



'''simple docstring'''




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a   :			List[str]					  =    """
@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\",
}
"""

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


a   :			Dict					  =    """
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       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:

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

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

							def remove_punc(_lowercase		):
														UpperCAmelCase     :		Optional[Any]							     =		set(string.punctuation		)
														return "".join(ch for ch in text if ch not in exclude		)

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

							return white_space_fix(remove_articles(remove_punc(lower(_lowercase		)		)		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			List[Any]:
							return int(normalize_answer(_lowercase		) == normalize_answer(_lowercase		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Tuple:
							UpperCAmelCase     :		Optional[int]							     =		[any(compute_exact(_lowercase	,				_lowercase		) for ref in refs		) for pred, refs in zip(_lowercase	,				_lowercase		)]
							return (sum(_lowercase		) / len(_lowercase		)) * 1_0_0



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Dict:
							UpperCAmelCase     :		Optional[Any]							     =		[rgram for rgrams in rgramslist for rgram in rgrams]
							UpperCAmelCase     :		Tuple							     =		Counter(_lowercase		)

							UpperCAmelCase     :		Any							     =		Counter(_lowercase		)
							UpperCAmelCase     :		List[Any]							     =		Counter()
							for sgram, scount in sgramcounter.items():
														UpperCAmelCase     :		Dict							     =		scount * numref

							UpperCAmelCase     :		str							     =		Counter(_lowercase		)
							UpperCAmelCase     :		Dict							     =		Counter()
							for cgram, ccount in cgramcounter.items():
														UpperCAmelCase     :		Dict							     =		ccount * numref

							# KEEP
							UpperCAmelCase     :		Union[str, Any]							     =		sgramcounter_rep & cgramcounter_rep
							UpperCAmelCase     :		List[str]							     =		keepgramcounter_rep & rgramcounter
							UpperCAmelCase     :		Any							     =		sgramcounter_rep & rgramcounter

							UpperCAmelCase     :		Dict							     =		0
							UpperCAmelCase     :		Any							     =		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     :		Optional[Any]							     =		1
							UpperCAmelCase     :		Optional[Any]							     =		1
							if len(_lowercase		) > 0:
														UpperCAmelCase     :		Any							     =		keeptmpscorea / len(_lowercase		)
							if len(_lowercase		) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														UpperCAmelCase     :		List[str]							     =		keeptmpscorea / sum(keepgramcounterall_rep.values()		)
							UpperCAmelCase     :		Optional[Any]							     =		0
							if keepscore_precision > 0 or keepscore_recall > 0:
														UpperCAmelCase     :		int							     =		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							UpperCAmelCase     :		str							     =		sgramcounter_rep - cgramcounter_rep
							UpperCAmelCase     :		Union[str, Any]							     =		delgramcounter_rep - rgramcounter
							UpperCAmelCase     :		Any							     =		sgramcounter_rep - rgramcounter
							UpperCAmelCase     :		Optional[Any]							     =		0
							UpperCAmelCase     :		Union[str, Any]							     =		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     :		str							     =		1
							if len(_lowercase		) > 0:
														UpperCAmelCase     :		Any							     =		deltmpscorea / len(_lowercase		)

							# ADDITION
							UpperCAmelCase     :		Tuple							     =		set(_lowercase		) - set(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		set(_lowercase		) & set(_lowercase		)
							UpperCAmelCase     :		List[str]							     =		set(_lowercase		) - set(_lowercase		)

							UpperCAmelCase     :		Union[str, Any]							     =		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     :		Union[str, Any]							     =		1
							UpperCAmelCase     :		str							     =		1
							if len(_lowercase		) > 0:
														UpperCAmelCase     :		Dict							     =		addtmpscore / len(_lowercase		)
							if len(_lowercase		) > 0:
														UpperCAmelCase     :		Any							     =		addtmpscore / len(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		0
							if addscore_precision > 0 or addscore_recall > 0:
														UpperCAmelCase     :		Any							     =		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Tuple							     =		len(_lowercase		)

