




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 __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						str):

        """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''': 224, '''width''': 224},
            '''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,
        }
        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						_snake_case)
        with open(self.image_processor_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             json.dump(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Dict					,						**_snake_case		:						Union[str, Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Dict					,						**_snake_case		:						List[Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Optional[int]					,						**_snake_case		:						Optional[Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        shutil.rmtree(self.tmpdirname)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


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

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

        return image_inputs







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


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

        UpperCAmelCase_          =							ChineseCLIPProcessor(tokenizer=_snake_case					,						image_processor=_snake_case)
        processor_slow.save_pretrained(self.tmpdirname)
        UpperCAmelCase_          =							ChineseCLIPProcessor.from_pretrained(self.tmpdirname					,						use_fast=_snake_case)

        UpperCAmelCase_          =							ChineseCLIPProcessor(tokenizer=_snake_case					,						image_processor=_snake_case)
        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					,						_snake_case)
        self.assertIsInstance(processor_fast.tokenizer					,						_snake_case)

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







   def lowerCamelCase						(      self		:						Optional[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=_snake_case)

        UpperCAmelCase_          =							ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname					,						cls_token='''(CLS)'''					,						sep_token='''(SEP)'''					,						do_normalize=_snake_case)

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

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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_image_processor()
        UpperCAmelCase_          =							self.get_tokenizer()

        UpperCAmelCase_          =							ChineseCLIPProcessor(tokenizer=_snake_case					,						image_processor=_snake_case)

        UpperCAmelCase_          =							self.prepare_image_inputs()

        UpperCAmelCase_          =							image_processor(_snake_case					,						return_tensors='''np''')
        UpperCAmelCase_          =							processor(images=_snake_case					,						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 lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_image_processor()
        UpperCAmelCase_          =							self.get_tokenizer()

        UpperCAmelCase_          =							ChineseCLIPProcessor(tokenizer=_snake_case					,						image_processor=_snake_case)

        UpperCAmelCase_          =							'''Alexandra，T-shirt的价格是15便士。'''

        UpperCAmelCase_          =							processor(text=_snake_case)

        UpperCAmelCase_          =							tokenizer(_snake_case)

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







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_image_processor()
        UpperCAmelCase_          =							self.get_tokenizer()

        UpperCAmelCase_          =							ChineseCLIPProcessor(tokenizer=_snake_case					,						image_processor=_snake_case)

        UpperCAmelCase_          =							'''Alexandra，T-shirt的价格是15便士。'''
        UpperCAmelCase_          =							self.prepare_image_inputs()

        UpperCAmelCase_          =							processor(text=_snake_case					,						images=_snake_case)

        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(_snake_case):
             processor()







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_image_processor()
        UpperCAmelCase_          =							self.get_tokenizer()

        UpperCAmelCase_          =							ChineseCLIPProcessor(tokenizer=_snake_case					,						image_processor=_snake_case)

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

        UpperCAmelCase_          =							processor.batch_decode(_snake_case)
        UpperCAmelCase_          =							tokenizer.batch_decode(_snake_case)

        self.assertListEqual(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_image_processor()
        UpperCAmelCase_          =							self.get_tokenizer()

        UpperCAmelCase_          =							ChineseCLIPProcessor(tokenizer=_snake_case					,						image_processor=_snake_case)

        UpperCAmelCase_          =							'''Alexandra，T-shirt的价格是15便士。'''
        UpperCAmelCase_          =							self.prepare_image_inputs()

        UpperCAmelCase_          =							processor(text=_snake_case					,						images=_snake_case)

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











"""simple docstring"""
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


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)

_lowerCAmelCase    :List[str]     =					'▁'

_lowerCAmelCase    :Tuple     =					{'vocab_file': 'sentencepiece.bpe.model'}

_lowerCAmelCase    :List[Any]     =					{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_lowerCAmelCase    :Tuple     =					{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=['''input_ids''', '''attention_mask''']





     def __init__(					self       ,	A       ,	A="<s>"       ,	A="</s>"       ,	A="</s>"       ,	A="<s>"       ,	A="<unk>"       ,	A="<pad>"       ,	A="<mask>"       ,	A = None       ,	**A       ,	)							->					None:
         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase							:  Tuple											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else mask_token

         _UpperCAmelCase							:  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       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**A       ,	)

         _UpperCAmelCase							:  Any											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(A		)		)
         _UpperCAmelCase							:  List[Any]											=    vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         _UpperCAmelCase							:  List[str]											=    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         _UpperCAmelCase							:  Any											=    1

         _UpperCAmelCase							:  Optional[Any]											=    len(self.sp_model		) + self.fairseq_offset
         _UpperCAmelCase							:  int											=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}





     def __getstate__(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Tuple											=    self.__dict__.copy()
         _UpperCAmelCase							:  List[str]											=    None
         _UpperCAmelCase							:  str											=    self.sp_model.serialized_model_proto()
         return state





     def __setstate__(					self       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[int]											=    d

         # for backward compatibility
         if not hasattr(self       ,	'''sp_model_kwargs'''		):
             _UpperCAmelCase							:  Optional[Any]											=    {}

         _UpperCAmelCase							:  Optional[int]											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _UpperCAmelCase							:  Any											=    [self.cls_token_id]
         _UpperCAmelCase							:  Any											=    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = False		)							->					List[int]:

         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       ,	A       ,	A = None		)							->					List[int]:
         _UpperCAmelCase							:  Dict											=    [self.sep_token_id]
         _UpperCAmelCase							:  List[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 + sep + token_ids_a + sep		) * [0]





     @property
     def  __lowerCAmelCase	(					self		)							->					Dict:
         return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Dict											=    {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       ,	A		)							->					List[str]:
         return self.sp_model.encode(A       ,	out_type=A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Any:
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         _UpperCAmelCase							:  Any											=    self.sp_model.PieceToId(A		)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         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       ,	A		)							->					int:
         _UpperCAmelCase							:  str											=    ''''''.join(A		).replace(A       ,	''' '''		).strip()
         return out_string





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					Tuple[str]:
         if not os.path.isdir(A		):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
             return
         _UpperCAmelCase							:  List[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:
                 _UpperCAmelCase							:  str											=    self.sp_model.serialized_model_proto()
                 fi.write(A		)

         return (out_vocab_file,)


