




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]				)   ->					Optional[Any]:
      SCREAMING_SNAKE_CASE_      		=     torch.exp(__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     torch.sum(__UpperCAmelCase     ,     dim=1				)  # sum of exp(x_i)
      SCREAMING_SNAKE_CASE_      		=     torch.sum(x * exp_x     ,     dim=1				)  # sum of x_i * exp(x_i)
      return torch.log(__UpperCAmelCase				) - B / A





class 			lowerCamelCase_				( nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[Any]		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     config.output_attentions
       SCREAMING_SNAKE_CASE_      		=     config.output_hidden_states
       SCREAMING_SNAKE_CASE_      		=     nn.ModuleList([BertLayer(_lowerCAmelCase		) for _ in range(config.num_hidden_layers		)]		)
       SCREAMING_SNAKE_CASE_      		=     nn.ModuleList([BertHighway(_lowerCAmelCase		) for _ in range(config.num_hidden_layers		)]		)

       SCREAMING_SNAKE_CASE_      		=     [-1 for _ in range(config.num_hidden_layers		)]



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Optional[Any]		):
       if (type(_lowerCAmelCase		) is float) or (type(_lowerCAmelCase		) is int):
             for i in range(len(self.early_exit_entropy		)		):
                   SCREAMING_SNAKE_CASE_      		=     x
       else:
             SCREAMING_SNAKE_CASE_      		=     x



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     pooler.state_dict()
       for highway in self.highway:
             for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name]		)




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Union[str, Any]=None							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						List[Any]=None							,							_lowerCAmelCase    :						Optional[Any]=None							,							):
       SCREAMING_SNAKE_CASE_      		=     ()
       SCREAMING_SNAKE_CASE_      		=     ()
       SCREAMING_SNAKE_CASE_      		=     ()
       for i, layer_module in enumerate(self.layer		):
             if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE_      		=     all_hidden_states + (hidden_states,)

             SCREAMING_SNAKE_CASE_      		=     layer_module(
                 _lowerCAmelCase							,							_lowerCAmelCase							,							head_mask[i]							,							_lowerCAmelCase							,							_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     layer_outputs[0]

             if self.output_attentions:
                   SCREAMING_SNAKE_CASE_      		=     all_attentions + (layer_outputs[1],)

             SCREAMING_SNAKE_CASE_      		=     (hidden_states,)
             if self.output_hidden_states:
                   SCREAMING_SNAKE_CASE_      		=     current_outputs + (all_hidden_states,)
             if self.output_attentions:
                   SCREAMING_SNAKE_CASE_      		=     current_outputs + (all_attentions,)

             SCREAMING_SNAKE_CASE_      		=     self.highway[i](_lowerCAmelCase		)
             # logits, pooled_output

             if not self.training:
                   SCREAMING_SNAKE_CASE_      		=     highway_exit[0]
                   SCREAMING_SNAKE_CASE_      		=     entropy(_lowerCAmelCase		)
                   SCREAMING_SNAKE_CASE_      		=     highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                   SCREAMING_SNAKE_CASE_      		=     all_highway_exits + (highway_exit,)

                   if highway_entropy < self.early_exit_entropy[i]:
                         SCREAMING_SNAKE_CASE_      		=     (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                         raise HighwayException(_lowerCAmelCase							,							i + 1		)
             else:
                   SCREAMING_SNAKE_CASE_      		=     all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     all_hidden_states + (hidden_states,)

       SCREAMING_SNAKE_CASE_      		=     (hidden_states,)
       if self.output_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     outputs + (all_hidden_states,)
       if self.output_attentions:
             SCREAMING_SNAKE_CASE_      		=     outputs + (all_attentions,)

       SCREAMING_SNAKE_CASE_      		=     outputs + (all_highway_exits,)
       return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "			,  _SCREAMING_SNAKE_CASE			,  )
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Tuple		):
       super().__init__(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     config

       SCREAMING_SNAKE_CASE_      		=     BertEmbeddings(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     DeeBertEncoder(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     BertPooler(_lowerCAmelCase		)

       self.init_weights()



 def  lowerCAmelCase_    (				self    :						int		):
       self.encoder.init_highway_pooler(self.pooler		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       return self.embeddings.word_embeddings



