" in name:
                              lowerCAmelCase__        =  'weight_hh_l1'
                        elif "bias_ih_l1" in name:
                              lowerCAmelCase__        =  'bias_ih_l1'
                        elif "bias_hh_l1" in name:
                              lowerCAmelCase__        =  'bias_hh_l1'
                        elif "bias" in name:
                              lowerCAmelCase__        =  'bias'
                        elif "weight" in name:
                              lowerCAmelCase__        =  'weight'
                        elif "running_mean" in name:
                              lowerCAmelCase__        =  'running_mean'
                        elif "running_var" in name:
                              lowerCAmelCase__        =  'running_var'
                        elif "num_batches_tracked" in name:
                              lowerCAmelCase__        =  'num_batches_tracked'
                        else:
                              lowerCAmelCase__        =  None
                        set_recursively(__a      ,    __a      ,    __a      ,    __a      ,    __a						)
                  continue
            if not is_used:
                  unused_weights.append(__a						)

      logger.warning(F"Unused weights: {unused_weights}"						)







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   str=None      ,    UpperCamelCase_    :   Dict=None      ,    )					->				Optional[Any]:

      """simple docstring"""



      if config_path is not None:
            lowerCAmelCase__        =  EncodecConfig.from_pretrained(__a						)
      else:
            lowerCAmelCase__        =  EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowerCAmelCase__        =  [8, 5, 4, 4]
            lowerCAmelCase__        =  [2.2]
            lowerCAmelCase__        =  64
            lowerCAmelCase__        =  3_2000
            lowerCAmelCase__        =  2048
            lowerCAmelCase__        =  False
            lowerCAmelCase__        =  False
            lowerCAmelCase__        =  False
      elif model_name == "encodec_48khz":
            lowerCAmelCase__        =  [8, 5, 4, 2]
            lowerCAmelCase__        =  [3.0, 6.0, 12.0, 24.0]
            lowerCAmelCase__        =  4_8000
            lowerCAmelCase__        =  2
            lowerCAmelCase__        =  False
            lowerCAmelCase__        =  'time_group_norm'
            lowerCAmelCase__        =  True
            lowerCAmelCase__        =  1.0
            lowerCAmelCase__        =  0.01
      else:
            raise ValueError(F"Unknown model name: {model_name}"						)

      lowerCAmelCase__        =  EncodecModel(__a						)

      lowerCAmelCase__        =  EncodecFeatureExtractor(
          feature_size=config.audio_channels      ,    sampling_rate=config.sampling_rate      ,    chunk_length_s=config.chunk_length_s      ,    overlap=config.overlap      ,    )
      feature_extractor.save_pretrained(__a						)

      lowerCAmelCase__        =  torch.load(__a						)
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            lowerCAmelCase__        =  original_checkpoint['best_state']
      recursively_load_weights(__a      ,    __a      ,    __a						)
      model.save_pretrained(__a						)

      if repo_id:
            print('Pushing to the hub...'						)
            feature_extractor.push_to_hub(__a						)
            model.push_to_hub(__a						)


if __name__ == "__main__":
     __snake_case      :      int             =       argparse.ArgumentParser()
     parser.add_argument(
         """--model""",
         default="""encodec_24khz""",
         type=str,
         help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
     )
     parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
     )

     __snake_case      :      str             =       parser.parse_args()
     convert_checkpoint(
         args.model,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )













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

import datasets


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

__snake_case      :      List[str]             =       """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

__snake_case      :      Dict             =       """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""


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






						def  UpperCamelCase__ (		self				):






												"""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       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  len(references[0]				)
												if any(len(_UpperCamelCase				) != references_per_prediction for refs in references				):
																		raise ValueError('Sacrebleu requires the same number of references for each prediction'				)
												lowerCAmelCase__        =  [[refs[i] for refs in references] for i in range(_UpperCamelCase				)]

												lowerCAmelCase__        =  TER(
												    normalized=_UpperCamelCase       ,  no_punct=_UpperCamelCase       ,  asian_support=_UpperCamelCase       ,  case_sensitive=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  sb_ter.corpus_score(_UpperCamelCase       ,  _UpperCamelCase				)

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





