




"""simple docstring"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__ :    Any 					=							logging.get_logger(__name__)

UpperCamelCase__ :    Union[str, Any] 					=							{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}
class     A__      (      lowercase_     ):
	A_							:				int						     =      """m2m_100"""
	A_							:				str						     =      ["""past_key_values"""]
	A_							:				Optional[int]						     =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__(   self  :	Any      ,    lowercase  :	Union[str, Any]=128_112      ,    lowercase  :	Optional[Any]=1_024      ,    lowercase  :	Any=12      ,    lowercase  :	int=4_096      ,    lowercase  :	Optional[Any]=16      ,    lowercase  :	Optional[int]=12      ,    lowercase  :	List[str]=4_096      ,    lowercase  :	List[str]=16      ,    lowercase  :	Optional[Any]=0.05      ,    lowercase  :	Any=0.05      ,    lowercase  :	Union[str, Any]=True      ,    lowercase  :	Union[str, Any]=True      ,    lowercase  :	Tuple="relu"      ,    lowercase  :	Optional[Any]=1_024      ,    lowercase  :	Optional[Any]=0.1      ,    lowercase  :	Optional[Any]=0.1      ,    lowercase  :	Optional[int]=0.0      ,    lowercase  :	Optional[Any]=0.02      ,    lowercase  :	Optional[Any]=2      ,    lowercase  :	List[str]=True      ,    lowercase  :	int=1      ,    lowercase  :	List[Any]=0      ,    lowercase  :	Union[str, Any]=2      ,    **lowercase  :	List[str]      ,    )					-> str:
						__snake_case          =	vocab_size
						__snake_case          =	max_position_embeddings
						__snake_case          =	d_model
						__snake_case          =	encoder_ffn_dim
						__snake_case          =	encoder_layers
						__snake_case          =	encoder_attention_heads
						__snake_case          =	decoder_ffn_dim
						__snake_case          =	decoder_layers
						__snake_case          =	decoder_attention_heads
						__snake_case          =	dropout
						__snake_case          =	attention_dropout
						__snake_case          =	activation_dropout
						__snake_case          =	activation_function
						__snake_case          =	init_std
						__snake_case          =	encoder_layerdrop
						__snake_case          =	decoder_layerdrop
						__snake_case          =	use_cache
						__snake_case          =	encoder_layers
						__snake_case          =	scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=lowercase      ,    bos_token_id=lowercase      ,    eos_token_id=lowercase      ,    is_encoder_decoder=lowercase      ,    decoder_start_token_id=lowercase      ,    **lowercase      ,    )

class     A__      (      lowercase_     ):




	@property
	def 							a_		(   self  :	Optional[int]	)					-> Mapping[str, Mapping[int, str]]:
						__snake_case          =	OrderedDict(
						    [
						        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
						        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
						    ]	)

						if self.use_past:
											__snake_case          =	{0: """batch"""}
											__snake_case          =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
						else:
											__snake_case          =	{0: """batch""", 1: """decoder_sequence"""}
											__snake_case          =	{0: """batch""", 1: """decoder_sequence"""}

						if self.use_past:
											self.fill_with_past_key_values_(lowercase      ,    direction="""inputs"""	)
						return common_inputs




	def 							a_		(   self  :	str      ,    lowercase  :	PreTrainedTokenizer      ,    lowercase  :	int = -1      ,    lowercase  :	int = -1      ,    lowercase  :	bool = False      ,    lowercase  :	Optional[TensorType] = None      ,    )					-> Mapping[str, Any]:
						# Copied from OnnxConfig.generate_dummy_inputs
						# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__snake_case          =	compute_effective_axis_dimension(
						    lowercase      ,    fixed_dimension=OnnxConfig.default_fixed_batch      ,    num_token_to_add=0	)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__snake_case          =	tokenizer.num_special_tokens_to_add(lowercase	)
						__snake_case          =	compute_effective_axis_dimension(
						    lowercase      ,    fixed_dimension=OnnxConfig.default_fixed_sequence      ,    num_token_to_add=lowercase	)

