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

import sentencepiece as spm

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


_UpperCAmelCase						=		logging.get_logger(__name__)

_UpperCAmelCase						=		{"vocab_file": "sentencepiece.bpe.model"}

_UpperCAmelCase						=		{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

_UpperCAmelCase						=		{
    "camembert-base": 512,
}

_UpperCAmelCase						=		"▁"





class UpperCAmelCase						(					lowerCAmelCase__  ):


				'''simple docstring'''






				lowerCamelCase_    =					VOCAB_FILES_NAMES
				lowerCamelCase_    =					PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    =					["""input_ids""", """attention_mask"""]
				def __init__(						self		,	lowercase		,	lowercase="<s>"		,	lowercase="</s>"		,	lowercase="</s>"		,	lowercase="<s>"		,	lowercase="<unk>"		,	lowercase="<pad>"		,	lowercase="<mask>"		,	lowercase=["<s>NOTUSED", "</s>NOTUSED"]		,	lowercase = None		,	**lowercase		,	):



						"""simple docstring"""





						A_		:				Optional[Any]     =      AddedToken(__a		,	lstrip=__a		,	rstrip=__a					) if isinstance(__a		,	__a					) else mask_token

						A_		:				List[Any]     =      {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=__a		,	eos_token=__a		,	unk_token=__a		,	sep_token=__a		,	cls_token=__a		,	pad_token=__a		,	mask_token=__a		,	additional_special_tokens=__a		,	sp_model_kwargs=self.sp_model_kwargs		,	**__a		,	)
						A_		:				Any     =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
						self.sp_model.Load(str(__a					)					)
						A_		:				Any     =      vocab_file
						# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
						# sentencepiece vocabulary (this is the case for <s> and </s>
						A_		:				List[str]     =      {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
						A_		:				Optional[Any]     =      len(self.fairseq_tokens_to_ids					)
						A_		:				Optional[Any]     =      len(self.sp_model					) + len(self.fairseq_tokens_to_ids					)
						A_		:				Union[str, Any]     =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None					):



						"""simple docstring"""





						if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A_		:				Optional[Any]     =      [self.cls_token_id]
						A_		:				Optional[int]     =      [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None		,	lowercase = False					):



						"""simple docstring"""





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

						if token_ids_a is None:
								return [1] + ([0] * len(__a					)) + [1]
						return [1] + ([0] * len(__a					)) + [1, 1] + ([0] * len(__a					)) + [1]
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None					):



						"""simple docstring"""





						A_		:				Any     =      [self.sep_token_id]
						A_		:				Union[str, Any]     =      [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]
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return len(self.fairseq_tokens_to_ids					) + len(self.sp_model					)
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				int     =      {self.convert_ids_to_tokens(__a					): i for i in range(self.vocab_size					)}
						vocab.update(self.added_tokens_encoder					)
						return vocab
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						return self.sp_model.encode(__a		,	out_type=__a					)
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
						elif self.sp_model.PieceToId(__a					) == 0:
								# Convert sentence piece unk token to fairseq unk token index
								return self.unk_token_id
						return self.fairseq_offset + self.sp_model.PieceToId(__a					)
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset					)
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						A_		:				int     =      []
						A_		:				Any     =      ''
						A_		:				Optional[Any]     =      False
						for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
										if not prev_is_special:
												out_string += " "
										out_string += self.sp_model.decode(__a					) + token
										A_		:				str     =      True
										A_		:				Optional[int]     =      []
								else:
										current_sub_tokens.append(__a					)
										A_		:				Tuple     =      False
						out_string += self.sp_model.decode(__a					)
						return out_string.strip()
				def __getstate__(						self					):



						"""simple docstring"""





						A_		:				Dict     =      self.__dict__.copy()
						A_		:				List[Any]     =      None
						return state
				def __setstate__(						self		,	lowercase					):



						"""simple docstring"""





						A_		:				Dict     =      d

						# for backward compatibility
						if not hasattr(self		,	'sp_model_kwargs'					):
								A_		:				Dict     =      {}

						A_		:				Union[str, Any]     =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
						self.sp_model.Load(self.vocab_file					)







				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None					):



						"""simple docstring"""





						if not os.path.isdir(__a					):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
								return
						A_		:				Union[str, Any]     =      os.path.join(
						    __a		,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

						if os.path.abspath(self.vocab_file					) != os.path.abspath(__a					) and os.path.isfile(self.vocab_file					):
								copyfile(self.vocab_file		,	__a					)
						elif not os.path.isfile(self.vocab_file					):
								with open(__a		,	'wb'					) as fi:
										A_		:				Union[str, Any]     =      self.sp_model.serialized_model_proto()
										fi.write(__a					)

						return (out_vocab_file,)










"""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   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			tempfile.mkdtemp()

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

										__lowerCAmelCase				    =			{
										    "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": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
										    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
										    "do_convert_rgb": True,
										}
										__lowerCAmelCase				    =			os.path.join(self.tmpdirname    ,   __a	)
										with open(self.image_processor_file    ,   "w"    ,   encoding="utf-8"	) as fp:
													json.dump(__a    ,   __a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizer.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizerFast.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self	):
										shutil.rmtree(self.tmpdirname	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			[np.random.randint(2_55    ,   size=(3, 30, 4_00)    ,   dtype=np.uinta	)]

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

										return image_inputs

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			self.get_rust_tokenizer()
										__lowerCAmelCase				    =			self.get_image_processor()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_slow.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,   use_fast=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_fast.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			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    ,   __a	)
										self.assertIsInstance(processor_fast.tokenizer    ,   __a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,   image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										__lowerCAmelCase				    =			self.get_tokenizer(cls_token="(CLS)"    ,   sep_token="(SEP)"	)
										__lowerCAmelCase				    =			self.get_image_processor(do_normalize=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname    ,   cls_token="(CLS)"    ,   sep_token="(SEP)"    ,   do_normalize=__a	)

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

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			image_processor(__a    ,   return_tensors="np"	)
										__lowerCAmelCase				    =			processor(images=__a    ,   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	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

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

										__lowerCAmelCase				    =			processor(text=__a	)

										__lowerCAmelCase				    =			tokenizer(__a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

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

										__lowerCAmelCase				    =			processor(text=__a    ,   images=__a	)

										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(__a	):
													processor()

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

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

										__lowerCAmelCase				    =			processor.batch_decode(__a	)
										__lowerCAmelCase				    =			tokenizer.batch_decode(__a	)

										self.assertListEqual(__a    ,   __a	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

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

										__lowerCAmelCase				    =			processor(text=__a    ,   images=__a	)

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