							UpperCAmelCase     :		List[str]							     =		ssent.split(""" """		)
							UpperCAmelCase     :		Optional[int]							     =		csent.split(""" """		)
							UpperCAmelCase     :		Optional[Any]							     =		[]
							UpperCAmelCase     :		Union[str, Any]							     =		[]
							UpperCAmelCase     :		Tuple							     =		[]
							UpperCAmelCase     :		List[str]							     =		[]
							UpperCAmelCase     :		Optional[Any]							     =		[]
							UpperCAmelCase     :		Optional[int]							     =		[]

							UpperCAmelCase     :		List[Any]							     =		[]
							UpperCAmelCase     :		List[Any]							     =		[]
							UpperCAmelCase     :		Tuple							     =		[]
							UpperCAmelCase     :		int							     =		[]
							for rsent in rsents:
														UpperCAmelCase     :		int							     =		rsent.split(""" """		)
														UpperCAmelCase     :		List[Any]							     =		[]
														UpperCAmelCase     :		List[str]							     =		[]
														UpperCAmelCase     :		Any							     =		[]
														ragramslist.append(_lowercase		)
														for i in range(0	,				len(_lowercase		) - 1		):
																					if i < len(_lowercase		) - 1:
																												UpperCAmelCase     :		Optional[int]							     =		ragrams[i] + """ """ + ragrams[i + 1]
																												ragrams.append(_lowercase		)
																					if i < len(_lowercase		) - 2:
																												UpperCAmelCase     :		Union[str, Any]							     =		ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																												ragrams.append(_lowercase		)
																					if i < len(_lowercase		) - 3:
																												UpperCAmelCase     :		Dict							     =		ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																												ragrams.append(_lowercase		)
														ragramslist.append(_lowercase		)
														ragramslist.append(_lowercase		)
														ragramslist.append(_lowercase		)

							for i in range(0	,				len(_lowercase		) - 1		):
														if i < len(_lowercase		) - 1:
																					UpperCAmelCase     :		Optional[int]							     =		sagrams[i] + """ """ + sagrams[i + 1]
																					sagrams.append(_lowercase		)
														if i < len(_lowercase		) - 2:
																					UpperCAmelCase     :		Optional[Any]							     =		sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
																					sagrams.append(_lowercase		)
														if i < len(_lowercase		) - 3:
																					UpperCAmelCase     :		str							     =		sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
																					sagrams.append(_lowercase		)

							for i in range(0	,				len(_lowercase		) - 1		):
														if i < len(_lowercase		) - 1:
																					UpperCAmelCase     :		List[str]							     =		cagrams[i] + """ """ + cagrams[i + 1]
																					cagrams.append(_lowercase		)
														if i < len(_lowercase		) - 2:
																					UpperCAmelCase     :		int							     =		cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
																					cagrams.append(_lowercase		)
														if i < len(_lowercase		) - 3:
																					UpperCAmelCase     :		List[Any]							     =		cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
																					cagrams.append(_lowercase		)

							((UpperCAmelCase)     ,     (UpperCAmelCase)     ,     (UpperCAmelCase))     :		Dict							     =		SARIngram(_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)
							((UpperCAmelCase)     ,     (UpperCAmelCase)     ,     (UpperCAmelCase))     :		List[Any]							     =		SARIngram(_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)
							((UpperCAmelCase)     ,     (UpperCAmelCase)     ,     (UpperCAmelCase))     :		int							     =		SARIngram(_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)
							((UpperCAmelCase)     ,     (UpperCAmelCase)     ,     (UpperCAmelCase))     :		Tuple							     =		SARIngram(_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)
							UpperCAmelCase     :		Optional[int]							     =		sum([keepascore, keepascore, keepascore, keepascore]		) / 4
							UpperCAmelCase     :		List[str]							     =		sum([delascore, delascore, delascore, delascore]		) / 4
							UpperCAmelCase     :		Any							     =		sum([addascore, addascore, addascore, addascore]		) / 4
							UpperCAmelCase     :		Any							     =		(avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore



def       __lowerCamelCase  (					_lowercase	,				_lowercase = True	,				_lowercase = "13a"	,				_lowercase = True		)      ->			Union[str, Any]:
							# 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     :		Any							     =		sentence.lower()