						# Generate dummy inputs according to compute batch and sequence
						__snake_case          =	[""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
						__snake_case          =	dict(tokenizer(lowercase      ,    return_tensors=lowercase	)	)
						return common_inputs




	def 							a_		(   self  :	Optional[int]      ,    lowercase  :	PreTrainedTokenizer      ,    lowercase  :	int = -1      ,    lowercase  :	int = -1      ,    lowercase  :	bool = False      ,    lowercase  :	Optional[TensorType] = None      ,    )					-> Mapping[str, Any]:
						__snake_case          =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase	)

						# Generate decoder inputs
						__snake_case          =	seq_length if not self.use_past else 1
						__snake_case          =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase	)
						__snake_case          =	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__snake_case          =	dict(**lowercase      ,    **lowercase	)

						if self.use_past:
											if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
											else:
																import torch
											__snake_case      ,      __snake_case          =	common_inputs["""input_ids"""].shape
											__snake_case          =	common_inputs["""decoder_input_ids"""].shape[1]
											__snake_case      ,      __snake_case          =	self.num_attention_heads
											__snake_case          =	(
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											__snake_case          =	decoder_seq_length + 3
											__snake_case          =	(
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											__snake_case          =	torch.cat(
											    [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase      ,    lowercase	)]      ,    dim=1	)

											__snake_case          =	[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__snake_case      ,      __snake_case          =	self.num_layers
											__snake_case          =	min(lowercase      ,    lowercase	)
											__snake_case          =	max(lowercase      ,    lowercase	) - min_num_layers
											__snake_case          =	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

											for _ in range(lowercase	):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(lowercase	),
																        torch.zeros(lowercase	),
																        torch.zeros(lowercase	),
																        torch.zeros(lowercase	),
																    )	)
											# TODO: test this.
											__snake_case          =	encoder_shape if remaining_side_name == """encoder""" else decoder_shape
											for _ in range(lowercase      ,    lowercase	):
																common_inputs["past_key_values"].append((torch.zeros(lowercase	), torch.zeros(lowercase	))	)
						return common_inputs
	A_							:				Dict						     =      _generate_dummy_inputs_for_default_and_seqaseq_lm








"""simple docstring"""

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase__ :    int 					=							"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

UpperCamelCase__ :    Union[str, Any] 					=							"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"


UpperCamelCase__ :    List[Any] 					=							"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"




def       snake_case__	(       lowerCAmelCase   :		Optional[Any]				,					lowerCAmelCase   :		int				,					lowerCAmelCase   :		int				,					lowerCAmelCase   :		List[str]		):
					__snake_case          =	[rgram for rgrams in rgramslist for rgram in rgrams]
					__snake_case          =	Counter(lowerCAmelCase		)

					__snake_case          =	Counter(lowerCAmelCase		)
					__snake_case          =	Counter()
					for sgram, scount in sgramcounter.items():
										__snake_case          =	scount * numref

					__snake_case          =	Counter(lowerCAmelCase		)
					__snake_case          =	Counter()
					for cgram, ccount in cgramcounter.items():
										__snake_case          =	ccount * numref

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

					__snake_case          =	0
					__snake_case          =	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.
					__snake_case          =	1
					__snake_case          =	1
					if len(lowerCAmelCase		) > 0:
										__snake_case          =	keeptmpscorea / len(lowerCAmelCase		)
					if len(lowerCAmelCase		) > 0:
										# Fix an alleged bug [2] in the keep score computation.
										# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
										__snake_case          =	keeptmpscorea / sum(keepgramcounterall_rep.values()		)
					__snake_case          =	0
					if keepscore_precision > 0 or keepscore_recall > 0:
										__snake_case          =	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

					# DELETION
					__snake_case          =	sgramcounter_rep - cgramcounter_rep
					__snake_case          =	delgramcounter_rep - rgramcounter
					__snake_case          =	sgramcounter_rep - rgramcounter
					__snake_case          =	0
					__snake_case          =	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.
					__snake_case          =	1
					if len(lowerCAmelCase		) > 0:
										__snake_case          =	deltmpscorea / len(lowerCAmelCase		)

