





"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

UpperCamelCase_									=logging.get_logger(__name__)


UpperCamelCase_									={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCamelCase_									={
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

UpperCamelCase_									={"""facebook/blenderbot-3B""": 128}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	a_    (							):
						_UpperCamelCase      : List[str]         =   (
						    list(range(ord('''!'''       )	,						ord('''~'''       ) + 1       )       ) + list(range(ord('''¡'''       )	,						ord('''¬'''       ) + 1       )       ) + list(range(ord('''®'''       )	,						ord('''ÿ'''       ) + 1       )       )
						)
						_UpperCamelCase      : List[Any]         =   bs[:]
						_UpperCamelCase      : List[Any]         =   0
						for b in range(2**8       ):
												if b not in bs:
																		bs.append(_lowercase       )
																		cs.append(2**8 + n       )
																		n += 1
						_UpperCamelCase      : Tuple         =   [chr(_lowercase       ) for n in cs]
						return dict(zip(_lowercase	,						_lowercase       )       )





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()
						_UpperCamelCase      : int         =   word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)       )
												_UpperCamelCase      : Tuple         =   char
						return pairs






class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=   ['''input_ids''', '''attention_mask''']






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   str="replace",			lowerCAmelCase__		:   str="<s>",			lowerCAmelCase__		:   List[str]="</s>",			lowerCAmelCase__		:   int="</s>",			lowerCAmelCase__		:   Dict="<s>",			lowerCAmelCase__		:   int="<unk>",			lowerCAmelCase__		:   str="<pad>",			lowerCAmelCase__		:   Optional[Any]="<mask>",			lowerCAmelCase__		:   Optional[int]=False,			**lowerCAmelCase__		:   Tuple,			)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else bos_token
										_UpperCamelCase      : Union[str, Any]         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else eos_token
										_UpperCamelCase      : Optional[int]         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else sep_token
										_UpperCamelCase      : str         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else cls_token
										_UpperCamelCase      : List[Any]         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else unk_token
										_UpperCamelCase      : Optional[int]         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										_UpperCamelCase      : Any         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else mask_token

										super().__init__(
										    errors=lowerCAmelCase__,			bos_token=lowerCAmelCase__,			eos_token=lowerCAmelCase__,			unk_token=lowerCAmelCase__,			sep_token=lowerCAmelCase__,			cls_token=lowerCAmelCase__,			pad_token=lowerCAmelCase__,			mask_token=lowerCAmelCase__,			add_prefix_space=lowerCAmelCase__,			**lowerCAmelCase__,			)

										with open(lowerCAmelCase__,			encoding='''utf-8'''							) as vocab_handle:
																_UpperCamelCase      : List[Any]         =   json.load(lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   {v: k for k, v in self.encoder.items()}
										_UpperCamelCase      : List[str]         =   errors  # how to handle errors in decoding
										_UpperCamelCase      : Union[str, Any]         =   bytes_to_unicode()
										_UpperCamelCase      : List[str]         =   {v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase__,			encoding='''utf-8'''							) as merges_handle:
																_UpperCamelCase      : Optional[int]         =   merges_handle.read().split('''\n'''							)[1:-1]
										_UpperCamelCase      : int         =   [tuple(merge.split()							) for merge in bpe_merges]
										_UpperCamelCase      : str         =   dict(zip(lowerCAmelCase__,			range(len(lowerCAmelCase__							)							)							)							)
										_UpperCamelCase      : str         =   {}
										_UpperCamelCase      : Optional[int]         =   add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										_UpperCamelCase      : str         =   re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''							)






				@property
				# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
				def     snake_case     (			self		:   Tuple							)     ->				int:




										'''simple docstring'''



										return len(self.encoder							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										return dict(self.encoder,			**self.added_tokens_encoder							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   str							)     ->				Optional[Any]:




