
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE__							=    """\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
"""

SCREAMING_SNAKE_CASE__							=    """\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


SCREAMING_SNAKE_CASE__							=    """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
"""




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Tuple:
				__lowercase								= [rgram for rgrams in rgramslist for rgram in rgrams]
				__lowercase								= Counter(_A      )

				__lowercase								= Counter(_A      )
				__lowercase								= Counter()
				for sgram, scount in sgramcounter.items():
								__lowercase								= scount * numref

				__lowercase								= Counter(_A      )
				__lowercase								= Counter()
				for cgram, ccount in cgramcounter.items():
								__lowercase								= ccount * numref

				# KEEP
				__lowercase								= sgramcounter_rep & cgramcounter_rep
				__lowercase								= keepgramcounter_rep & rgramcounter
				__lowercase								= sgramcounter_rep & rgramcounter

				__lowercase								= 0
				__lowercase								= 0
				for keepgram in keepgramcountergood_rep:
								keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
								# Fix an alleged bug [2] in the keep score computation.
								# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
								keeptmpscorea += keepgramcountergood_rep[keepgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				#      a target exactly.
				__lowercase								= 1
				__lowercase								= 1
				if len(_A      ) > 0:
								__lowercase								= keeptmpscorea / len(_A      )
				if len(_A      ) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								__lowercase								= keeptmpscorea / sum(keepgramcounterall_rep.values()      )
				__lowercase								= 0
				if keepscore_precision > 0 or keepscore_recall > 0:
								__lowercase								= 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				__lowercase								= sgramcounter_rep - cgramcounter_rep
				__lowercase								= delgramcounter_rep - rgramcounter
				__lowercase								= sgramcounter_rep - rgramcounter
				__lowercase								= 0
				__lowercase								= 0
				for delgram in delgramcountergood_rep:
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				__lowercase								= 1
				if len(_A      ) > 0:
								__lowercase								= deltmpscorea / len(_A      )

				# ADDITION
				__lowercase								= set(_A      ) - set(_A      )
				__lowercase								= set(_A      ) & set(_A      )
				__lowercase								= set(_A      ) - set(_A      )

				__lowercase								= 0
				for addgram in addgramcountergood:
								addtmpscore += 1

				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				__lowercase								= 1
				__lowercase								= 1
				if len(_A      ) > 0:
								__lowercase								= addtmpscore / len(_A      )
				if len(_A      ) > 0:
								__lowercase								= addtmpscore / len(_A      )
				__lowercase								= 0
				if addscore_precision > 0 or addscore_recall > 0:
								__lowercase								= 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]   ,							SCREAMING_SNAKE_CASE	:				str      )					->						List[str]:
				__lowercase								= len(_A      )

				__lowercase								= ssent.split(' '      )
				__lowercase								= csent.split(' '      )
				__lowercase								= []
				__lowercase								= []
				__lowercase								= []
				__lowercase								= []
				__lowercase								= []
				__lowercase								= []

				__lowercase								= []
				__lowercase								= []
				__lowercase								= []
				__lowercase								= []
				for rsent in rsents:
								__lowercase								= rsent.split(' '      )
								__lowercase								= []
								__lowercase								= []
								__lowercase								= []
								ragramslist.append(_A      )
								for i in range(0   ,							len(_A      ) - 1      ):
												if i < len(_A      ) - 1:
																__lowercase								= ragrams[i] + ' ' + ragrams[i + 1]
																ragrams.append(_A      )
												if i < len(_A      ) - 2:
																__lowercase								= ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																ragrams.append(_A      )
												if i < len(_A      ) - 3:
																__lowercase								= ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
																ragrams.append(_A      )
								ragramslist.append(_A      )
								ragramslist.append(_A      )
								ragramslist.append(_A      )

				for i in range(0   ,							len(_A      ) - 1      ):
								if i < len(_A      ) - 1:
												__lowercase								= sagrams[i] + ' ' + sagrams[i + 1]
												sagrams.append(_A      )
								if i < len(_A      ) - 2:
												__lowercase								= sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
												sagrams.append(_A      )
								if i < len(_A      ) - 3:
												__lowercase								= sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
												sagrams.append(_A      )

				for i in range(0   ,							len(_A      ) - 1      ):
								if i < len(_A      ) - 1:
												__lowercase								= cagrams[i] + ' ' + cagrams[i + 1]
												cagrams.append(_A      )
								if i < len(_A      ) - 2:
												__lowercase								= cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
												cagrams.append(_A      )
								if i < len(_A      ) - 3:
												__lowercase								= cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
												cagrams.append(_A      )

				((__lowercase)		, (__lowercase)		, (__lowercase))								= SARIngram(_A   ,							_A   ,							_A   ,							_A      )
				((__lowercase)		, (__lowercase)		, (__lowercase))								= SARIngram(_A   ,							_A   ,							_A   ,							_A      )
				((__lowercase)		, (__lowercase)		, (__lowercase))								= SARIngram(_A   ,							_A   ,							_A   ,							_A      )
				((__lowercase)		, (__lowercase)		, (__lowercase))								= SARIngram(_A   ,							_A   ,							_A   ,							_A      )
				__lowercase								= sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
				__lowercase								= sum([delascore, delascore, delascore, delascore]      ) / 4
				__lowercase								= sum([addascore, addascore, addascore, addascore]      ) / 4
				__lowercase								= (avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[int]   ,							SCREAMING_SNAKE_CASE	:				bool = True   ,							SCREAMING_SNAKE_CASE	:				str = "13a"   ,							SCREAMING_SNAKE_CASE	:				bool = True      )					->						Union[str, Any]:


				if lowercase:
								__lowercase								= sentence.lower()