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     value



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Tuple		):
       for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(_lowerCAmelCase		)




 @add_start_docstrings_to_model_forward(_lowerCAmelCase		)
 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						str=None							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						str=None							,							_lowerCAmelCase    :						str=None							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						Any=None							,							):
       if input_ids is not None and inputs_embeds is not None:
             raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'		)
       elif input_ids is not None:
             SCREAMING_SNAKE_CASE_      		=     input_ids.size()
       elif inputs_embeds is not None:
             SCREAMING_SNAKE_CASE_      		=     inputs_embeds.size()[:-1]
       else:
             raise ValueError('You have to specify either input_ids or inputs_embeds'		)

       SCREAMING_SNAKE_CASE_      		=     input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
             SCREAMING_SNAKE_CASE_      		=     torch.ones(_lowerCAmelCase							,							device=_lowerCAmelCase		)
       if encoder_attention_mask is None:
             SCREAMING_SNAKE_CASE_      		=     torch.ones(_lowerCAmelCase							,							device=_lowerCAmelCase		)
       if token_type_ids is None:
             SCREAMING_SNAKE_CASE_      		=     torch.zeros(_lowerCAmelCase							,							dtype=torch.long							,							device=_lowerCAmelCase		)

       # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
       # ourselves in which case we just need to make it broadcastable to all heads.
       SCREAMING_SNAKE_CASE_      		=     self.get_extended_attention_mask(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

       # If a 2D ou 3D attention mask is provided for the cross-attention
       # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
       if encoder_attention_mask.dim() == 3:
             SCREAMING_SNAKE_CASE_      		=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
             SCREAMING_SNAKE_CASE_      		=     encoder_attention_mask[:, None, None, :]

       SCREAMING_SNAKE_CASE_      		=     encoder_extended_attention_mask.to(
           dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
       SCREAMING_SNAKE_CASE_      		=     (1.0 - encoder_extended_attention_mask) * -1_0000.0

       # Prepare head mask if needed
       # 1.0 in head_mask indicate we keep the head
       # attention_probs has shape bsz x n_heads x N x N
       # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
       # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
       SCREAMING_SNAKE_CASE_      		=     self.get_head_mask(_lowerCAmelCase							,							self.config.num_hidden_layers		)

       SCREAMING_SNAKE_CASE_      		=     self.embeddings(
           input_ids=_lowerCAmelCase							,							position_ids=_lowerCAmelCase							,							token_type_ids=_lowerCAmelCase							,							inputs_embeds=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.encoder(
           _lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							encoder_hidden_states=_lowerCAmelCase							,							encoder_attention_mask=_lowerCAmelCase							,							)
       SCREAMING_SNAKE_CASE_      		=     encoder_outputs[0]
       SCREAMING_SNAKE_CASE_      		=     self.pooler(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     (
           sequence_output,
           pooled_output,
       ) + encoder_outputs[
           1:
       ]  # add hidden_states and attentions if they are here
       return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     message
       SCREAMING_SNAKE_CASE_      		=     exit_layer  # start from 1!





class 			lowerCamelCase_				( nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						int		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     BertPooler(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     nn.Dropout(config.hidden_dropout_prob		)
       SCREAMING_SNAKE_CASE_      		=     nn.Linear(config.hidden_size							,							config.num_labels		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Optional[Any]		):
       # Pooler
       SCREAMING_SNAKE_CASE_      		=     encoder_outputs[0]
       SCREAMING_SNAKE_CASE_      		=     self.pooler(_lowerCAmelCase		)
       # "return" pooler_output

       # BertModel
       SCREAMING_SNAKE_CASE_      		=     (pooler_input, pooler_output) + encoder_outputs[1:]
       # "return" bmodel_output

       # Dropout and classification
       SCREAMING_SNAKE_CASE_      		=     bmodel_output[1]

       SCREAMING_SNAKE_CASE_      		=     self.dropout(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.classifier(_lowerCAmelCase		)

       return logits, pooled_output





@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "			,  _SCREAMING_SNAKE_CASE			,  )
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]		):
       super().__init__(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     config.num_labels
       SCREAMING_SNAKE_CASE_      		=     config.num_hidden_layers

