'''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 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  tempfile.mkdtemp()

         __UpperCamelCase	:	Union[str, Any]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             '[PAD]',
             '[MASK]',
             '的',
             '价',
             '格',
             '是',
             '15',
             '便',
             'alex',
             '##andra',
             '，',
             '。',
             '-',
             't',
             'shirt',
         ]
         __UpperCamelCase	:	str		    =  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	:	Tuple		    =  {
             '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': [0.4_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	:	int		    =  os.path.join(self.tmpdirname     ,			_SCREAMING_SNAKE_CASE    )
         with open(self.image_processor_file     ,			'w'     ,			encoding='utf-8'    ) as fp:
             json.dump(_SCREAMING_SNAKE_CASE     ,			_SCREAMING_SNAKE_CASE    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return BertTokenizer.from_pretrained(self.tmpdirname     ,			**_SCREAMING_SNAKE_CASE    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return BertTokenizerFast.from_pretrained(self.tmpdirname     ,			**_SCREAMING_SNAKE_CASE    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,			**_SCREAMING_SNAKE_CASE    )







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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  [np.random.randint(2_5_5     ,			size=(3, 3_0, 4_0_0)     ,			dtype=np.uinta    )]

         __UpperCamelCase	:	List[str]		    =  [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE     ,			0     ,			-1    )    ) for x in image_inputs]

         return image_inputs







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  self.get_tokenizer()
         __UpperCamelCase	:	Union[str, Any]		    =  self.get_rust_tokenizer()
         __UpperCamelCase	:	Any		    =  self.get_image_processor()

         __UpperCamelCase	:	int		    =  ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,			image_processor=_SCREAMING_SNAKE_CASE    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __UpperCamelCase	:	int		    =  ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,			use_fast=_SCREAMING_SNAKE_CASE    )

         __UpperCamelCase	:	Union[str, Any]		    =  ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,			image_processor=_SCREAMING_SNAKE_CASE    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __UpperCamelCase	:	str		    =  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     ,			_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(processor_fast.tokenizer     ,			_SCREAMING_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     ,			_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(processor_fast.image_processor     ,			_SCREAMING_SNAKE_CASE    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  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	:	str		    =  self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE    )

         __UpperCamelCase	:	List[str]		    =  ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname     ,			cls_token='(CLS)'     ,			sep_token='(SEP)'     ,			do_normalize=_SCREAMING_SNAKE_CASE    )

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

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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.get_image_processor()
         __UpperCamelCase	:	List[str]		    =  self.get_tokenizer()

         __UpperCamelCase	:	List[Any]		    =  ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,			image_processor=_SCREAMING_SNAKE_CASE    )

         __UpperCamelCase	:	int		    =  self.prepare_image_inputs()

         __UpperCamelCase	:	Optional[int]		    =  image_processor(_SCREAMING_SNAKE_CASE     ,			return_tensors='np'    )
         __UpperCamelCase	:	int		    =  processor(images=_SCREAMING_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    ):
         __UpperCamelCase	:	Any		    =  self.get_image_processor()
         __UpperCamelCase	:	Any		    =  self.get_tokenizer()

         __UpperCamelCase	:	Dict		    =  ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,			image_processor=_SCREAMING_SNAKE_CASE    )

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

         __UpperCamelCase	:	Optional[Any]		    =  processor(text=_SCREAMING_SNAKE_CASE    )

         __UpperCamelCase	:	Optional[int]		    =  tokenizer(_SCREAMING_SNAKE_CASE    )

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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.get_image_processor()
         __UpperCamelCase	:	Union[str, Any]		    =  self.get_tokenizer()

         __UpperCamelCase	:	List[Any]		    =  ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,			image_processor=_SCREAMING_SNAKE_CASE    )

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

         __UpperCamelCase	:	List[Any]		    =  processor(text=_SCREAMING_SNAKE_CASE     ,			images=_SCREAMING_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(_SCREAMING_SNAKE_CASE    ):
             processor()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  self.get_image_processor()
         __UpperCamelCase	:	int		    =  self.get_tokenizer()