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

							if not return_str:
														UpperCAmelCase     :		Union[str, Any]							     =		normalized_sent.split()

							return normalized_sent



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Any:
							if not (len(_lowercase		) == len(_lowercase		) == len(_lowercase		)):
														raise ValueError("""Sources length must match predictions and references lengths."""		)
							UpperCAmelCase     :		Any							     =		0
							for src, pred, refs in zip(_lowercase	,				_lowercase	,				_lowercase		):
														sari_score += SARIsent(normalize(_lowercase		)	,				normalize(_lowercase		)	,				[normalize(_lowercase		) for sent in refs]		)
							UpperCAmelCase     :		Optional[int]							     =		sari_score / len(_lowercase		)
							return 1_0_0 * sari_score



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase="exp"	,				_lowercase=None	,				_lowercase=False	,				_lowercase=False	,				_lowercase=False	,				)      ->			Tuple:
							UpperCAmelCase     :		Dict							     =		len(references[0]		)
							if any(len(_lowercase		) != references_per_prediction for refs in references		):
														raise ValueError("""Sacrebleu requires the same number of references for each prediction"""		)
							UpperCAmelCase     :		Optional[Any]							     =		[[refs[i] for refs in references] for i in range(_lowercase		)]
							UpperCAmelCase     :		List[Any]							     =		sacrebleu.corpus_bleu(
							    _lowercase	,				_lowercase	,				smooth_method=_lowercase	,				smooth_value=_lowercase	,				force=_lowercase	,				lowercase=_lowercase	,				use_effective_order=_lowercase	,				)
							return output.score


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





	def  _lowercase(  self    )			->			List[str]:
								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  _lowercase(  self		,		A		,		A		,		A    )			->			Dict:
								UpperCAmelCase     :		int							     =		{}
								result.update({"""sari""": compute_sari(sources=A		,		predictions=A		,		references=A    )}    )
								result.update({"""sacrebleu""": compute_sacrebleu(predictions=A		,		references=A    )}    )
								result.update({"""exact""": compute_em(predictions=A		,		references=A    )}    )
								return result






'''simple docstring'''




import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    GPTNeoXForCausalLM,
							    GPTNeoXForQuestionAnswering,
							    GPTNeoXForSequenceClassification,
							    GPTNeoXForTokenClassification,
							    GPTNeoXModel,
							)

class 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A=13		,		A=7		,		A=True		,		A=True		,		A=True		,		A=True		,		A=99		,		A=64		,		A=5		,		A=4		,		A=37		,		A="gelu"		,		A=0.1		,		A=0.1		,		A=512		,		A=16		,		A=2		,		A=0.0_2		,		A=3		,		A=4		,		A=None		,		)			->			Optional[int]:
								UpperCAmelCase     :		List[Any]							     =		parent
								UpperCAmelCase     :		Optional[int]							     =		batch_size
								UpperCAmelCase     :		Union[str, Any]							     =		seq_length
								UpperCAmelCase     :		Optional[Any]							     =		is_training
								UpperCAmelCase     :		Dict							     =		use_input_mask
								UpperCAmelCase     :		str							     =		use_token_type_ids
								UpperCAmelCase     :		List[Any]							     =		use_labels
								UpperCAmelCase     :		List[Any]							     =		vocab_size
								UpperCAmelCase     :		Dict							     =		hidden_size
								UpperCAmelCase     :		Dict							     =		num_hidden_layers
								UpperCAmelCase     :		Optional[int]							     =		num_attention_heads
								UpperCAmelCase     :		int							     =		intermediate_size
								UpperCAmelCase     :		List[str]							     =		hidden_act
								UpperCAmelCase     :		List[str]							     =		hidden_dropout_prob
								UpperCAmelCase     :		int							     =		attention_probs_dropout_prob
								UpperCAmelCase     :		str							     =		max_position_embeddings
								UpperCAmelCase     :		Optional[Any]							     =		type_vocab_size
								UpperCAmelCase     :		List[str]							     =		type_sequence_label_size
								UpperCAmelCase     :		int							     =		initializer_range
								UpperCAmelCase     :		str							     =		num_labels
								UpperCAmelCase     :		Optional[int]							     =		num_choices
								UpperCAmelCase     :		Dict							     =		scope
								UpperCAmelCase     :		Union[str, Any]							     =		vocab_size - 1