										'''simple docstring'''



										if token in self.cache:
																return self.cache[token]
										_UpperCamelCase      : Optional[Any]         =   tuple(lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   get_pairs(lowerCAmelCase__							)

										if not pairs:
																return token

										while True:
																_UpperCamelCase      : Optional[int]         =   min(lowerCAmelCase__,			key=lambda lowerCAmelCase__							: self.bpe_ranks.get(lowerCAmelCase__,			float('''inf'''							)							)							)
																if bigram not in self.bpe_ranks:
																						break
																_UpperCamelCase       ,							_UpperCamelCase      : int         =   bigram
																_UpperCamelCase      : Optional[Any]         =   []
																_UpperCamelCase      : Any         =   0
																while i < len(lowerCAmelCase__							):
																						try:
																												_UpperCamelCase      : Optional[Any]         =   word.index(lowerCAmelCase__,			lowerCAmelCase__							)
																						except ValueError:
																												new_word.extend(word[i:]							)
																												break
																						else:
																												new_word.extend(word[i:j]							)
																												_UpperCamelCase      : int         =   j

																						if word[i] == first and i < len(lowerCAmelCase__							) - 1 and word[i + 1] == second:
																												new_word.append(first + second							)
																												i += 2
																						else:
																												new_word.append(word[i]							)
																												i += 1
																_UpperCamelCase      : Dict         =   tuple(lowerCAmelCase__							)
																_UpperCamelCase      : List[str]         =   new_word
																if len(lowerCAmelCase__							) == 1:
																						break
																else:
																						_UpperCamelCase      : Dict         =   get_pairs(lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   ''' '''.join(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[Any]         =   word
										return word






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   Tuple							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   []
										for token in re.findall(self.pat,			lowerCAmelCase__							):
																_UpperCamelCase      : Optional[Any]         =   ''''''.join(
																    self.byte_encoder[b] for b in token.encode('''utf-8'''							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__							).split(''' '''							)							)
										return bpe_tokens






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   Dict							)     ->				Optional[Any]:




										'''simple docstring'''



										return self.encoder.get(lowerCAmelCase__,			self.encoder.get(self.unk_token							)							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Optional[int]							)     ->				int:




										'''simple docstring'''



										return self.decoder.get(lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   str							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   ''''''.join(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8''',			errors=self.errors							)
										return text






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str] = None							)     ->				Tuple[str]:




										'''simple docstring'''



										if not os.path.isdir(lowerCAmelCase__							):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
																return
										_UpperCamelCase      : Optional[Any]         =   os.path.join(
										    lowerCAmelCase__,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
										_UpperCamelCase      : List[str]         =   os.path.join(
										    lowerCAmelCase__,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']							)

										with open(lowerCAmelCase__,			'''w''',			encoding='''utf-8'''							) as f:
																f.write(json.dumps(self.encoder,			indent=2,			sort_keys=lowerCAmelCase__,			ensure_ascii=lowerCAmelCase__							) + '''\n'''							)

										_UpperCamelCase      : int         =   0
										with open(lowerCAmelCase__,			'''w''',			encoding='''utf-8'''							) as writer:
																writer.write('''#version: 0.2\n'''							)
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),			key=lambda lowerCAmelCase__							: kv[1]							):
																						if index != token_index:
																												logger.warning(
																												    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																												    ''' Please check that the tokenizer is not corrupted!'''							)
																												_UpperCamelCase      : Dict         =   token_index
																						writer.write(''' '''.join(lowerCAmelCase__							) + '''\n'''							)
																						index += 1

										return vocab_file, merge_file






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None,			lowerCAmelCase__		:   bool = False							)     ->				List[int]:




										'''simple docstring'''



										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase__,			token_ids_a=lowerCAmelCase__,			already_has_special_tokens=lowerCAmelCase__							)

										if token_ids_a is None:
																return [1] + ([0] * len(lowerCAmelCase__							)) + [1]
										return [1] + ([0] * len(lowerCAmelCase__							)) + [1, 1] + ([0] * len(lowerCAmelCase__							)) + [1]






