





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









import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     tempfile.mkdtemp()

						UpperCAmelCase_								=     [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """的""",
						    """价""",
						    """格""",
						    """是""",
						    """15""",
						    """便""",
						    """alex""",
						    """##andra""",
						    """，""",
						    """。""",
						    """-""",
						    """t""",
						    """shirt""",
						]
						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]		)
						with open(self.vocab_file ,					"""w""" ,					encoding="""utf-8"""		) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

						UpperCAmelCase_								=     {
						    """do_resize""": True,
						    """size""": {"""height""": 2_24, """width""": 2_24},
						    """do_center_crop""": True,
						    """crop_size""": {"""height""": 18, """width""": 18},
						    """do_normalize""": True,
						    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
						    """image_std""": [0.26862954, 0.26130258, 0.27577711],
						    """do_convert_rgb""": True,
						}
						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					__lowercase		)
						with open(self.image_processor_file ,					"""w""" ,					encoding="""utf-8"""		) as fp:
							json.dump(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizer.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizerFast.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					**__lowercase    :  int		):






						'''simple docstring'''







						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						shutil.rmtree(self.tmpdirname		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     [np.random.randint(2_55 ,					size=(3, 30, 4_00) ,					dtype=np.uinta		)]

						UpperCAmelCase_								=     [Image.fromarray(np.moveaxis(__lowercase ,					0 ,					-1		)		) for x in image_inputs]

						return image_inputs





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_tokenizer()
						UpperCAmelCase_								=     self.get_rust_tokenizer()
						UpperCAmelCase_								=     self.get_image_processor()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_slow.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(self.tmpdirname ,					use_fast=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_fast.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

						self.assertEqual(processor_slow.tokenizer.get_vocab() ,					tokenizer_slow.get_vocab()		)
						self.assertEqual(processor_fast.tokenizer.get_vocab() ,					tokenizer_fast.get_vocab()		)
						self.assertEqual(tokenizer_slow.get_vocab() ,					tokenizer_fast.get_vocab()		)
						self.assertIsInstance(processor_slow.tokenizer ,					__lowercase		)
						self.assertIsInstance(processor_fast.tokenizer ,					__lowercase		)

						self.assertEqual(processor_slow.image_processor.to_json_string() ,					image_processor.to_json_string()		)
						self.assertEqual(processor_fast.image_processor.to_json_string() ,					image_processor.to_json_string()		)
						self.assertIsInstance(processor_slow.image_processor ,					__lowercase		)
						self.assertIsInstance(processor_fast.image_processor ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						UpperCAmelCase_								=     self.get_tokenizer(cls_token="""(CLS)""" ,					sep_token="""(SEP)"""		)
						UpperCAmelCase_								=     self.get_image_processor(do_normalize=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname ,					cls_token="""(CLS)""" ,					sep_token="""(SEP)""" ,					do_normalize=__lowercase		)

						self.assertEqual(processor.tokenizer.get_vocab() ,					tokenizer_add_kwargs.get_vocab()		)
						self.assertIsInstance(processor.tokenizer ,					__lowercase		)

						self.assertEqual(processor.image_processor.to_json_string() ,					image_processor_add_kwargs.to_json_string()		)
						self.assertIsInstance(processor.image_processor ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     image_processor(__lowercase ,					return_tensors="""np"""		)
						UpperCAmelCase_								=     processor(images=__lowercase ,					return_tensors="""np"""		)

						for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum() ,					input_processor[key].sum() ,					delta=1e-2		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""

						UpperCAmelCase_								=     processor(text=__lowercase		)

						UpperCAmelCase_								=     tokenizer(__lowercase		)

						for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key] ,					encoded_processor[key]		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

						self.assertListEqual(list(inputs.keys()		) ,					["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]		)

						# test if it raises when no input is passed
						with pytest.raises(__lowercase		):
							processor()





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCAmelCase_								=     processor.batch_decode(__lowercase		)
						UpperCAmelCase_								=     tokenizer.batch_decode(__lowercase		)

						self.assertListEqual(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

						self.assertListEqual(list(inputs.keys()		) ,					processor.model_input_names		)