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		List[str]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Any							     =		None
								if self.use_input_mask:
															UpperCAmelCase     :		int							     =		random_attention_mask([self.batch_size, self.seq_length]    )

								UpperCAmelCase     :		List[str]							     =		None
								if self.use_labels:
															UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels    )

								UpperCAmelCase     :		Optional[int]							     =		self.get_config()

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self    )			->			Optional[Any]:
								return GPTNeoXConfig(
								    vocab_size=self.vocab_size		,		hidden_size=self.hidden_size		,		num_hidden_layers=self.num_hidden_layers		,		num_attention_heads=self.num_attention_heads		,		intermediate_size=self.intermediate_size		,		hidden_act=self.hidden_act		,		hidden_dropout_prob=self.hidden_dropout_prob		,		attention_probs_dropout_prob=self.attention_probs_dropout_prob		,		max_position_embeddings=self.max_position_embeddings		,		type_vocab_size=self.type_vocab_size		,		is_decoder=A		,		initializer_range=self.initializer_range		,		pad_token_id=self.pad_token_id		,		)





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.prepare_config_and_inputs()

								UpperCAmelCase     :		Any							     =		True

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		str							     =		GPTNeoXModel(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A    )
								UpperCAmelCase     :		List[str]							     =		model(A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		str							     =		True
								UpperCAmelCase     :		Optional[Any]							     =		GPTNeoXModel(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[Any]							     =		model(A		,		attention_mask=A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			List[str]:
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		str							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			Tuple:
								UpperCAmelCase     :		List[str]							     =		self.num_labels
								UpperCAmelCase     :		Any							     =		GPTNeoXForQuestionAnswering(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		str							     =		model(A		,		attention_mask=A    )
								self.parent.assertEqual(result.start_logits.shape		,		(self.batch_size, self.seq_length)    )
								self.parent.assertEqual(result.end_logits.shape		,		(self.batch_size, self.seq_length)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		Tuple							     =		self.num_labels
								UpperCAmelCase     :		List[str]							     =		GPTNeoXForSequenceClassification(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[Any]							     =		ids_tensor([self.batch_size]		,		self.type_sequence_label_size    )
								UpperCAmelCase     :		Tuple							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			str:
								UpperCAmelCase     :		List[Any]							     =		self.num_labels
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForTokenClassification(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		int							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)    )





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[int]							     =		True
								UpperCAmelCase     :		str							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()

								# first forward pass
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A		,		use_cache=A    )
								UpperCAmelCase     :		List[Any]							     =		outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase     :		Optional[int]							     =		ids_tensor((self.batch_size, 3)		,		config.vocab_size    )
								UpperCAmelCase     :		Any							     =		ids_tensor((self.batch_size, 3)		,		vocab_size=2    )

								# append to next input_ids and
								UpperCAmelCase     :		str							     =		torch.cat([input_ids, next_tokens]		,		dim=-1    )
								UpperCAmelCase     :		Any							     =		torch.cat([input_mask, next_mask]		,		dim=-1    )

