



"""simple docstring"""
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A:   List[Any]								=		'''
@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:   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       _snake_case						(						UpperCamelCase						:		int						):

 def remove_articles(UpperCamelCase						:		Union[str, Any]						):
  UpperCAmelCase	:					Any	=	re.compile(R"""\b(a|an|the)\b"""			,		re.UNICODE						)
  return re.sub(__lowercase			,		""" """			,		__lowercase						)

 def white_space_fix(UpperCamelCase						:		Union[str, Any]						):
  return " ".join(text.split()						)

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

 def lower(UpperCamelCase						:		Tuple						):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(__lowercase						)						)						)						)

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Any						):
 return int(normalize_answer(__lowercase						) == normalize_answer(__lowercase						)						)

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					Dict	=	[any(compute_exact(__lowercase			,		__lowercase						) for ref in refs						) for pred, refs in zip(__lowercase			,		__lowercase						)]
 return (sum(__lowercase						) / len(__lowercase						)) * 100

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		int			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					str	=	[rgram for rgrams in rgramslist for rgram in rgrams]
 UpperCAmelCase	:					List[Any]	=	Counter(__lowercase						)

 UpperCAmelCase	:					str	=	Counter(__lowercase						)
 UpperCAmelCase	:					List[str]	=	Counter()
 for sgram, scount in sgramcounter.items():
  UpperCAmelCase	:					Tuple	=	scount * numref

 UpperCAmelCase	:					Optional[int]	=	Counter(__lowercase						)
 UpperCAmelCase	:					Any	=	Counter()
 for cgram, ccount in cgramcounter.items():
  UpperCAmelCase	:					Union[str, Any]	=	ccount * numref

 # KEEP
 UpperCAmelCase	:					Tuple	=	sgramcounter_rep & cgramcounter_rep
 UpperCAmelCase	:					Any	=	keepgramcounter_rep & rgramcounter
 UpperCAmelCase	:					str	=	sgramcounter_rep & rgramcounter

 UpperCAmelCase	:					Optional[int]	=	0
 UpperCAmelCase	:					Optional[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	:					List[str]	=	1
 UpperCAmelCase	:					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	:					Dict	=	keeptmpscorea / sum(keepgramcounterall_rep.values()						)
 UpperCAmelCase	:					Optional[int]	=	0
 if keepscore_precision > 0 or keepscore_recall > 0:
  UpperCAmelCase	:					Optional[int]	=	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 UpperCAmelCase	:					Tuple	=	sgramcounter_rep - cgramcounter_rep
 UpperCAmelCase	:					Dict	=	delgramcounter_rep - rgramcounter
 UpperCAmelCase	:					Dict	=	sgramcounter_rep - rgramcounter
 UpperCAmelCase	:					int	=	0
 UpperCAmelCase	:					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	:					Dict	=	1
 if len(__lowercase						) > 0:
  UpperCAmelCase	:					str	=	deltmpscorea / len(__lowercase						)

 # ADDITION
 UpperCAmelCase	:					Optional[int]	=	set(__lowercase						) - set(__lowercase						)
 UpperCAmelCase	:					Optional[Any]	=	set(__lowercase						) & set(__lowercase						)
 UpperCAmelCase	:					Union[str, Any]	=	set(__lowercase						) - set(__lowercase						)

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

 return (keepscore, delscore_precision, addscore)

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		Tuple						):
 UpperCAmelCase	:					List[str]	=	len(__lowercase						)

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

 UpperCAmelCase	:					Any	=	[]
 UpperCAmelCase	:					str	=	[]
 UpperCAmelCase	:					Optional[Any]	=	[]
 UpperCAmelCase	:					Tuple	=	[]
 for rsent in rsents:
  UpperCAmelCase	:					Union[str, Any]	=	rsent.split(""" """						)
  UpperCAmelCase	:					Any	=	[]
  UpperCAmelCase	:					Any	=	[]
  UpperCAmelCase	:					Tuple	=	[]
  ragramslist.append(__lowercase						)
  for i in range(0			,		len(__lowercase						) - 1						):
   if i < len(__lowercase						) - 1:
    UpperCAmelCase	:					str	=	ragrams[i] + """ """ + ragrams[i + 1]
    ragrams.append(__lowercase						)
   if i < len(__lowercase						) - 2:
    UpperCAmelCase	:					Optional[Any]	=	ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
    ragrams.append(__lowercase						)
   if i < len(__lowercase						) - 3:
    UpperCAmelCase	:					List[str]	=	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	:					Tuple	=	sagrams[i] + """ """ + sagrams[i + 1]
   sagrams.append(__lowercase						)
  if i < len(__lowercase						) - 2:
   UpperCAmelCase	:					List[str]	=	sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
   sagrams.append(__lowercase						)
  if i < len(__lowercase						) - 3:
   UpperCAmelCase	:					Dict	=	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	:					str	=	cagrams[i] + """ """ + cagrams[i + 1]
   cagrams.append(__lowercase						)
  if i < len(__lowercase						) - 2:
   UpperCAmelCase	:					Union[str, Any]	=	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))	:					List[Any]	=	SARIngram(__lowercase			,		__lowercase			,		__lowercase			,		__lowercase						)
 ((UpperCAmelCase)	,		(UpperCAmelCase)	,		(UpperCAmelCase))	:					Union[str, Any]	=	SARIngram(__lowercase			,		__lowercase			,		__lowercase			,		__lowercase						)
 ((UpperCAmelCase)	,		(UpperCAmelCase)	,		(UpperCAmelCase))	:					Union[str, Any]	=	SARIngram(__lowercase			,		__lowercase			,		__lowercase			,		__lowercase						)
 ((UpperCAmelCase)	,		(UpperCAmelCase)	,		(UpperCAmelCase))	:					int	=	SARIngram(__lowercase			,		__lowercase			,		__lowercase			,		__lowercase						)
 UpperCAmelCase	:					int	=	sum([keepascore, keepascore, keepascore, keepascore]						) / 4
 UpperCAmelCase	:					Tuple	=	sum([delascore, delascore, delascore, delascore]						) / 4
 UpperCAmelCase	:					int	=	sum([addascore, addascore, addascore, addascore]						) / 4
 UpperCAmelCase	:					Tuple	=	(avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		bool = True			,		UpperCamelCase						:		str = "13a"			,		UpperCamelCase						:		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:
  UpperCAmelCase	:					int	=	sentence.lower()

