

"""simple docstring"""





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       RobertaTokenizer
					lowercase__				 =       RobertaTokenizerFast
					lowercase__				 =       True
					lowercase__				 =       {"cls_token": "<s>"}







					def        __lowerCAmelCase (       self			:				Any		):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						lowerCAmelCase__	:						Any  				=	[
						    '''l''',
						    '''o''',
						    '''w''',
						    '''e''',
						    '''r''',
						    '''s''',
						    '''t''',
						    '''i''',
						    '''d''',
						    '''n''',
						    '''\u0120''',
						    '''\u0120l''',
						    '''\u0120n''',
						    '''\u0120lo''',
						    '''\u0120low''',
						    '''er''',
						    '''\u0120lowest''',
						    '''\u0120newer''',
						    '''\u0120wider''',
						    '''<unk>''',
						]
						lowerCAmelCase__	:						str  				=	dict(zip(lowerCamelCase_   ,range(len(lowerCamelCase_		)		)		)		)
						lowerCAmelCase__	:						Tuple  				=	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
						lowerCAmelCase__	:						Any  				=	{'''unk_token''': '''<unk>'''}

						lowerCAmelCase__	:						List[str]  				=	os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''vocab_file''']		)
						lowerCAmelCase__	:						Optional[int]  				=	os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''merges_file''']		)
						with open(self.vocab_file   ,'''w'''   ,encoding='''utf-8'''		) as fp:
							fp.write(json.dumps(lowerCamelCase_		) + '''\n'''		)
						with open(self.merges_file   ,'''w'''   ,encoding='''utf-8'''		) as fp:
							fp.write('''\n'''.join(lowerCamelCase_		)		)







					def        __lowerCAmelCase (       self			:				Dict   ,**lowercase_			:				Optional[int]		):
						kwargs.update(self.special_tokens_map		)
						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,**lowerCamelCase_		)







					def        __lowerCAmelCase (       self			:				Tuple   ,**lowercase_			:				Tuple		):
						kwargs.update(self.special_tokens_map		)
						return RobertaTokenizerFast.from_pretrained(self.tmpdirname   ,**lowerCamelCase_		)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				Dict		):
						lowerCAmelCase__	:						List[str]  				=	'''lower newer'''
						lowerCAmelCase__	:						Optional[int]  				=	'''lower newer'''
						return input_text, output_text







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	self.tokenizer_class(self.vocab_file   ,self.merges_file   ,**self.special_tokens_map		)
						lowerCAmelCase__	:						str  				=	'''lower newer'''
						lowerCAmelCase__	:						Any  				=	['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
						lowerCAmelCase__	:						int  				=	tokenizer.tokenize(lowerCamelCase_		)  # , add_prefix_space=True)
						self.assertListEqual(lowerCamelCase_   ,lowerCamelCase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	tokens + [tokenizer.unk_token]
						lowerCAmelCase__	:						Optional[int]  				=	[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_		)   ,lowerCamelCase_		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_tokenizer()

						self.assertListEqual(tokenizer.encode('''Hello world!'''   ,add_special_tokens=lowerCamelCase_		)   ,[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]		)
						self.assertListEqual(
						    tokenizer.encode('''Hello world! cécé herlolip 418'''   ,add_special_tokens=lowerCamelCase_		)   ,[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]   ,)







					@slow
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Any  				=	self.tokenizer_class.from_pretrained('''roberta-base'''		)

						lowerCAmelCase__	:						Optional[int]  				=	tokenizer.encode('''sequence builders'''   ,add_special_tokens=lowerCamelCase_		)
						lowerCAmelCase__	:						str  				=	tokenizer.encode('''multi-sequence build'''   ,add_special_tokens=lowerCamelCase_		)

						lowerCAmelCase__	:						Dict  				=	tokenizer.encode(
						    '''sequence builders'''   ,add_special_tokens=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_		)
						lowerCAmelCase__	:						List[str]  				=	tokenizer.encode(
						    '''sequence builders'''   ,'''multi-sequence build'''   ,add_special_tokens=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_		)