       SCREAMING_SNAKE_CASE_      		=     DeeBertModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     nn.Dropout(config.hidden_dropout_prob		)
       SCREAMING_SNAKE_CASE_      		=     nn.Linear(config.hidden_size							,							self.config.num_labels		)

       self.init_weights()




 @add_start_docstrings_to_model_forward(_lowerCAmelCase		)
 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						List[str]=None							,							_lowerCAmelCase    :						Optional[Any]=None							,							_lowerCAmelCase    :						List[Any]=None							,							_lowerCAmelCase    :						List[str]=None							,							_lowerCAmelCase    :						int=None							,							_lowerCAmelCase    :						List[Any]=None							,							_lowerCAmelCase    :						List[str]=-1							,							_lowerCAmelCase    :						List[str]=False							,							):
       SCREAMING_SNAKE_CASE_      		=     self.num_layers
       try:
             SCREAMING_SNAKE_CASE_      		=     self.bert(
                 _lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							token_type_ids=_lowerCAmelCase							,							position_ids=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							inputs_embeds=_lowerCAmelCase							,							)
             # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

             SCREAMING_SNAKE_CASE_      		=     outputs[1]

             SCREAMING_SNAKE_CASE_      		=     self.dropout(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     self.classifier(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
             SCREAMING_SNAKE_CASE_      		=     e.message
             SCREAMING_SNAKE_CASE_      		=     e.exit_layer
             SCREAMING_SNAKE_CASE_      		=     outputs[0]

       if not self.training:
             SCREAMING_SNAKE_CASE_      		=     entropy(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     []
             SCREAMING_SNAKE_CASE_      		=     []
       if labels is not None:
             if self.num_labels == 1:
                   #  We are doing regression
                   SCREAMING_SNAKE_CASE_      		=     MSELoss()
                   SCREAMING_SNAKE_CASE_      		=     loss_fct(logits.view(-1		)							,							labels.view(-1		)		)
             else:
                   SCREAMING_SNAKE_CASE_      		=     CrossEntropyLoss()
                   SCREAMING_SNAKE_CASE_      		=     loss_fct(logits.view(-1							,							self.num_labels		)							,							labels.view(-1		)		)

             # work with highway exits
             SCREAMING_SNAKE_CASE_      		=     []
             for highway_exit in outputs[-1]:
                   SCREAMING_SNAKE_CASE_      		=     highway_exit[0]
                   if not self.training:
                         highway_logits_all.append(_lowerCAmelCase		)
                         highway_entropy.append(highway_exit[2]		)
                   if self.num_labels == 1:
                         #  We are doing regression
                         SCREAMING_SNAKE_CASE_      		=     MSELoss()
                         SCREAMING_SNAKE_CASE_      		=     loss_fct(highway_logits.view(-1		)							,							labels.view(-1		)		)
                   else:
                         SCREAMING_SNAKE_CASE_      		=     CrossEntropyLoss()
                         SCREAMING_SNAKE_CASE_      		=     loss_fct(highway_logits.view(-1							,							self.num_labels		)							,							labels.view(-1		)		)
                   highway_losses.append(_lowerCAmelCase		)

             if train_highway:
                   SCREAMING_SNAKE_CASE_      		=     (sum(highway_losses[:-1]		),) + outputs
                   # exclude the final highway, of course
             else:
                   SCREAMING_SNAKE_CASE_      		=     (loss,) + outputs
       if not self.training:
             SCREAMING_SNAKE_CASE_      		=     outputs + ((original_entropy, highway_entropy), exit_layer)
             if output_layer >= 0:
                   SCREAMING_SNAKE_CASE_      		=     (
                       (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                   )  # use the highway of the last layer

       return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)

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

import datasets


__A							='''\
@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",
}
'''

__A							='''\
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.
'''

__A							='''
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        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					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        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase = False		,    lowercase = False		,    lowercase = False		,    lowercase = False		,    )							->			Tuple:
					lowerCamelCase_         =     len(references[0]					)
					if any(len(lowercase					) != 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(lowercase					)]

					lowerCamelCase_         =     TER(
					    normalized=lowercase		,    no_punct=lowercase		,    asian_support=lowercase		,    case_sensitive=lowercase		,    )
					lowerCamelCase_         =     sb_ter.corpus_score(lowercase		,    lowercase					)

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