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

 if not return_str:
  UpperCAmelCase	:					Tuple	=	normalized_sent.split()

 return normalized_sent

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Tuple						):
 if not (len(__lowercase						) == len(__lowercase						) == len(__lowercase						)):
  raise ValueError("""Sources length must match predictions and references lengths."""						)
 UpperCAmelCase	:					Union[str, 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	:					Dict	=	sari_score / len(__lowercase						)
 return 100 * sari_score

def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Tuple="exp"			,		UpperCamelCase						:		Union[str, Any]=None			,		UpperCamelCase						:		Optional[Any]=False			,		UpperCamelCase						:		Any=False			,		UpperCamelCase						:		Tuple=False			,		):
 UpperCAmelCase	:					int	=	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	:					List[Any]	=	[[refs[i] for refs in references] for i in range(__lowercase						)]
 UpperCAmelCase	:					Dict	=	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       SCREAMING_SNAKE_CASE__  (					datasets.Metric ):

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





    '''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      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




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


import unittest

from transformers import LiltConfig, is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    LiltForQuestionAnswering,
			    LiltForSequenceClassification,
			    LiltForTokenClassification,
			    LiltModel,
			)
			from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Dict			,  lowercase	:    str			,  lowercase	:    List[str]=13			,  lowercase	:    Any=7			,  lowercase	:    Dict=True			,  lowercase	:    str=True			,  lowercase	:    List[Any]=True			,  lowercase	:    Any=True			,  lowercase	:    Tuple=99			,  lowercase	:    str=24			,  lowercase	:    str=2			,  lowercase	:    Any=6			,  lowercase	:    Dict=37			,  lowercase	:    List[str]="gelu"			,  lowercase	:    Dict=0.1			,  lowercase	:    Tuple=0.1			,  lowercase	:    Optional[Any]=512			,  lowercase	:    List[Any]=16			,  lowercase	:    str=2			,  lowercase	:    int=0.02			,  lowercase	:    List[Any]=3			,  lowercase	:    List[Any]=None			,  lowercase	:    int=1_000			,  ):


				'''simple docstring'''


				_snake_case		=			parent
				_snake_case		=			batch_size
				_snake_case		=			seq_length
				_snake_case		=			is_training
				_snake_case		=			use_input_mask
				_snake_case		=			use_token_type_ids
				_snake_case		=			use_labels
				_snake_case		=			vocab_size
				_snake_case		=			hidden_size
				_snake_case		=			num_hidden_layers
				_snake_case		=			num_attention_heads
				_snake_case		=			intermediate_size
				_snake_case		=			hidden_act
				_snake_case		=			hidden_dropout_prob
				_snake_case		=			attention_probs_dropout_prob
				_snake_case		=			max_position_embeddings
				_snake_case		=			type_vocab_size
				_snake_case		=			type_sequence_label_size
				_snake_case		=			initializer_range
				_snake_case		=			num_labels
				_snake_case		=			scope
				_snake_case		=			range_bbox



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size	)

				_snake_case		=			ids_tensor([self.batch_size, self.seq_length, 4]			,  self.range_bbox	)
				# Ensure that bbox is legal
				for i in range(bbox.shape[0]	):
							for j in range(bbox.shape[1]	):
										if bbox[i, j, 3] < bbox[i, j, 1]:
													_snake_case		=			bbox[i, j, 3]
													_snake_case		=			bbox[i, j, 1]
													_snake_case		=			t
										if bbox[i, j, 2] < bbox[i, j, 0]:
													_snake_case		=			bbox[i, j, 2]
													_snake_case		=			bbox[i, j, 0]
													_snake_case		=			t

