





import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


lowercase_		:				Tuple				=						get_logger(__name__)


lowercase_		:				Union[str, Any]				=						r'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''


class 			lowercase				:



						"""simple docstring"""

						@add_start_docstrings(lowerCamelCase_					)
						def __call__(    self			:		Any ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray					):





										'''simple docstring'''







										raise NotImplementedError(
										    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)



class 			lowercase				:



						"""simple docstring"""

						@add_start_docstrings(lowerCamelCase_					)
						def __call__(    self			:		Dict ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray					):





										'''simple docstring'''







										raise NotImplementedError(
										    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)



class 			lowercase				(							a_     ):



						"""simple docstring"""

						@add_start_docstrings(lowerCamelCase_					)
						def __call__(    self			:		Tuple ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int ,				**lowerCamelCase_			:		Dict					):





										'''simple docstring'''







										for processor in self:
														_snake_case :			List[Any]  						=   inspect.signature(processor.__call__					).parameters
														if len(lowerCamelCase_					) > 3:
																		if not all(arg in kwargs for arg in list(function_args.keys()					)[2:]					):
																						raise ValueError(
																						    f'''Make sure that all the required parameters: {list(function_args.keys()					)} for '''
																						    f'''{processor.__class__} are passed to the logits processor.'''					)
																		_snake_case :			Dict  						=   processor(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_					)
														else:
																		_snake_case :			List[Any]  						=   processor(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_					)
										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		int ,				lowerCamelCase_			:		float					):





										'''simple docstring'''







										if not isinstance(lowerCamelCase_ ,				lowerCamelCase_					) or not (temperature > 0):
														raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}'''					)

										_snake_case :			Tuple  						=   temperature







						def __call__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			int  						=   scores / self.temperature
										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		float = -float('Inf'					) ,				lowerCamelCase_			:		int = 1					):





										'''simple docstring'''







										if not isinstance(lowerCamelCase_ ,				lowerCamelCase_					) or (top_p < 0 or top_p > 1.0):
														raise ValueError(f'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''					)
										if not isinstance(lowerCamelCase_ ,				lowerCamelCase_					) or (min_tokens_to_keep < 1):
														raise ValueError(f'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''					)

										_snake_case :			str  						=   top_p
										_snake_case :			List[Any]  						=   filter_value
										_snake_case :			Optional[int]  						=   min_tokens_to_keep







						def __call__(    self			:		Dict ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Dict  						=   lax.top_k(lowerCamelCase_ ,				scores.shape[-1]					)

										_snake_case :			List[Any]  						=   jnp.full_like(lowerCamelCase_ ,				self.filter_value					)
										_snake_case :			str  						=   jax.nn.softmax(lowerCamelCase_ ,				axis=-1					).cumsum(axis=-1					)
										_snake_case :			List[str]  						=   cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										_snake_case :			Optional[int]  						=   jnp.roll(lowerCamelCase_ ,				1					)
										score_mask |= score_mask.at[:, 0].set(lowerCamelCase_					)

										# min tokens to keep
										_snake_case :			int  						=   score_mask.at[:, : self.min_tokens_to_keep].set(lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   jnp.where(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   jax.lax.sort_key_val(lowerCamelCase_ ,				lowerCamelCase_					)[-1]

										return next_scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float = -float('Inf'					) ,				lowerCamelCase_			:		int = 1					):





										'''simple docstring'''







										if not isinstance(lowerCamelCase_ ,				lowerCamelCase_					) or top_k <= 0:
														raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}'''					)

										_snake_case :			List[Any]  						=   max(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   filter_value







						def __call__(    self			:		str ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			str  						=   scores.shape
										_snake_case :			Any  						=   jnp.full(batch_size * vocab_size ,				self.filter_value					)

										_snake_case :			Dict  						=   min(self.top_k ,				scores.shape[-1]					)  # Safety check
										_snake_case :			Optional[Any]  						=   lax.top_k(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   jnp.broadcast_to((jnp.arange(lowerCamelCase_					) * vocab_size)[:, None] ,				(batch_size, topk)					).flatten()
										_snake_case :			List[str]  						=   topk_scores.flatten()
										_snake_case :			Any  						=   topk_indices.flatten() + shift