					# ADDITION
					__snake_case          =	set(lowerCAmelCase		) - set(lowerCAmelCase		)
					__snake_case          =	set(lowerCAmelCase		) & set(lowerCAmelCase		)
					__snake_case          =	set(lowerCAmelCase		) - set(lowerCAmelCase		)

					__snake_case          =	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.
					__snake_case          =	1
					__snake_case          =	1
					if len(lowerCAmelCase		) > 0:
										__snake_case          =	addtmpscore / len(lowerCAmelCase		)
					if len(lowerCAmelCase		) > 0:
										__snake_case          =	addtmpscore / len(lowerCAmelCase		)
					__snake_case          =	0
					if addscore_precision > 0 or addscore_recall > 0:
										__snake_case          =	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

					return (keepscore, delscore_precision, addscore)




def       snake_case__	(       lowerCAmelCase   :		int				,					lowerCAmelCase   :		str				,					lowerCAmelCase   :		Union[str, Any]		):
					__snake_case          =	len(lowerCAmelCase		)

					__snake_case          =	ssent.split(""" """		)
					__snake_case          =	csent.split(""" """		)
					__snake_case          =	[]
					__snake_case          =	[]
					__snake_case          =	[]
					__snake_case          =	[]
					__snake_case          =	[]
					__snake_case          =	[]

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

					for i in range(0				,					len(lowerCAmelCase		) - 1		):
										if i < len(lowerCAmelCase		) - 1:
															__snake_case          =	sagrams[i] + """ """ + sagrams[i + 1]
															sagrams.append(lowerCAmelCase		)
										if i < len(lowerCAmelCase		) - 2:
															__snake_case          =	sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
															sagrams.append(lowerCAmelCase		)
										if i < len(lowerCAmelCase		) - 3:
															__snake_case          =	sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
															sagrams.append(lowerCAmelCase		)

					for i in range(0				,					len(lowerCAmelCase		) - 1		):
										if i < len(lowerCAmelCase		) - 1:
															__snake_case          =	cagrams[i] + """ """ + cagrams[i + 1]
															cagrams.append(lowerCAmelCase		)
										if i < len(lowerCAmelCase		) - 2:
															__snake_case          =	cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
															cagrams.append(lowerCAmelCase		)
										if i < len(lowerCAmelCase		) - 3:
															__snake_case          =	cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
															cagrams.append(lowerCAmelCase		)

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




def       snake_case__	(       lowerCAmelCase   :		List[str]				,					lowerCAmelCase   :		bool = True				,					lowerCAmelCase   :		str = "13a"				,					lowerCAmelCase   :		bool = True		):
					# Normalization is requried for the ASSET dataset (one of the primary
					# datasets in sentence simplification) to allow using space
					# to split the sentence. Even though Wiki-Auto and TURK datasets,
					# do not require normalization, we do it for consistency.
					# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
					# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

					if lowercase:
										__snake_case          =	sentence.lower()

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

					if not return_str:
										__snake_case          =	normalized_sent.split()

					return normalized_sent

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




	def 							a_		(   self  :	Tuple	)					-> str:
						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  :	Dict      ,    lowercase  :	Optional[int]      ,    lowercase  :	Tuple      ,    lowercase  :	Union[str, Any]	)					-> Tuple:
						if not (len(lowercase	) == len(lowercase	) == len(lowercase	)):
											raise ValueError("""Sources length must match predictions and references lengths."""	)
						__snake_case          =	0
						for src, pred, refs in zip(lowercase      ,    lowercase      ,    lowercase	):
											sari_score += SARIsent(normalize(lowercase	)      ,    normalize(lowercase	)      ,    [normalize(lowercase	) for sent in refs]	)
						__snake_case          =	sari_score / len(lowercase	)
						return {"sari": 100 * sari_score}


