





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


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class        snake_case_    (		__lowercase						):
  A_      							=   'gpt_neox'






  def __init__(							self			:					Dict       ,  _snake_case			:					Optional[int]=50432       ,  _snake_case			:					Dict=6144       ,  _snake_case			:					Dict=44       ,  _snake_case			:					int=64       ,  _snake_case			:					Dict=24576       ,  _snake_case			:					Optional[int]="gelu"       ,  _snake_case			:					Optional[Any]=0.25       ,  _snake_case			:					Optional[Any]=10000       ,  _snake_case			:					str=0.0       ,  _snake_case			:					List[str]=0.0       ,  _snake_case			:					List[Any]=0.1       ,  _snake_case			:					Dict=2048       ,  _snake_case			:					List[str]=0.02       ,  _snake_case			:					str=1E-5       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Tuple=0       ,  _snake_case			:					Dict=2       ,  _snake_case			:					List[str]=False       ,  _snake_case			:					List[str]=True       ,  _snake_case			:					List[Any]=None       ,  **_snake_case			:					Union[str, Any]       ,  )->Tuple:






   '''simple docstring'''

   super().__init__(bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  **_snake_case      )
   __lowerCAmelCase					:	Optional[int]      =       vocab_size
   __lowerCAmelCase					:	str      =       max_position_embeddings
   __lowerCAmelCase					:	int      =       hidden_size
   __lowerCAmelCase					:	Union[str, Any]      =       num_hidden_layers
   __lowerCAmelCase					:	List[Any]      =       num_attention_heads
   __lowerCAmelCase					:	str      =       intermediate_size
   __lowerCAmelCase					:	int      =       hidden_act
   __lowerCAmelCase					:	str      =       rotary_pct
   __lowerCAmelCase					:	str      =       rotary_emb_base
   __lowerCAmelCase					:	int      =       attention_dropout
   __lowerCAmelCase					:	Any      =       hidden_dropout
   __lowerCAmelCase					:	List[Any]      =       classifier_dropout
   __lowerCAmelCase					:	Optional[int]      =       initializer_range
   __lowerCAmelCase					:	str      =       layer_norm_eps
   __lowerCAmelCase					:	List[str]      =       use_cache
   __lowerCAmelCase					:	List[str]      =       tie_word_embeddings
   __lowerCAmelCase					:	int      =       use_parallel_residual
   __lowerCAmelCase					:	Any      =       rope_scaling
   self._rope_scaling_validation()

   if self.hidden_size % self.num_attention_heads != 0:
    raise ValueError(
        """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""      )






  def 				UpperCAmelCase__						(							self			:					Any      )->Tuple:






   '''simple docstring'''

   if self.rope_scaling is None:
    return

   if not isinstance(self.rope_scaling       ,  _snake_case      ) or len(self.rope_scaling      ) != 2:
    raise ValueError(
        """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
        F'''got {self.rope_scaling}'''      )
   __lowerCAmelCase					:	List[Any]      =       self.rope_scaling.get("""type"""       ,  _snake_case      )
   __lowerCAmelCase					:	Any      =       self.rope_scaling.get("""factor"""       ,  _snake_case      )
   if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
    raise ValueError(
        F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''      )
   if rope_scaling_factor is None or not isinstance(_snake_case       ,  _snake_case      ) or rope_scaling_factor <= 1.0:
    raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )










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

import datasets


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

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

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






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






  def 				UpperCAmelCase__						(							self			:					Tuple      )->str:






   '''simple docstring'''

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






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






   '''simple docstring'''

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

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

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