				_snake_case		=			None
				if self.use_input_mask:
							_snake_case		=			ids_tensor([self.batch_size, self.seq_length]			,  vocab_size=2	)

				_snake_case		=			None
				if self.use_token_type_ids:
							_snake_case		=			ids_tensor([self.batch_size, self.seq_length]			,  self.type_vocab_size	)

				_snake_case		=			None
				_snake_case		=			None
				if self.use_labels:
							_snake_case		=			ids_tensor([self.batch_size]			,  self.type_sequence_label_size	)
							_snake_case		=			ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels	)

				_snake_case		=			self.get_config()

				return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return LiltConfig(
				    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			,  initializer_range=self.initializer_range			,  )



	def  A							(  self	:    str			,  lowercase	:    Tuple			,  lowercase	:    Tuple			,  lowercase	:    str			,  lowercase	:    Any			,  lowercase	:    Union[str, Any]			,  lowercase	:    List[str]			,  lowercase	:    str			,  ):


				'''simple docstring'''


				_snake_case		=			LiltModel(config=lowercase	)
				model.to(lowercase	)
				model.eval()
				_snake_case		=			model(lowercase			,  bbox=lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase	)
				_snake_case		=			model(lowercase			,  bbox=lowercase			,  token_type_ids=lowercase	)
				_snake_case		=			model(lowercase			,  bbox=lowercase	)

				self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)	)
				self.parent.assertEqual(result.pooler_output.shape			,  (self.batch_size, self.hidden_size)	)



	def  A							(  self	:    List[Any]			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    Any			,  lowercase	:    Optional[int]			,  lowercase	:    Union[str, Any]			,  lowercase	:    Optional[Any]			,  lowercase	:    Optional[int]			,  ):


				'''simple docstring'''


				_snake_case		=			self.num_labels
				_snake_case		=			LiltForTokenClassification(config=lowercase	)
				model.to(lowercase	)
				model.eval()
				_snake_case		=			model(
				    lowercase			,  bbox=lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase			,  labels=lowercase	)
				self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)	)



	def  A							(  self	:    List[str]			,  lowercase	:    Union[str, Any]			,  lowercase	:    str			,  lowercase	:    Dict			,  lowercase	:    Optional[int]			,  lowercase	:    List[str]			,  lowercase	:    int			,  lowercase	:    int			,  ):


				'''simple docstring'''


				_snake_case		=			LiltForQuestionAnswering(config=lowercase	)
				model.to(lowercase	)
				model.eval()
				_snake_case		=			model(
				    lowercase			,  bbox=lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase			,  start_positions=lowercase			,  end_positions=lowercase			,  )
				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  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			self.prepare_config_and_inputs()
				(
				    (
				    _snake_case
				)	,					(
				    _snake_case
				)	,					(
				    _snake_case
				)	,					(
				    _snake_case
				)	,					(
				    _snake_case
				)	,					(
				    _snake_case
				)	,					(
				    _snake_case
				)	,					
				)		=			config_and_inputs
				_snake_case		=			{
				    'input_ids': input_ids,
				    'bbox': bbox,
				    'token_type_ids': token_type_ids,
				    'attention_mask': input_mask,
				}
				return config, inputs_dict




@require_torch
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase							,UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		List[Any]       =					(
	    (
	        LiltModel,
	        LiltForSequenceClassification,
	        LiltForTokenClassification,
	        LiltForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_UpperCAmelCase       :		List[str]       =					(
	    {
	        "feature-extraction": LiltModel,
	        "question-answering": LiltForQuestionAnswering,
	        "text-classification": LiltForSequenceClassification,
	        "token-classification": LiltForTokenClassification,
	        "zero-shot": LiltForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_UpperCAmelCase       :		Optional[Any]       =					False
	_UpperCAmelCase       :		Union[str, Any]       =					False



	def  A							(  self	:    Dict			,  lowercase	:    Dict			,  lowercase	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[str]			,  lowercase	:    Tuple	):


				'''simple docstring'''


				return True



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			LiltModelTester(self	)
				_snake_case		=			ConfigTester(self			,  config_class=lowercase			,  hidden_size=37	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				self.config_tester.run_common_tests()



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase	)



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							_snake_case		=			type
							self.model_tester.create_and_check_model(*lowercase	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*lowercase	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*lowercase	)





	@slow
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							_snake_case		=			LiltModel.from_pretrained(lowercase	)
							self.assertIsNotNone(lowercase	)




@require_torch
@slow
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'	).to(lowercase	)

				_snake_case		=			torch.tensor([[1, 2]]			,  device=lowercase	)
				_snake_case		=			torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]			,  device=lowercase	)

				# forward pass
				with torch.no_grad():
							_snake_case		=			model(input_ids=lowercase			,  bbox=lowercase	)

				_snake_case		=			torch.Size([1, 2, 768]	)
				_snake_case		=			torch.tensor(
				    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]			,  device=lowercase			,  )

				self.assertTrue(outputs.last_hidden_state.shape			,  lowercase	)
				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]			,  lowercase			,  atol=1E-3	)	)