         __UpperCamelCase	:	Union[str, Any]		    =  ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,			image_processor=_SCREAMING_SNAKE_CASE    )

         __UpperCamelCase	:	int		    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __UpperCamelCase	:	Union[str, Any]		    =  processor.batch_decode(_SCREAMING_SNAKE_CASE    )
         __UpperCamelCase	:	List[str]		    =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

         self.assertListEqual(_SCREAMING_SNAKE_CASE     ,			_SCREAMING_SNAKE_CASE    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  self.get_image_processor()
         __UpperCamelCase	:	str		    =  self.get_tokenizer()

         __UpperCamelCase	:	str		    =  ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,			image_processor=_SCREAMING_SNAKE_CASE    )

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

         __UpperCamelCase	:	Optional[int]		    =  processor(text=_SCREAMING_SNAKE_CASE     ,			images=_SCREAMING_SNAKE_CASE    )

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




'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a=  logging.get_logger(__name__)


a=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a=  {
    '''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'''
    },
}

a=  {'''facebook/blenderbot-3B''': 1_2_8}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE__		   =						BlenderbotTokenizer







     def __init__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase="replace"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<mask>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			**_lowerCamelCase     ,			):
         super().__init__(
             _lowerCamelCase     ,			_lowerCamelCase     ,			tokenizer_file=_lowerCamelCase     ,			errors=_lowerCamelCase     ,			bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			sep_token=_lowerCamelCase     ,			cls_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			mask_token=_lowerCamelCase     ,			add_prefix_space=_lowerCamelCase     ,			trim_offsets=_lowerCamelCase     ,			**_lowerCamelCase     ,			)

         __UpperCamelCase	:	str		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
             __UpperCamelCase	:	Any		    =  getattr(_lowerCamelCase     ,			pre_tok_state.pop('type'    )    )
             __UpperCamelCase	:	Dict		    =  add_prefix_space
             __UpperCamelCase	:	Optional[Any]		    =  pre_tok_class(**_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  add_prefix_space

         __UpperCamelCase	:	Optional[int]		    =  'post_processor'
         __UpperCamelCase	:	Tuple		    =  getattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         if tokenizer_component_instance:
             __UpperCamelCase	:	str		    =  json.loads(tokenizer_component_instance.__getstate__()    )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __UpperCamelCase	:	List[Any]		    =  tuple(state['sep']    )
             if "cls" in state:
                 __UpperCamelCase	:	str		    =  tuple(state['cls']    )

             __UpperCamelCase	:	Tuple		    =  False

             if state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
                 __UpperCamelCase	:	Dict		    =  add_prefix_space
                 __UpperCamelCase	:	str		    =  True

             if state.get('trim_offsets'     ,			_lowerCamelCase    ) != trim_offsets:
                 __UpperCamelCase	:	int		    =  trim_offsets
                 __UpperCamelCase	:	Any		    =  True

             if changes_to_apply:
                 __UpperCamelCase	:	Dict		    =  getattr(_lowerCamelCase     ,			state.pop('type'    )    )
                 __UpperCamelCase	:	Any		    =  component_class(**_lowerCamelCase    )
                 setattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def lowerCAmelCase     (		self    ):
         if self._mask_token is None:
             if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.'    )
             return None
         return str(self._mask_token    )







     @mask_token.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  AddedToken(_lowerCamelCase     ,			lstrip=_lowerCamelCase     ,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else value
         __UpperCamelCase	:	Optional[Any]		    =  value







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  self._tokenizer.model.save(_lowerCamelCase     ,			name=_lowerCamelCase    )
         return tuple(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Union[str, Any]		    =  [self.sep_token_id]
         __UpperCamelCase	:	List[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]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         return token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         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	:	Union[str, Any]		    =  '  '.join(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.encode(_lowerCamelCase    )
         if len(_lowerCamelCase    ) > self.model_max_length:
             __UpperCamelCase	:	Tuple		    =  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



