





"""simple docstring"""

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

import datasets


__UpperCAmelCase        ="""\
@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\",
}
"""

__UpperCAmelCase        ="""\
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.
"""

__UpperCAmelCase        ="""
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       lowerCAmelCase__				(   datasets.Metric  ):







 def  lowercase_       (   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  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__ = False		,	UpperCamelCase__ = False		,	UpperCamelCase__ = False		,	UpperCamelCase__ = False		,	):

    '''simple docstring'''


    A__											=					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"					)
    A__											=					[[refs[i] for refs in references] for i in range(UpperCAmelCase__					)]

    A__											=					TER(
        normalized=UpperCAmelCase__		,	no_punct=UpperCAmelCase__		,	asian_support=UpperCAmelCase__		,	case_sensitive=UpperCAmelCase__		,	)
    A__											=					sb_ter.corpus_score(UpperCAmelCase__		,	UpperCAmelCase__					)

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


'''simple docstring'''





from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)

# TODO Update this
UpperCamelCase_    						=       {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'esm'







				def __init__(       self     :		Optional[Any]		,					UpperCAmelCase__     :		str=None		,					UpperCAmelCase__     :		Dict=None		,					UpperCAmelCase__     :		Any=None		,					UpperCAmelCase__     :		int=768		,					UpperCAmelCase__     :		Union[str, Any]=12		,					UpperCAmelCase__     :		List[Any]=12		,					UpperCAmelCase__     :		Optional[Any]=3072		,					UpperCAmelCase__     :		Dict=0.1		,					UpperCAmelCase__     :		int=0.1		,					UpperCAmelCase__     :		int=1026		,					UpperCAmelCase__     :		Tuple=0.02		,					UpperCAmelCase__     :		Optional[int]=1E-12		,					UpperCAmelCase__     :		List[Any]="absolute"		,					UpperCAmelCase__     :		int=True		,					UpperCAmelCase__     :		Dict=None		,					UpperCAmelCase__     :		str=False		,					UpperCAmelCase__     :		int=False		,					UpperCAmelCase__     :		Optional[Any]=None		,					UpperCAmelCase__     :		List[str]=None		,					**UpperCAmelCase__     :		int		,					):
										'''simple docstring'''
										super().__init__(pad_token_id=UpperCAmelCase__		,					mask_token_id=UpperCAmelCase__		,					**UpperCAmelCase__   )

										lowercase :							Any    		=vocab_size
										lowercase :							List[Any]    		=hidden_size
										lowercase :							Any    		=num_hidden_layers
										lowercase :							Optional[Any]    		=num_attention_heads
										lowercase :							Tuple    		=intermediate_size
										lowercase :							int    		=hidden_dropout_prob
										lowercase :							Dict    		=attention_probs_dropout_prob
										lowercase :							Optional[int]    		=max_position_embeddings
										lowercase :							Union[str, Any]    		=initializer_range
										lowercase :							Tuple    		=layer_norm_eps
										lowercase :							Union[str, Any]    		=position_embedding_type
										lowercase :							List[Any]    		=use_cache
										lowercase :							Dict    		=emb_layer_norm_before
										lowercase :							Optional[Any]    		=token_dropout
										lowercase :							Union[str, Any]    		=is_folding_model
										if is_folding_model:
																if esmfold_config is None:
																						logger.info('''No esmfold_config supplied for folding model, using default values.'''   )
																						lowercase :							Any    		=EsmFoldConfig()
																elif isinstance(UpperCAmelCase__		,					UpperCAmelCase__   ):
																						lowercase :							Optional[int]    		=EsmFoldConfig(**UpperCAmelCase__   )
																lowercase :							Union[str, Any]    		=esmfold_config
																if vocab_list is None:
																						logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''   )
																						lowercase :							int    		=get_default_vocab_list()
																else:
																						lowercase :							Tuple    		=vocab_list
										else:
																lowercase :							Union[str, Any]    		=None
																lowercase :							Dict    		=None
										if self.esmfold_config is not None and getattr(self.esmfold_config		,					'''use_esm_attn_map'''		,					UpperCAmelCase__   ):
																raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=super().to_dict()
										if isinstance(self.esmfold_config		,					UpperCAmelCase__   ):
																lowercase :							Optional[Any]    		=self.esmfold_config.to_dict()
										return output





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    None
				lowerCamelCase_		       =    True
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    0

				lowerCamelCase_		       =    True
				lowerCamelCase_		       =    False

				lowerCamelCase_		       =    1_28
				lowerCamelCase_		       =    None







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										if self.trunk is None:
																lowercase :							str    		=TrunkConfig()
										elif isinstance(self.trunk		,					UpperCAmelCase__   ):
																lowercase :							int    		=TrunkConfig(**self.trunk   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							str    		=asdict(self   )
										lowercase :							Union[str, Any]    		=self.trunk.to_dict()
										return output





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    48
				lowerCamelCase_		       =    10_24
				lowerCamelCase_		       =    1_28
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    0
				lowerCamelCase_		       =    0
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    4
				lowerCamelCase_		       =    1_28
				lowerCamelCase_		       =    None







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										if self.structure_module is None:
																lowercase :							Any    		=StructureModuleConfig()
										elif isinstance(self.structure_module		,					UpperCAmelCase__   ):
																lowercase :							Union[str, Any]    		=StructureModuleConfig(**self.structure_module   )

										if self.max_recycles <= 0:
																raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.'''   )
										if self.sequence_state_dim % self.sequence_state_dim != 0:
																raise ValueError(
																    '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
																    F''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''   )
										if self.pairwise_state_dim % self.pairwise_state_dim != 0:
																raise ValueError(
																    '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
																    F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''   )

										lowercase :							str    		=self.sequence_state_dim // self.sequence_head_width
										lowercase :							int    		=self.pairwise_state_dim // self.pairwise_head_width

										if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
																raise ValueError(
																    '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
																    F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''   )
										if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
																raise ValueError(
																    '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
																    F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''   )
										if self.pairwise_state_dim % 2 != 0:
																raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''   )

										if self.dropout >= 0.4:
																raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.'''   )







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=asdict(self   )
										lowercase :							Any    		=self.structure_module.to_dict()
										return output





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    3_84
				lowerCamelCase_		       =    1_28
				lowerCamelCase_		       =    16
				lowerCamelCase_		       =    1_28
				lowerCamelCase_		       =    12
				lowerCamelCase_		       =    4
				lowerCamelCase_		       =    8
				lowerCamelCase_		       =    0.1
				lowerCamelCase_		       =    8
				lowerCamelCase_		       =    1
				lowerCamelCase_		       =    2
				lowerCamelCase_		       =    7
				lowerCamelCase_		       =    10
				lowerCamelCase_		       =    1E-8
				lowerCamelCase_		       =    1E5







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										return asdict(self   )






def 				_lowerCAmelCase			(   )				->  Optional[int]:
						return (
						    "<cls>",
						    "<pad>",
						    "<eos>",
						    "<unk>",
						    "L",
						    "A",
						    "G",
						    "V",
						    "S",
						    "E",
						    "R",
						    "T",
						    "I",
						    "D",
						    "P",
						    "K",
						    "Q",
						    "N",
						    "F",
						    "Y",
						    "M",
						    "H",
						    "W",
						    "C",
						    "X",
						    "B",
						    "U",
						    "Z",
						    "O",
						    ".",
						    "-",
						    "<null_1>",
						    "<mask>",
						)





