




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


_A     =			logging.get_logger(__name__)

_A     =			{}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   'llama'
		__snake_case									=   ['past_key_values']



		def __init__(     self,	UpperCamelCase__=3_2000,	UpperCamelCase__=4096,	UpperCamelCase__=1_1008,	UpperCamelCase__=32,	UpperCamelCase__=32,	UpperCamelCase__=None,	UpperCamelCase__="silu",	UpperCamelCase__=2048,	UpperCamelCase__=0.02,	UpperCamelCase__=1E-6,	UpperCamelCase__=True,	UpperCamelCase__=0,	UpperCamelCase__=1,	UpperCamelCase__=2,	UpperCamelCase__=1,	UpperCamelCase__=False,	UpperCamelCase__=None,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  vocab_size
								lowerCAmelCase_      			=  max_position_embeddings
								lowerCAmelCase_      			=  hidden_size
								lowerCAmelCase_      			=  intermediate_size
								lowerCAmelCase_      			=  num_hidden_layers
								lowerCAmelCase_      			=  num_attention_heads

								# for backward compatibility
								if num_key_value_heads is None:
														lowerCAmelCase_      			=  num_attention_heads

								lowerCAmelCase_      			=  num_key_value_heads
								lowerCAmelCase_      			=  hidden_act
								lowerCAmelCase_      			=  initializer_range
								lowerCAmelCase_      			=  rms_norm_eps
								lowerCAmelCase_      			=  pretraining_tp
								lowerCAmelCase_      			=  use_cache
								lowerCAmelCase_      			=  rope_scaling
								self._rope_scaling_validation()

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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								if self.rope_scaling is None:
														return

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






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


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

snake_case :		Any							      =  "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

snake_case :		str							      =  "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    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))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"





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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a=None						,   _a=True						,   _a=False		):
				if rouge_types is None:
					__magic_name__		:		str	   =     ["rouge1", "rouge2", "rougeL", "rougeLsum"]

				__magic_name__		:		List[str]	   =     rouge_scorer.RougeScorer(rouge_types=_a						,   use_stemmer=_a		)
				if use_aggregator:
					__magic_name__		:		Dict	   =     scoring.BootstrapAggregator()
				else:
					__magic_name__		:		str	   =     []

				for ref, pred in zip(_a						,   _a		):
					__magic_name__		:		Union[str, Any]	   =     scorer.score(_a						,   _a		)
					if use_aggregator:
						aggregator.add_scores(_a		)
					else:
						scores.append(_a		)

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

				return result