						lowerCAmelCase__	:						List[str]  				=	tokenizer.build_inputs_with_special_tokens(lowerCamelCase_		)
						lowerCAmelCase__	:						int  				=	tokenizer.build_inputs_with_special_tokens(lowerCamelCase_   ,lowerCamelCase_		)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Dict  				=	self.get_tokenizer()

						lowerCAmelCase__	:						int  				=	'''Encode this sequence.'''
						lowerCAmelCase__	:						Dict  				=	tokenizer.byte_encoder[''' '''.encode('''utf-8'''		)[0]]

						# Testing encoder arguments
						lowerCAmelCase__	:						int  				=	tokenizer.encode(lowerCamelCase_   ,add_special_tokens=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_		)
						lowerCAmelCase__	:						Optional[int]  				=	tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
						self.assertNotEqual(lowerCamelCase_   ,lowerCamelCase_		)

						lowerCAmelCase__	:						str  				=	tokenizer.encode(lowerCamelCase_   ,add_special_tokens=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_		)
						lowerCAmelCase__	:						List[Any]  				=	tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
						self.assertEqual(lowerCamelCase_   ,lowerCamelCase_		)

						tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}		)
						lowerCAmelCase__	:						Tuple  				=	tokenizer.encode(lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
						lowerCAmelCase__	:						List[str]  				=	tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
						self.assertNotEqual(lowerCamelCase_   ,lowerCamelCase_		)

						# Testing spaces after special tokens
						lowerCAmelCase__	:						Union[str, Any]  				=	'''<mask>'''
						tokenizer.add_special_tokens(
						    {'''mask_token''': AddedToken(lowerCamelCase_   ,lstrip=lowerCamelCase_   ,rstrip=lowerCamelCase_		)}		)  # mask token has a left space
						lowerCAmelCase__	:						List[str]  				=	tokenizer.convert_tokens_to_ids(lowerCamelCase_		)

						lowerCAmelCase__	:						List[str]  				=	'''Encode <mask> sequence'''
						lowerCAmelCase__	:						Optional[int]  				=	'''Encode <mask>sequence'''

						lowerCAmelCase__	:						int  				=	tokenizer.encode(lowerCamelCase_		)
						lowerCAmelCase__	:						List[Any]  				=	encoded.index(lowerCamelCase_		)
						lowerCAmelCase__	:						Tuple  				=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
						self.assertEqual(lowerCamelCase_   ,lowerCamelCase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	tokenizer.encode(lowerCamelCase_		)
						lowerCAmelCase__	:						Tuple  				=	encoded.index(lowerCamelCase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
						self.assertNotEqual(lowerCamelCase_   ,lowerCamelCase_		)







					def        __lowerCAmelCase (       self			:				str		):
						pass







					def        __lowerCAmelCase (       self			:				Tuple		):

						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
								lowerCAmelCase__	:						int  				=	self.rust_tokenizer_class.from_pretrained(lowerCamelCase_   ,**lowerCamelCase_		)
								lowerCAmelCase__	:						List[str]  				=	self.tokenizer_class.from_pretrained(lowerCamelCase_   ,**lowerCamelCase_		)
								lowerCAmelCase__	:						str  				=	'''A, <mask> AllenNLP sentence.'''
								lowerCAmelCase__	:						Optional[Any]  				=	tokenizer_r.encode_plus(lowerCamelCase_   ,add_special_tokens=lowerCamelCase_   ,return_token_type_ids=lowerCamelCase_		)
								lowerCAmelCase__	:						Optional[int]  				=	tokenizer_p.encode_plus(lowerCamelCase_   ,add_special_tokens=lowerCamelCase_   ,return_token_type_ids=lowerCamelCase_		)

								# token_type_ids should put 0 everywhere
								self.assertEqual(sum(tokens_r['''token_type_ids''']		)   ,sum(tokens_p['''token_type_ids''']		)		)

