

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  int   	=						logging.get_logger(__name__)

lowerCAmelCase							:  Tuple   	=						{"""vocab_file""": """sentencepiece.model"""}

lowerCAmelCase							:  int   	=						{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCAmelCase							:  Any   	=						{
    """google/rembert""": 256,
}



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							int										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							Tuple										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[int]=False   ,						lowerCAmelCase__  :     Union[str, Any]=True   ,						lowerCAmelCase__  :     Any=True   ,						lowerCAmelCase__  :     List[str]="[CLS]"   ,						lowerCAmelCase__  :     Tuple="[SEP]"   ,						lowerCAmelCase__  :     List[str]="[UNK]"   ,						lowerCAmelCase__  :     Union[str, Any]="[SEP]"   ,						lowerCAmelCase__  :     List[Any]="[PAD]"   ,						lowerCAmelCase__  :     int="[CLS]"   ,						lowerCAmelCase__  :     Optional[Any]="[MASK]"   ,						**lowerCAmelCase__  :     Tuple   ,						):
									super().__init__(
									    do_lower_case=lowerCAmelCase__   ,						remove_space=lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__   ,						bos_token=lowerCAmelCase__   ,						eos_token=lowerCAmelCase__   ,						unk_token=lowerCAmelCase__   ,						sep_token=lowerCAmelCase__   ,						pad_token=lowerCAmelCase__   ,						cls_token=lowerCAmelCase__   ,						mask_token=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       do_lower_case
									SCREAMING_SNAKE_CASE_: Any  		=       remove_space
									SCREAMING_SNAKE_CASE_: Tuple  		=       keep_accents
									SCREAMING_SNAKE_CASE_: int  		=       vocab_file

									SCREAMING_SNAKE_CASE_: List[Any]  		=       spm.SentencePieceProcessor()
									self.sp_model.Load(lowerCAmelCase__)



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									return len(self.sp_model)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
									vocab.update(self.added_tokens_encoder)
									return vocab



			def __getstate__( self  :     int):
									SCREAMING_SNAKE_CASE_: int  		=       self.__dict__.copy()
									SCREAMING_SNAKE_CASE_: Any  		=       None
									return state



			def __setstate__( self  :     str   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       d
									SCREAMING_SNAKE_CASE_: Dict  		=       spm.SentencePieceProcessor()
									self.sp_model.Load(self.vocab_file)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     List[str]=False):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.sp_model.EncodeAsPieces(lowerCAmelCase__)
									return pieces



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any):
									return self.sp_model.PieceToId(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return self.sp_model.IdToPiece(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.sp_model.decode_pieces(lowerCAmelCase__)
									return out_string



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Dict  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: str  		=       [self.cls_token_id]
									if token_ids_a is None:
															return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None   ,						lowerCAmelCase__  :     bool = False):

									if already_has_special_tokens:
															if token_ids_a is not None:
																					raise ValueError(
																					    "You should not supply a second sequence if the provided sequence of "
																					    "ids is already formatted with special tokens for the model.")
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
															return [1] + ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1]
									return [1] + ([0] * len(lowerCAmelCase__)) + [1]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Any  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: str  		=       [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) * [0] + len(token_ids_a + sep) * [1]




			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = None):
									if not os.path.isdir(lowerCAmelCase__):
															logger.error("Vocabulary path ({}) should be a directory".format(lowerCAmelCase__))
															return
									SCREAMING_SNAKE_CASE_: Any  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

									if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__):
															copyfile(self.vocab_file   ,						lowerCAmelCase__)

									return (out_vocab_file,)











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		)
