





'''simple docstring'''



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_SCREAMING_SNAKE_CASE   =      '''\
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W04-1013",
    pages = "74--81",
}
'''

_SCREAMING_SNAKE_CASE   =      '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''

_SCREAMING_SNAKE_CASE   =      '''
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
        `"rougeL"`: Longest common subsequence based scoring.
        `"rougeLSum"`: rougeLsum splits text using `"\n"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric(\'rouge\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
    >>> print(results["rouge1"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results["rouge1"].mid.fmeasure)
    1.0
'''






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







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				return datasets.MetricInfo(
				    description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
				        {
				            '''predictions''': datasets.Value('''string'''       ,id='''sequence'''	),
				            '''references''': datasets.Value('''string'''       ,id='''sequence'''	),
				        }	)       ,codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']       ,reference_urls=[
				        '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
				        '''https://github.com/google-research/google-research/tree/master/rouge''',
				    ]       ,)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase=False	)							->							Optional[int]:







				'''simple docstring'''





				if rouge_types is None:
							__lowercase			  =							['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

				__lowercase			  =							rouge_scorer.RougeScorer(rouge_types=_lowerCamelCase       ,use_stemmer=_lowerCamelCase	)
				if use_aggregator:
							__lowercase			  =							scoring.BootstrapAggregator()
				else:
							__lowercase			  =							[]

				for ref, pred in zip(_lowerCamelCase       ,_lowerCamelCase	):
							__lowercase			  =							scorer.score(_lowerCamelCase       ,_lowerCamelCase	)
							if use_aggregator:
										aggregator.add_scores(_lowerCamelCase	)
							else:
										scores.append(_lowerCamelCase	)

				if use_aggregator:
							__lowercase			  =							aggregator.aggregate()
				else:
							__lowercase			  =							{}
							for key in scores[0]:
										__lowercase			  =							[score[key] for score in scores]

				return result












'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Dict     =    "open-llama"


	def __init__(self       ,_lowerCamelCase=100000       ,_lowerCamelCase=4096       ,_lowerCamelCase=11008       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase="silu"       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-6       ,_lowerCamelCase=True       ,_lowerCamelCase=0       ,_lowerCamelCase=1       ,_lowerCamelCase=2       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							intermediate_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							hidden_act
				__lowercase			  =							initializer_range
				__lowercase			  =							rms_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							kwargs.pop(
				    '''use_memorry_efficient_attention'''       ,_lowerCamelCase	)
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_dropout_prob
				__lowercase			  =							use_stable_embedding
				__lowercase			  =							shared_input_output_embedding
				__lowercase			  =							rope_scaling
				self._rope_scaling_validation()

				super().__init__(
				    pad_token_id=_lowerCamelCase       ,bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,tie_word_embeddings=_lowerCamelCase       ,**_lowerCamelCase       ,)


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				if self.rope_scaling is None:
							return

				if not isinstance(self.rope_scaling       ,_lowerCamelCase	) 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}"	)
				__lowercase			  =							self.rope_scaling.get('''type'''       ,_lowerCamelCase	)
				__lowercase			  =							self.rope_scaling.get('''factor'''       ,_lowerCamelCase	)
				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(_lowerCamelCase       ,_lowerCamelCase	) or rope_scaling_factor <= 1.0:
							raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"	)





