






'''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	:			List[Any]         =logging.get_logger(__name__)


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

_A	:			List[Any]         ={
    '''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	:			List[Any]         ={'''facebook/blenderbot-3B''': 128}

class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       PRETRAINED_VOCAB_FILES_MAP
							a				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       ["""input_ids""", """attention_mask"""]
							a				      =       BlenderbotTokenizer




							def __init__(							self:				Union[str, Any]			,							UpperCamelCase__:				Union[str, Any]=None			,							UpperCamelCase__:				List[str]=None			,							UpperCamelCase__:				int=None			,							UpperCamelCase__:				Dict="replace"			,							UpperCamelCase__:				Any="<s>"			,							UpperCamelCase__:				Dict="</s>"			,							UpperCamelCase__:				Any="</s>"			,							UpperCamelCase__:				Union[str, Any]="<s>"			,							UpperCamelCase__:				Tuple="<unk>"			,							UpperCamelCase__:				Union[str, Any]="<pad>"			,							UpperCamelCase__:				Optional[Any]="<mask>"			,							UpperCamelCase__:				Tuple=False			,							UpperCamelCase__:				str=True			,							**UpperCamelCase__:				Optional[Any]			,							):
														super().__init__(
														    UpperCamelCase__			,							UpperCamelCase__			,							tokenizer_file=UpperCamelCase__			,							errors=UpperCamelCase__			,							bos_token=UpperCamelCase__			,							eos_token=UpperCamelCase__			,							sep_token=UpperCamelCase__			,							cls_token=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							pad_token=UpperCamelCase__			,							mask_token=UpperCamelCase__			,							add_prefix_space=UpperCamelCase__			,							trim_offsets=UpperCamelCase__			,							**UpperCamelCase__			,							)

														lowerCamelCase__					:	Optional[int]         =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
														if pre_tok_state.get("""add_prefix_space"""			,							UpperCamelCase__     ) != add_prefix_space:
																					lowerCamelCase__					:	List[str]         =       getattr(UpperCamelCase__			,							pre_tok_state.pop("""type"""     )     )
																					lowerCamelCase__					:	Union[str, Any]         =       add_prefix_space
																					lowerCamelCase__					:	Optional[int]         =       pre_tok_class(**UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       add_prefix_space

														lowerCamelCase__					:	Tuple         =       """post_processor"""
														lowerCamelCase__					:	Tuple         =       getattr(self.backend_tokenizer			,							UpperCamelCase__			,							UpperCamelCase__     )
														if tokenizer_component_instance:
																					lowerCamelCase__					:	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:
																												lowerCamelCase__					:	str         =       tuple(state["""sep"""]     )
																					if "cls" in state:
																												lowerCamelCase__					:	Optional[Any]         =       tuple(state["""cls"""]     )

																					lowerCamelCase__					:	Optional[int]         =       False

																					if state.get("""add_prefix_space"""			,							UpperCamelCase__     ) != add_prefix_space:
																												lowerCamelCase__					:	Tuple         =       add_prefix_space
																												lowerCamelCase__					:	Optional[Any]         =       True

																					if state.get("""trim_offsets"""			,							UpperCamelCase__     ) != trim_offsets:
																												lowerCamelCase__					:	int         =       trim_offsets
																												lowerCamelCase__					:	int         =       True

																					if changes_to_apply:
																												lowerCamelCase__					:	List[Any]         =       getattr(UpperCamelCase__			,							state.pop("""type"""     )     )
																												lowerCamelCase__					:	Any         =       component_class(**UpperCamelCase__     )
																												setattr(self.backend_tokenizer			,							UpperCamelCase__			,							UpperCamelCase__     )




							@property
							# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 		lowerCamelCase_				(							self:				str     ):
														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:				Optional[Any]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       AddedToken(UpperCamelCase__			,							lstrip=UpperCamelCase__			,							rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else value
														lowerCamelCase__					:	int         =       value