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   [self.sep_token_id]
										_UpperCamelCase      : int         =   [self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep							) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Dict=False,			**lowerCAmelCase__		:   Dict							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   kwargs.pop('''add_prefix_space''',			self.add_prefix_space							)
										if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__							) > 0 and not text[0].isspace()):
																_UpperCamelCase      : str         =   ''' ''' + text
										return (text, kwargs)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[Any]:




										'''simple docstring'''



										return token_ids_a + [self.eos_token_id]






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   "Conversation"							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   []
										for is_user, text in conversation.iter_texts():
																if is_user:
																						# We need to space prefix as it's being done within blenderbot
																						inputs.append(''' ''' + text							)
																else:
																						# Generated responses should contain them already.
																						inputs.append(lowerCAmelCase__							)

										_UpperCamelCase      : str         =   '''  '''.join(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   self.encode(lowerCAmelCase__							)
										if len(lowerCAmelCase__							) > self.model_max_length:
																_UpperCamelCase      : List[Any]         =   input_ids[-self.model_max_length :]
																logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""							)
										return input_ids










"""simple docstring"""




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 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : int         =   tempfile.mkdtemp()

										_UpperCamelCase      : List[str]         =   [
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''的''',
										    '''价''',
										    '''格''',
										    '''是''',
										    '''15''',
										    '''便''',
										    '''alex''',
										    '''##andra''',
										    '''，''',
										    '''。''',
										    '''-''',
										    '''t''',
										    '''shirt''',
										]
										_UpperCamelCase      : Optional[Any]         =   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      : Dict         =   {
										    '''do_resize''': True,
										    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
										    '''do_center_crop''': True,
										    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
										    '''do_normalize''': True,
										    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										    '''do_convert_rgb''': True,
										}
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			lowerCAmelCase__							)
										with open(self.image_processor_file,			'''w''',			encoding='''utf-8'''							) as fp:
																json.dump(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   str,			**lowerCAmelCase__		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Union[str, Any],			**lowerCAmelCase__		:   Tuple							)     ->				str:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Any,			**lowerCAmelCase__		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname							)






				def     snake_case     (			self		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   [np.random.randint(2_5_5,			size=(3, 3_0, 4_0_0),			dtype=np.uinta							)]

										_UpperCamelCase      : List[Any]         =   [Image.fromarray(np.moveaxis(lowerCAmelCase__,			0,			-1							)							) for x in image_inputs]

										return image_inputs






				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_tokenizer()
										_UpperCamelCase      : int         =   self.get_rust_tokenizer()
										_UpperCamelCase      : int         =   self.get_image_processor()

										_UpperCamelCase      : Tuple         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   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     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										_UpperCamelCase      : Dict         =   self.get_tokenizer(cls_token='''(CLS)''',			sep_token='''(SEP)'''							)
										_UpperCamelCase      : List[str]         =   self.get_image_processor(do_normalize=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   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     snake_case     (			self		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.get_image_processor()
										_UpperCamelCase      : str         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.prepare_image_inputs()

										_UpperCamelCase      : Any         =   image_processor(lowerCAmelCase__,			return_tensors='''np'''							)
										_UpperCamelCase      : Any         =   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     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Any         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   '''Alexandra，T-shirt的价格是15便士。'''

										_UpperCamelCase      : List[str]         =   processor(text=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   tokenizer(lowerCAmelCase__							)

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






				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Dict         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : Union[str, Any]         =   self.prepare_image_inputs()

										_UpperCamelCase      : str         =   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     snake_case     (			self		:   Optional[Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_image_processor()
										_UpperCamelCase      : int         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase      : List[Any]         =   processor.batch_decode(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   tokenizer.batch_decode(lowerCAmelCase__							)

										self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_image_processor()
										_UpperCamelCase      : Optional[int]         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : int         =   self.prepare_image_inputs()

										_UpperCamelCase      : Dict         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

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