										_snake_case :			Tuple  						=   next_scores_flat.at[topk_indices_flat].set(lowerCamelCase_					)
										_snake_case :			List[Any]  						=   next_scores_flat.reshape(lowerCamelCase_ ,				lowerCamelCase_					)
										return next_scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   bos_token_id







						def __call__(    self			:		str ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   jnp.full(scores.shape ,				-float('inf'					)					)

										_snake_case :			Optional[int]  						=   1 - jnp.bool_(cur_len - 1					)

										_snake_case :			Any  						=   jnp.where(lowerCamelCase_ ,				new_scores.at[:, self.bos_token_id].set(0					) ,				lowerCamelCase_					)

										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   max_length
										_snake_case :			Union[str, Any]  						=   eos_token_id







						def __call__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Any  						=   jnp.full(scores.shape ,				-float('inf'					)					)

										_snake_case :			str  						=   1 - jnp.bool_(cur_len - self.max_length + 1					)

										_snake_case :			str  						=   jnp.where(lowerCamelCase_ ,				new_scores.at[:, self.eos_token_id].set(0					) ,				lowerCamelCase_					)

										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		str ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										if not isinstance(lowerCamelCase_ ,				lowerCamelCase_					) or min_length < 0:
														raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}'''					)

										if not isinstance(lowerCamelCase_ ,				lowerCamelCase_					) or eos_token_id < 0:
														raise ValueError(f'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''					)

										_snake_case :			str  						=   min_length
										_snake_case :			Optional[Any]  						=   eos_token_id







						def __call__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   1 - jnp.clip(cur_len - self.min_length ,				0 ,				1					)

										_snake_case :			Dict  						=   jnp.where(lowerCamelCase_ ,				scores.at[:, self.eos_token_id].set(-float('inf'					)					) ,				lowerCamelCase_					)

										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[Any] ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   list(lowerCamelCase_					)
										_snake_case :			int  						=   begin_index







						def __call__(    self			:		Tuple ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Any  						=   1 - jnp.bool_(cur_len - self.begin_index					)

										_snake_case :			Union[str, Any]  						=   jnp.where(lowerCamelCase_ ,				scores.at[:, self.begin_suppress_tokens].set(-float('inf'					)					) ,				lowerCamelCase_					)

										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[Any] ,				lowerCamelCase_			:		list					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   list(lowerCamelCase_					)







						def __call__(    self			:		int ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   scores.at[..., self.suppress_tokens].set(-float('inf'					)					)

										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		int ,				lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   dict(lowerCamelCase_					)
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										_snake_case :			Dict  						=   jnp.ones((max(force_token_map.keys()					) + 1) ,				dtype=jnp.intaa					) * -1
										for index, token in force_token_map.items():
														if token is not None:
																		_snake_case :			Any  						=   force_token_array.at[index].set(lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   jnp.intaa(lowerCamelCase_					)







						def __call__(    self			:		int ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		jnp.ndarray ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										def _force_token(lowerCamelCase_			:		Any					):
														_snake_case :			int  						=   scores.shape[0]
														_snake_case :			List[str]  						=   self.force_token_array[generation_idx]

														_snake_case :			List[Any]  						=   jnp.ones_like(lowerCamelCase_ ,				dtype=scores.dtype					) * -float('inf'					)
														_snake_case :			int  						=   jnp.zeros((batch_size, 1) ,				dtype=scores.dtype					)
														_snake_case :			List[Any]  						=   lax.dynamic_update_slice(lowerCamelCase_ ,				lowerCamelCase_ ,				(0, current_token)					)
														return new_scores

										_snake_case :			Optional[Any]  						=   lax.cond(
										    cur_len >= self.force_token_array.shape[0] ,				lambda: scores ,				lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0 ,				lambda: _force_token(lowerCamelCase_					) ,				lambda: scores ,				) ,				)
										return scores