								UpperCAmelCase     :		Dict							     =		model(A		,		attention_mask=A		,		output_hidden_states=A    )
								UpperCAmelCase     :		Any							     =		output_from_no_past["""hidden_states"""][0]
								UpperCAmelCase     :		List[str]							     =		model(
								    A		,		attention_mask=A		,		past_key_values=A		,		output_hidden_states=A		,		)["""hidden_states"""][0]

								# select random slice
								UpperCAmelCase     :		Tuple							     =		ids_tensor((1,)		,		output_from_past.shape[-1]    ).item()
								UpperCAmelCase     :		List[Any]							     =		output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase     :		List[str]							     =		output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(A		,		A		,		atol=1e-3    )    )






	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Tuple							     =		self.prepare_config_and_inputs()
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		config_and_inputs
								UpperCAmelCase     :		Union[str, Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = (
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase          = (GPTNeoXForCausalLM,) if is_torch_available() else ()
	lowercase          = (
	    {
	        'feature-extraction': GPTNeoXModel,
	        'question-answering': GPTNeoXForQuestionAnswering,
	        'text-classification': GPTNeoXForSequenceClassification,
	        'text-generation': GPTNeoXForCausalLM,
	        'token-classification': GPTNeoXForTokenClassification,
	        'zero-shot': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase          = False
	lowercase          = False
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		GPTNeoXModelTester(self    )
								UpperCAmelCase     :		Optional[Any]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=64		,		num_attention_heads=8    )





	def  _lowercase(  self    )			->			Optional[Any]:
								self.config_tester.run_common_tests()





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								# This regression test was failing with PyTorch < 1.3
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()

								UpperCAmelCase     :		Optional[Any]							     =		None

								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(A		,		A		,		A    )





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*A    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*A    )





	@unittest.skip(reason="""Feed forward chunking is not implemented"""    )
	def  _lowercase(  self    )			->			Optional[int]:
								pass






	@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
	def  _lowercase(  self		,		A    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase     :		int							     =		ids_tensor([1, 10]		,		config.vocab_size    )
								UpperCAmelCase     :		Optional[Any]							     =		ids_tensor([1, int(config.max_position_embeddings * 1.5    )]		,		config.vocab_size    )

								set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase     :		Dict							     =		GPTNeoXModel(A    )
								original_model.to(A    )
								original_model.eval()
								UpperCAmelCase     :		List[str]							     =		original_model(A    ).last_hidden_state
								UpperCAmelCase     :		Any							     =		original_model(A    ).last_hidden_state

								set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase     :		Any							     =		{"""type""": scaling_type, """factor""": 1_0.0}
								UpperCAmelCase     :		str							     =		GPTNeoXModel(A    )
								scaled_model.to(A    )
								scaled_model.eval()
								UpperCAmelCase     :		Optional[Any]							     =		scaled_model(A    ).last_hidden_state
								UpperCAmelCase     :		Optional[Any]							     =		scaled_model(A    ).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
															self.assertTrue(torch.allclose(A		,		A		,		atol=1e-5    )    )
								else:
															self.assertFalse(torch.allclose(A		,		A		,		atol=1e-5    )    )

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(A		,		A		,		atol=1e-5    )    )




@require_torch
class 			UpperCamelCase_ (							unittest.TestCase			):





	@slow
	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )
								for checkpointing in [True, False]:
															UpperCAmelCase     :		int							     =		GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )

															if checkpointing:
																						model.gradient_checkpointing_enable()
															else:
																						model.gradient_checkpointing_disable()
															model.to(A    )

															UpperCAmelCase     :		List[Any]							     =		tokenizer("""My favorite food is"""		,		return_tensors="""pt"""    ).to(A    )
															# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
															# See: https://github.com/huggingface/transformers/pull/24193
															UpperCAmelCase     :		List[str]							     =		"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

															UpperCAmelCase     :		Union[str, Any]							     =		model.generate(**A		,		do_sample=A		,		max_new_tokens=20    )
															UpperCAmelCase     :		Tuple							     =		tokenizer.batch_decode(A    )[0]

															self.assertEqual(A		,		A    )

