





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase__   : Dict					  = """
@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
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

UpperCamelCase__   : Optional[int]					  = """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


UpperCamelCase__   : List[str]					  = """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
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
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""




def    A_(			A   ):

	def remove_articles(A   ):
		UpperCAmelCase_								=     re.compile(R"""\b(a|an|the)\b"""				,      re.UNICODE   )
		return re.sub(A				,      """ """				,      A   )

	def white_space_fix(A   ):
		return " ".join(text.split()   )

	def remove_punc(A   ):
		UpperCAmelCase_								=     set(string.punctuation   )
		return "".join(ch for ch in text if ch not in exclude   )

	def lower(A   ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(A   )   )   )   )




def    A_(			A				,      A   ):
	return int(normalize_answer(A   ) == normalize_answer(A   )   )




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     [any(compute_exact(A				,      A   ) for ref in refs   ) for pred, refs in zip(A				,      A   )]
	return (sum(A   ) / len(A   )) * 100




def    A_(			A				,      A				,      A				,      A   ):
	UpperCAmelCase_								=     [rgram for rgrams in rgramslist for rgram in rgrams]
	UpperCAmelCase_								=     Counter(A   )

	UpperCAmelCase_								=     Counter(A   )
	UpperCAmelCase_								=     Counter()
	for sgram, scount in sgramcounter.items():
		UpperCAmelCase_								=     scount * numref

	UpperCAmelCase_								=     Counter(A   )
	UpperCAmelCase_								=     Counter()
	for cgram, ccount in cgramcounter.items():
		UpperCAmelCase_								=     ccount * numref

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

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

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

	# ADDITION
	UpperCAmelCase_								=     set(A   ) - set(A   )
	UpperCAmelCase_								=     set(A   ) & set(A   )
	UpperCAmelCase_								=     set(A   ) - set(A   )

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

	return (keepscore, delscore_precision, addscore)




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     len(A   )

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

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

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




def    A_(			A				,      A = True				,      A = "13a"				,      A = 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:
		UpperCAmelCase_								=     sentence.lower()

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

	if not return_str:
		UpperCAmelCase_								=     normalized_sent.split()

	return normalized_sent




def    A_(			A				,      A				,      A   ):
	if not (len(A   ) == len(A   ) == len(A   )):
		raise ValueError("""Sources length must match predictions and references lengths."""   )
	UpperCAmelCase_								=     0
	for src, pred, refs in zip(A				,      A				,      A   ):
		sari_score += SARIsent(normalize(A   )				,      normalize(A   )				,      [normalize(A   ) for sent in refs]   )
	UpperCAmelCase_								=     sari_score / len(A   )
	return 100 * sari_score




def    A_(			A				,      A				,      A="exp"				,      A=None				,      A=False				,      A=False				,      A=False				,      ):
	UpperCAmelCase_								=     len(references[0]   )
	if any(len(A   ) != references_per_prediction for refs in references   ):
		raise ValueError("""Sacrebleu requires the same number of references for each prediction"""   )
	UpperCAmelCase_								=     [[refs[i] for refs in references] for i in range(A   )]
	UpperCAmelCase_								=     sacrebleu.corpus_bleu(
	    A				,      A				,      smooth_method=A				,      smooth_value=A				,      force=A				,      lowercase=A				,      use_effective_order=A				,      )
	return output.score







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







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						return datasets.MetricInfo(
						    description=_DESCRIPTION ,					citation=_CITATION ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
						        {
						            """predictions""": datasets.Value("""string""" ,					id="""sequence"""		),
						            """references""": datasets.Sequence(datasets.Value("""string""" ,					id="""sequence"""		) ,					id="""references"""		),
						        }		) ,					codebase_urls=[
						        """https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
						        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
						        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
						        """https://github.com/mjpost/sacreBLEU""",
						    ] ,					reference_urls=[
						        """https://www.aclweb.org/anthology/Q16-1029.pdf""",
						        """https://github.com/mjpost/sacreBLEU""",
						        """https://en.wikipedia.org/wiki/BLEU""",
						        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
						    ] ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  str ,					__lowercase    :  List[str] ,					__lowercase    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     {}
						result.update({"""sari""": compute_sari(sources=__lowercase ,					predictions=__lowercase ,					references=__lowercase		)}		)
						result.update({"""sacrebleu""": compute_sacrebleu(predictions=__lowercase ,					references=__lowercase		)}		)
						result.update({"""exact""": compute_em(predictions=__lowercase ,					references=__lowercase		)}		)
						return result









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


UpperCamelCase__   : Any					  = logging.get_logger(__name__)

UpperCamelCase__   : int					  = {
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       'trajectory_transformer'
					lowerCamelCase     :     Optional[Any]			    =       ['past_key_values']
					lowerCamelCase     :     Tuple			    =       {
					    'hidden_size': 'n_embd',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}





					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  Optional[Any]=1_00 ,					__lowercase    :  int=5 ,					__lowercase    :  str=1 ,					__lowercase    :  Union[str, Any]=1 ,					__lowercase    :  Optional[Any]=2_49 ,					__lowercase    :  Tuple=6 ,					__lowercase    :  Tuple=17 ,					__lowercase    :  List[str]=25 ,					__lowercase    :  Tuple=4 ,					__lowercase    :  Dict=4 ,					__lowercase    :  int=1_28 ,					__lowercase    :  List[str]=0.1 ,					__lowercase    :  Optional[Any]=0.1 ,					__lowercase    :  Union[str, Any]=0.1 ,					__lowercase    :  Tuple=0.0006 ,					__lowercase    :  Optional[int]=5_12 ,					__lowercase    :  Any=0.02 ,					__lowercase    :  Tuple=1e-12 ,					__lowercase    :  Optional[Any]=1 ,					__lowercase    :  Dict=True ,					__lowercase    :  Optional[Any]=1 ,					__lowercase    :  str=5_02_56 ,					__lowercase    :  Any=5_02_56 ,					**__lowercase    :  str ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     vocab_size
						UpperCAmelCase_								=     action_weight
						UpperCAmelCase_								=     reward_weight
						UpperCAmelCase_								=     value_weight
						UpperCAmelCase_								=     max_position_embeddings
						UpperCAmelCase_								=     block_size
						UpperCAmelCase_								=     action_dim
						UpperCAmelCase_								=     observation_dim
						UpperCAmelCase_								=     transition_dim
						UpperCAmelCase_								=     learning_rate
						UpperCAmelCase_								=     n_layer
						UpperCAmelCase_								=     n_head
						UpperCAmelCase_								=     n_embd
						UpperCAmelCase_								=     embd_pdrop
						UpperCAmelCase_								=     attn_pdrop
						UpperCAmelCase_								=     resid_pdrop
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     layer_norm_eps
						UpperCAmelCase_								=     kaiming_initializer_range
						UpperCAmelCase_								=     use_cache
						super().__init__(pad_token_id=__lowercase ,					bos_token_id=__lowercase ,					eos_token_id=__lowercase ,					**__lowercase		)


