






import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__magic_name__:					Optional[int]							=     logging.get_logger(__name__)


__magic_name__:					Union[str, Any]							=     r"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"





class   snake_case__ (							_lowerCAmelCase				):
	@add_start_docstrings(lowerCAmelCase__			)
	def __call__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				bool:
							raise NotImplementedError("""StoppingCriteria needs to be subclassed"""			)





class   snake_case__ (							_lowerCAmelCase				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[str]:
							__magic_name__      :       int       						= max_length
							__magic_name__      :       Tuple       						= max_position_embeddings







	@add_start_docstrings(lowerCAmelCase__			)
	def __call__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				bool:
							__magic_name__      :       Dict       						= input_ids.shape[-1]
							__magic_name__      :       Dict       						= cur_len >= self.max_length
							if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
													logger.warning_once(
													    """This is a friendly reminder - the current text generation call will exceed the model's predefined """
													    F'maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '
													    """exceptions, performance degradation, or nothing at all."""			)
							return is_done





class   snake_case__ (							_lowerCAmelCase				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Optional[int]:
							warnings.warn(
							    """The class `MaxNewTokensCriteria` is deprecated. """
							    F'Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '
							    """with `max_length = start_length + max_new_tokens` instead."""      ,       lowerCAmelCase__      ,       )
							__magic_name__      :       Optional[Any]       						= start_length
							__magic_name__      :       List[str]       						= max_new_tokens
							__magic_name__      :       Dict       						= start_length + max_new_tokens







	@add_start_docstrings(lowerCAmelCase__			)
	def __call__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				bool:
							return input_ids.shape[-1] >= self.max_length





class   snake_case__ (							_lowerCAmelCase				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				str:
							__magic_name__      :       Optional[Any]       						= max_time
							__magic_name__      :       Tuple       						= time.time() if initial_timestamp is None else initial_timestamp







	@add_start_docstrings(lowerCAmelCase__			)
	def __call__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				bool:
							return time.time() - self.initial_timestamp > self.max_time





class   snake_case__ (							_lowerCAmelCase				):
	@add_start_docstrings(lowerCAmelCase__			)
	def __call__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				bool:
							return any(criteria(lowerCAmelCase__      ,       lowerCAmelCase__			) for criteria in self			)







	@property
	def       __magic_name__    (    self			)							->				Optional[int]:
							for stopping_criterium in self:
													if isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			):
																			return stopping_criterium.max_length
													elif isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			):
																			return stopping_criterium.max_length
							return None



def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= stopping_criteria.max_length
						__magic_name__      :       str       						= deepcopy(_A			)
						if stopping_max_length is not None and stopping_max_length != max_length:
												warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter""",   _A			)
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=_A			)			)
						return new_stopping_criteria








from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__magic_name__:					Optional[Any]							=     "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

__magic_name__:					List[Any]							=     "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

__magic_name__:					Union[str, Any]							=     "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION				)
class 	snake_case__ (							datasets.Metric				):
	def       __magic_name__    (    self			)							->				MetricInfo:
							return datasets.MetricInfo(
							    description=_DESCRIPTION      ,       citation=_CITATION      ,       inputs_description=_KWARGS_DESCRIPTION      ,       features=datasets.Features(
							        {
							            """predictions""": datasets.Sequence(datasets.Value("""string"""      ,       id="""token"""			)      ,       id="""sequence"""			),
							            """references""": datasets.Sequence(
							                datasets.Sequence(datasets.Value("""string"""      ,       id="""token"""			)      ,       id="""sequence"""			)      ,       id="""references"""			),
							        }			)      ,       )







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = 4      ,       )							->				Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=lowerCAmelCase__      ,       hypotheses=lowerCAmelCase__      ,       min_len=lowerCAmelCase__      ,       max_len=lowerCAmelCase__			)
							}