				if tokenizer in ["13a", "intl"]:
								if version.parse(sacrebleu.__version__      ).major >= 2:
												__lowercase								= sacrebleu.metrics.bleu._get_tokenizer(_A      )()(_A      )
								else:
												__lowercase								= sacrebleu.TOKENIZERS[tokenizer]()(_A      )
				elif tokenizer == "moses":
								__lowercase								= sacremoses.MosesTokenizer().tokenize(_A   ,							return_str=_A   ,							escape=_A      )
				elif tokenizer == "penn":
								__lowercase								= sacremoses.MosesTokenizer().penn_tokenize(_A   ,							return_str=_A      )
				else:
								__lowercase								= sentence

				if not return_str:
								__lowercase								= normalized_sent.split()

				return normalized_sent
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION    )
class      A__       (						datasets.Metric    ):





							def       a__			(			self  :       List[str]    )						->		str:



											"""simple docstring"""


											return datasets.MetricInfo(
											    description=_DESCRIPTION					,			citation=_CITATION					,			inputs_description=_KWARGS_DESCRIPTION					,			features=datasets.Features(
											        {
											            'sources': datasets.Value('string'					,			id='sequence'    ),
											            'predictions': datasets.Value('string'					,			id='sequence'    ),
											            'references': datasets.Sequence(datasets.Value('string'					,			id='sequence'    )					,			id='references'    ),
											        }    )					,			codebase_urls=[
											        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
											        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
											    ]					,			reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']					,			)





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Optional[int]    )						->		Union[str, Any]:



											"""simple docstring"""


											if not (len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) == len(__lowerCAmelCase    )):
															raise ValueError('Sources length must match predictions and references lengths.'    )
											__lowercase								= 0
											for src, pred, refs in zip(__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase    ):
															sari_score += SARIsent(normalize(__lowerCAmelCase    )					,			normalize(__lowerCAmelCase    )					,			[normalize(__lowerCAmelCase    ) for sent in refs]    )
											__lowercase								= sari_score / len(__lowerCAmelCase    )
											return {"sari": 1_00 * sari_score}







import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Optional[Any]:
				if isinstance(SCREAMING_SNAKE_CASE   ,							(list, tuple)      ):
								return honor_type(SCREAMING_SNAKE_CASE   ,							(move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for t in state)      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								return type(SCREAMING_SNAKE_CASE      )({k: move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for k, v in state.items()}      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							torch.Tensor      ):
								return state.to(SCREAMING_SNAKE_CASE      )
				return state
class      A__       (						torch.optim.Optimizer    ):





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



											"""simple docstring"""


											__lowercase								= optimizer
											__lowercase								= scaler
											__lowercase								= AcceleratorState()
											__lowercase								= GradientState()
											__lowercase								= device_placement
											__lowercase								= False
											__lowercase								= None

											# Handle device placement
											if device_placement:
															__lowercase								= self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
															else:
																			__lowercase								= move_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
															self.optimizer.load_state_dict(_UpperCAmelCase    )





							@property
							def       a__			(			self  :       Union[str, Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											return self.optimizer.state





							@state.setter
							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= state





							@property
							def       a__			(			self  :       str    )						->		int:



											"""simple docstring"""


											return self.optimizer.param_groups





							@param_groups.setter
							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= param_groups





							@property
							def       a__			(			self  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											return self.optimizer.defaults





							@defaults.setter
							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       int    )						->		Any:



											"""simple docstring"""


											__lowercase								= defaults





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



											"""simple docstring"""


											self.optimizer.add_param_group(_UpperCAmelCase    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
											self.optimizer.load_state_dict(_UpperCAmelCase    )





							def       a__			(			self  :       str    )						->		Any:



											"""simple docstring"""


											return self.optimizer.state_dict()





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[str]=None    )						->		Tuple:



											"""simple docstring"""


											if self.gradient_state.sync_gradients:
															__lowercase								= 'set_to_none' in inspect.signature(self.optimizer.zero_grad    ).parameters
															if accept_arg:
																			if set_to_none is None:
																							__lowercase								= False
																			self.optimizer.zero_grad(set_to_none=_UpperCAmelCase    )
															else:
																			if set_to_none is not None:
																							raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'    )
																			self.optimizer.zero_grad()





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       Optional[Any]=None    )						->		int:



											"""simple docstring"""


											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			__lowercase								= {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer					,			optimizer_args=_UpperCAmelCase    )
															elif self.scaler is not None:
																			__lowercase								= False
																			if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							__lowercase								= self.scaler.get_scale()
																							__lowercase								= True
																			self.scaler.step(self.optimizer					,			_UpperCAmelCase    )
																			self.scaler.update()
																			__lowercase								= self.scaler.get_scale()
																			if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							__lowercase								= scale_after < self._last_scale
																			__lowercase								= scale_after

															else:
																			self.optimizer.step(_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       int    )						->		Tuple:



											"""simple docstring"""


											for param_group in self.optimizer.param_groups:
															__lowercase								= [parameters_map.get(_UpperCAmelCase					,			_UpperCAmelCase    ) for p in param_group['params']]





							@property
							def       a__			(			self  :       Optional[Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											warnings.warn(
											    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
											    '`optimizer.step_was_skipped` instead.'					,			_UpperCAmelCase					,			)
											return self._is_overflow





							@property
							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											return self._is_overflow





							def __getstate__(			self  :       Optional[int]    )						->		str:



											"""simple docstring"""


											return self.__dict__.copy()





							def __setstate__(			self  :       List[Any]					,			_UpperCAmelCase  :       Optional[int]    )						->		Any:



											"""simple docstring"""


											self.__dict__.update(_UpperCAmelCase    )