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							*UpperCamelCase__:				Optional[Any]			,							**UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	List[str]         =       kwargs.get("""is_split_into_words"""			,							UpperCamelCase__     )
														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(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							*UpperCamelCase__:				List[Any]			,							**UpperCamelCase__:				int     ):
														lowerCamelCase__					:	List[str]         =       kwargs.get("""is_split_into_words"""			,							UpperCamelCase__     )

														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(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[str] = None     ):
														lowerCamelCase__					:	Dict         =       self._tokenizer.model.save(UpperCamelCase__			,							name=UpperCamelCase__     )
														return tuple(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				Optional[List[int]] = None     ):
														lowerCamelCase__					:	List[Any]         =       [self.sep_token_id]
														lowerCamelCase__					:	Optional[int]         =       [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:				Tuple			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				Optional[List[int]] = None     ):
														return token_ids_a + [self.eos_token_id]




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				"Conversation"     ):
														lowerCamelCase__					:	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(UpperCamelCase__     )

														lowerCamelCase__					:	str         =       """  """.join(UpperCamelCase__     )
														lowerCamelCase__					:	str         =       self.encode(UpperCamelCase__     )
														if len(UpperCamelCase__     ) > self.model_max_length:
																					lowerCamelCase__					:	List[Any]         =       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









'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       tempfile.mkdtemp()

														# fmt: off
														lowerCamelCase__					:	int         =       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
														# fmt: on
														lowerCamelCase__					:	Union[str, Any]         =       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__					:	Tuple         =       {
														    """do_resize""": True,
														    """size""": {"""height""": 18, """width""": 18},
														    """do_normalize""": True,
														    """image_mean""": [0.5, 0.5, 0.5],
														    """image_std""": [0.5, 0.5, 0.5],
														}
														lowerCamelCase__					:	Tuple         =       os.path.join(self.tmpdirname			,							UpperCamelCase__     )
														with open(self.image_processor_file			,							"""w"""			,							encoding="""utf-8"""     ) as fp:
																					json.dump(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							**UpperCamelCase__:				List[str]     ):
														return BertTokenizer.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int			,							**UpperCamelCase__:				Tuple     ):
														return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														shutil.rmtree(self.tmpdirname     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[Any]         =       [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta     )]

														lowerCamelCase__					:	Tuple         =       [Image.fromarray(np.moveaxis(UpperCamelCase__			,							0			,							-1     )     ) for x in image_inputs]

														return image_inputs




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_tokenizer()
														lowerCamelCase__					:	Dict         =       self.get_image_processor()

														lowerCamelCase__					:	Optional[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

														self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()     )
														self.assertIsInstance(processor.image_processor			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Dict         =       VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCamelCase__					:	int         =       self.get_tokenizer(bos_token="""(BOS)"""			,							eos_token="""(EOS)"""     )
														lowerCamelCase__					:	List[Any]         =       self.get_image_processor(do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														lowerCamelCase__					:	Tuple         =       VisionTextDualEncoderProcessor.from_pretrained(
														    self.tmpdirname			,							bos_token="""(BOS)"""			,							eos_token="""(EOS)"""			,							do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

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




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	Any         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       self.prepare_image_inputs()

														lowerCamelCase__					:	List[str]         =       image_processor(UpperCamelCase__			,							return_tensors="""np"""     )
														lowerCamelCase__					:	Optional[Any]         =       processor(images=UpperCamelCase__			,							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[int]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       """lower newer"""

														lowerCamelCase__					:	Union[str, Any]         =       processor(text=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       tokenizer(UpperCamelCase__     )

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




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       """lower newer"""
														lowerCamelCase__					:	Dict         =       self.prepare_image_inputs()

														lowerCamelCase__					:	Tuple         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

														self.assertListEqual(list(inputs.keys()     )			,							["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

														# test if it raises when no input is passed
														with self.assertRaises(UpperCamelCase__     ):
																					processor()




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCamelCase__					:	Union[str, Any]         =       processor.batch_decode(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       tokenizer.batch_decode(UpperCamelCase__     )

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       """lower newer"""
														lowerCamelCase__					:	str         =       self.prepare_image_inputs()

														lowerCamelCase__					:	int         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

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