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Any ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Dict  						=   generate_config.eos_token_id
										_snake_case :			Any  						=   generate_config.no_timestamps_token_id
										_snake_case :			Optional[Any]  						=   generate_config.no_timestamps_token_id + 1

										_snake_case :			Dict  						=   decoder_input_length + 1

										if generate_config.is_multilingual:
														# room for language token and task token
														self.begin_index += 2
										if hasattr(lowerCamelCase_ ,				'max_initial_timestamp_index'					):
														_snake_case :			str  						=   generate_config.max_initial_timestamp_index
										else:
														_snake_case :			Union[str, Any]  						=   model_config.vocab_size
										if self.max_initial_timestamp_index is None:
														_snake_case :			List[Any]  						=   model_config.vocab_size







						def __call__(    self			:		List[str] ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Dict					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   scores.at[:, self.no_timestamps_token_id].set(-float('inf'					)					)

										def handle_pairs(lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		List[Any]					):
														_snake_case :			Optional[int]  						=   jnp.where((cur_len - self.begin_index) >= 1 ,				lowerCamelCase_ ,				lowerCamelCase_					)
														_snake_case :			Tuple  						=   jnp.where(
														    input_ids_k[cur_len - 1] >= self.timestamp_begin ,				True and last_was_timestamp ,				lowerCamelCase_ ,				)

														_snake_case :			Any  						=   jnp.where((cur_len - self.begin_index) < 2 ,				lowerCamelCase_ ,				lowerCamelCase_					)
														_snake_case :			Tuple  						=   jnp.where(
														    input_ids_k[cur_len - 2] >= self.timestamp_begin ,				lowerCamelCase_ ,				lowerCamelCase_ ,				)

														return jnp.where(
														    lowerCamelCase_ ,				jnp.where(
														        penultimate_was_timestamp > 0 ,				scores_k.at[self.timestamp_begin :].set(-float('inf'					)					) ,				scores_k.at[: self.eos_token_id].set(-float('inf'					)					) ,				) ,				lowerCamelCase_ ,				)

										_snake_case :			Optional[Any]  						=   jax.vmap(lowerCamelCase_					)(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   jnp.where(cur_len == self.begin_index ,				lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   jnp.where(
										    self.max_initial_timestamp_index is not None ,				True and apply_max_initial_timestamp ,				lowerCamelCase_ ,				)

										_snake_case :			Any  						=   self.timestamp_begin + self.max_initial_timestamp_index

										_snake_case :			str  						=   jnp.where(
										    lowerCamelCase_ ,				scores.at[:, last_allowed + 1 :].set(-float('inf'					)					) ,				lowerCamelCase_ ,				)

										# if sum of probability over timestamps is above any other token, sample timestamp
										_snake_case :			List[Any]  						=   jax.nn.log_softmax(lowerCamelCase_ ,				axis=-1					)

										def handle_cumulative_probs(lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		List[Any]					):
														_snake_case :			List[str]  						=   jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] ,				axis=-1					)
														_snake_case :			Tuple  						=   jnp.max(logprobs_k[: self.timestamp_begin]					)
														return jnp.where(
														    timestamp_logprob > max_text_token_logprob ,				scores_k.at[: self.timestamp_begin].set(-float('inf'					)					) ,				lowerCamelCase_ ,				)

										_snake_case :			Optional[int]  						=   jax.vmap(lowerCamelCase_					)(lowerCamelCase_ ,				lowerCamelCase_					)

										return scores













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

import datasets


lowercase_		:				List[str]				=						'''\
@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",
}
'''

lowercase_		:				Optional[int]				=						'''\
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.
'''

lowercase_		:				Any				=						'''
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 			lowercase				(							datasets.Metric     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''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 ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







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

										_snake_case :			Optional[int]  						=   TER(
										    normalized=lowerCamelCase_ ,				no_punct=lowerCamelCase_ ,				asian_support=lowerCamelCase_ ,				case_sensitive=lowerCamelCase_ ,				)
										_snake_case :			Optional[Any]  						=   sb_ter.corpus_score(lowerCamelCase_ ,				lowerCamelCase_					)

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