								# attention_mask should put 1 everywhere, so sum over length should be 1
								self.assertEqual(
								    sum(tokens_r['''attention_mask''']		) / len(tokens_r['''attention_mask''']		)   ,sum(tokens_p['''attention_mask''']		) / len(tokens_p['''attention_mask''']		)   ,)

								lowerCAmelCase__	:						Dict  				=	tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
								lowerCAmelCase__	:						List[Any]  				=	tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)

								# Rust correctly handles the space before the mask while python doesnt
								self.assertSequenceEqual(tokens_p['''input_ids''']   ,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)
								self.assertSequenceEqual(tokens_r['''input_ids''']   ,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)

								self.assertSequenceEqual(
								    lowerCamelCase_   ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
								self.assertSequenceEqual(
								    lowerCamelCase_   ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)







					def        __lowerCAmelCase (       self			:				Dict		):

						for trim_offsets, add_prefix_space in itertools.product([True, False]   ,repeat=2		):
							lowerCAmelCase__	:						Optional[int]  				=	self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)

							lowerCAmelCase__	:						Tuple  				=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
							lowerCAmelCase__	:						Union[str, Any]  				=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

							self.assertEqual(pre_tokenizer_state['''add_prefix_space''']   ,lowerCamelCase_		)

							self.assertEqual(post_processor_state['''add_prefix_space''']   ,lowerCamelCase_		)
							self.assertEqual(post_processor_state['''trim_offsets''']   ,lowerCamelCase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):

						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
								lowerCAmelCase__	:						str  				=	'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
								lowerCAmelCase__	:						List[Any]  				=	F'{text_of_1_token} {text_of_1_token}'

								lowerCAmelCase__	:						int  				=	self.rust_tokenizer_class.from_pretrained(
								    lowerCamelCase_   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)
								lowerCAmelCase__	:						List[Any]  				=	tokenizer_r(lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
								self.assertEqual(encoding.offset_mapping[0]   ,(0, len(lowerCamelCase_		))		)
								self.assertEqual(
								    encoding.offset_mapping[1]   ,(len(lowerCamelCase_		) + 1, len(lowerCamelCase_		) + 1 + len(lowerCamelCase_		))   ,)

								lowerCAmelCase__	:						int  				=	self.rust_tokenizer_class.from_pretrained(
								    lowerCamelCase_   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)
								lowerCAmelCase__	:						Dict  				=	tokenizer_r(lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
								self.assertEqual(encoding.offset_mapping[0]   ,(0, len(lowerCamelCase_		))		)
								self.assertEqual(
								    encoding.offset_mapping[1]   ,(len(lowerCamelCase_		) + 1, len(lowerCamelCase_		) + 1 + len(lowerCamelCase_		))   ,)

								lowerCAmelCase__	:						Tuple  				=	self.rust_tokenizer_class.from_pretrained(
								    lowerCamelCase_   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)
								lowerCAmelCase__	:						Dict  				=	tokenizer_r(lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
								self.assertEqual(encoding.offset_mapping[0]   ,(0, len(lowerCamelCase_		))		)
								self.assertEqual(
								    encoding.offset_mapping[1]   ,(len(lowerCamelCase_		), len(lowerCamelCase_		) + 1 + len(lowerCamelCase_		))   ,)

								lowerCAmelCase__	:						List[str]  				=	self.rust_tokenizer_class.from_pretrained(
								    lowerCamelCase_   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)
								lowerCAmelCase__	:						int  				=	tokenizer_r(lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
								self.assertEqual(encoding.offset_mapping[0]   ,(0, len(lowerCamelCase_		))		)
								self.assertEqual(
								    encoding.offset_mapping[1]   ,(len(lowerCamelCase_		), len(lowerCamelCase_		) + 1 + len(lowerCamelCase_		))   ,)

								lowerCAmelCase__	:						List[str]  				=	F' {text}'

								# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
								#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
								# )
								# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
								# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
								# self.assertEqual(
								#     encoding.offset_mapping[1],
								#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
								# )

								lowerCAmelCase__	:						Optional[int]  				=	self.rust_tokenizer_class.from_pretrained(
								    lowerCamelCase_   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)
								lowerCAmelCase__	:						Optional[int]  				=	tokenizer_r(lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
								self.assertEqual(encoding.offset_mapping[0]   ,(1, 1 + len(lowerCamelCase_		))		)
								self.assertEqual(
								    encoding.offset_mapping[1]   ,(1 + len(lowerCamelCase_		) + 1, 1 + len(lowerCamelCase_		) + 1 + len(lowerCamelCase_		))   ,)

								lowerCAmelCase__	:						Union[str, Any]  				=	self.rust_tokenizer_class.from_pretrained(
								    lowerCamelCase_   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)
								lowerCAmelCase__	:						Tuple  				=	tokenizer_r(lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
								self.assertEqual(encoding.offset_mapping[0]   ,(0, 1 + len(lowerCamelCase_		))		)
								self.assertEqual(
								    encoding.offset_mapping[1]   ,(1 + len(lowerCamelCase_		), 1 + len(lowerCamelCase_		) + 1 + len(lowerCamelCase_		))   ,)

								lowerCAmelCase__	:						Optional[int]  				=	self.rust_tokenizer_class.from_pretrained(
								    lowerCamelCase_   ,use_fast=lowerCamelCase_   ,add_prefix_space=lowerCamelCase_   ,trim_offsets=lowerCamelCase_		)
								lowerCAmelCase__	:						List[str]  				=	tokenizer_r(lowerCamelCase_   ,return_offsets_mapping=lowerCamelCase_   ,add_special_tokens=lowerCamelCase_		)
								self.assertEqual(encoding.offset_mapping[0]   ,(0, 1 + len(lowerCamelCase_		))		)
								self.assertEqual(
								    encoding.offset_mapping[1]   ,(1 + len(lowerCamelCase_		), 1 + len(lowerCamelCase_		) + 1 + len(lowerCamelCase_		))   ,)









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 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   tempfile.mkdtemp()

						a					     =   [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "的",
						    "价",
						    "格",
						    "是",
						    "15",
						    "便",
						    "alex",
						    "##andra",
						    "，",
						    "。",
						    "-",
						    "t",
						    "shirt",
						]
						a					     =   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]		)		)

						a					     =   {
						    "do_resize": True,
						    "size": {"height": 224, "width": 224},
						    "do_center_crop": True,
						    "crop_size": {"height": 18, "width": 18},
						    "do_normalize": True,
						    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
						    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
						    "do_convert_rgb": True,
						}
						a					     =   os.path.join(self.tmpdirname		,       lowerCamelCase_		)
						with open(self.image_processor_file		,       "w"		,       encoding="utf-8"		) as fp:
											json.dump(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						shutil.rmtree(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   [np.random.randint(255		,       size=(3, 30, 400)		,       dtype=np.uinta		)]

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

						return image_inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_slow.save_pretrained(self.tmpdirname		)
						a					     =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,       use_fast=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_fast.save_pretrained(self.tmpdirname		)
						a					     =   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		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.tokenizer		,       lowerCamelCase_		)

						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		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.image_processor		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   self.get_tokenizer(cls_token="(CLS)"		,       sep_token="(SEP)"		)
						a					     =   self.get_image_processor(do_normalize=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname		,       cls_token="(CLS)"		,       sep_token="(SEP)"		,       do_normalize=lowerCamelCase_		)

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   self.prepare_image_inputs()

						a					     =   image_processor(lowerCamelCase_		,       return_tensors="np"		)
						a					     =   processor(images=lowerCamelCase_		,       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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

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

						a					     =   processor(text=lowerCamelCase_		)

						a					     =   tokenizer(lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

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

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

						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(lowerCamelCase_		):
											processor()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

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

						a					     =   processor.batch_decode(lowerCamelCase_		)
						a					     =   tokenizer.batch_decode(lowerCamelCase_		)

						self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

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

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

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