





from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

UpperCamelCase__   : Any					  = _symbol_database.Default()


UpperCamelCase__   : Any					  = _descriptor_pool.Default().AddSerializedFile(
    b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)

UpperCamelCase__   : Union[str, Any]					  = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
				UpperCamelCase__   : Tuple					  = None
				UpperCamelCase__   : int					  = b"""H\003"""
				# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
				# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
				# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
				# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
				# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
				UpperCamelCase__   : Tuple					  = 45
				UpperCamelCase__   : Any					  = 15_81
				UpperCamelCase__   : Dict					  = 15_17
				UpperCamelCase__   : Tuple					  = 15_70
				UpperCamelCase__   : Optional[int]					  = 15_84
				UpperCamelCase__   : int					  = 17_93
				UpperCamelCase__   : List[Any]					  = 17_95
				UpperCamelCase__   : List[Any]					  = 19_16
				UpperCamelCase__   : Optional[int]					  = 18_64
				UpperCamelCase__   : Dict					  = 19_05
				UpperCamelCase__   : List[str]					  = 19_19
				UpperCamelCase__   : Optional[Any]					  = 24_29
				UpperCamelCase__   : Optional[Any]					  = 22_08
				UpperCamelCase__   : List[str]					  = 24_18
				UpperCamelCase__   : Tuple					  = 23_23
				UpperCamelCase__   : Optional[int]					  = 24_07
# @@protoc_insertion_point(module_scope)









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		)


