



"""simple docstring"""

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

import sentencepiece as spm

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


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

A_	:						Tuple				=			logging.get_logger(__name__)

A_	:						Tuple				=			{'vocab_file': 'tokenizer.model'}

A_	:						str				=			{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
A_	:						int				=			{
    'hf-internal-testing/llama-tokenizer': 2048,
}
A_	:						Dict				=			'▁'

A_	:						List[str]				=			'[INST]', '[/INST]'
A_	:						str				=			'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
A_	:						Dict				=			'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE  :		Any     	=   PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE  :		Dict     	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE  :		Tuple     	=   ['''input_ids''', '''attention_mask''']





	def __init__(			self							:					str    ,					_SCREAMING_SNAKE_CASE							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[Any]="<unk>"    ,					_SCREAMING_SNAKE_CASE							:					int="<s>"    ,					_SCREAMING_SNAKE_CASE							:					List[str]="</s>"    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=None    ,					_SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=True    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=False    ,					_SCREAMING_SNAKE_CASE							:					Dict=False    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=True    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    {} if sp_model_kwargs is None else sp_model_kwargs
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    AddedToken(_SCREAMING_SNAKE_CASE    ,					lstrip=_SCREAMING_SNAKE_CASE    ,					rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) else bos_token
					SCREAMING_SNAKE_CASE	:       str 							=    AddedToken(_SCREAMING_SNAKE_CASE    ,					lstrip=_SCREAMING_SNAKE_CASE    ,					rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) else eos_token
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    AddedToken(_SCREAMING_SNAKE_CASE    ,					lstrip=_SCREAMING_SNAKE_CASE    ,					rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) else unk_token
					SCREAMING_SNAKE_CASE	:       List[str] 							=    AddedToken(_SCREAMING_SNAKE_CASE    ,					lstrip=_SCREAMING_SNAKE_CASE    ,					rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) else pad_token
					super().__init__(
					    bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					add_bos_token=_SCREAMING_SNAKE_CASE    ,					add_eos_token=_SCREAMING_SNAKE_CASE    ,					sp_model_kwargs=self.sp_model_kwargs    ,					clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE    ,					legacy=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)
					if legacy:
									logger.warning_once(
									    f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
									    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    legacy
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    vocab_file
					SCREAMING_SNAKE_CASE	:       Tuple 							=    add_bos_token
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    add_eos_token
					SCREAMING_SNAKE_CASE	:       Tuple 							=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.Load(_SCREAMING_SNAKE_CASE						)





	def __getstate__(			self							:					Tuple						)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.__dict__.copy()
					SCREAMING_SNAKE_CASE	:       Dict 							=    None
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.sp_model.serialized_model_proto()
					return state





	def __setstate__(			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    d
					SCREAMING_SNAKE_CASE	:       List[str] 							=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)





	@property
	def  _lowerCAmelCase    (			self							:					Any						)	->  List[str]:





					"""simple docstring"""


					return self.sp_model.get_piece_size()





	def  _lowerCAmelCase    (			self							:					int						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
					vocab.update(self.added_tokens_encoder						)
					return vocab





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					int    ,					**_SCREAMING_SNAKE_CASE							:					Dict						)	->  List[str]:





					"""simple docstring"""


					if not self.legacy:
									SCREAMING_SNAKE_CASE	:       Dict 							=    SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE    ,					' '						)
					return super().tokenize(_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  Tuple:





					"""simple docstring"""


					if not self.legacy:
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    text.startswith(_SCREAMING_SNAKE_CASE						)
									if is_first:
													SCREAMING_SNAKE_CASE	:       Dict 							=    text[1:]

					SCREAMING_SNAKE_CASE	:       Dict 							=    self.sp_model.encode(_SCREAMING_SNAKE_CASE    ,					out_type=_SCREAMING_SNAKE_CASE						)

					if not self.legacy and not is_first and not text.startswith(' '						) and tokens[0].startswith(_SCREAMING_SNAKE_CASE						):
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
					return tokens





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					List[str]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE						)
					return token





	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    []
					SCREAMING_SNAKE_CASE	:       Dict 							=    ''
					SCREAMING_SNAKE_CASE	:       Any 							=    False
					for i, token in enumerate(_SCREAMING_SNAKE_CASE						):
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special and i != 0:
																	out_string += " "
													out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						) + token
													SCREAMING_SNAKE_CASE	:       str 							=    True
													SCREAMING_SNAKE_CASE	:       Any 							=    []
									else:
													current_sub_tokens.append(_SCREAMING_SNAKE_CASE						)
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    False
					out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						)
					return out_string





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[str] = None						)	->  Tuple[str]:





					"""simple docstring"""


					if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
									return
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    os.path.join(
					    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

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

					return (out_vocab_file,)





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					str=None						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    [self.bos_token_id] if self.add_bos_token else []
					SCREAMING_SNAKE_CASE	:       List[str] 							=    [self.eos_token_id] if self.add_eos_token else []

					SCREAMING_SNAKE_CASE	:       Dict 							=    bos_token_id + token_ids_a + eos_token_id

					if token_ids_a is not None:
									SCREAMING_SNAKE_CASE	:       Dict 							=    output + bos_token_id + token_ids_a + eos_token_id

					return output





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





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       int 							=    [1] if self.add_bos_token else []
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    [1] if self.add_eos_token else []

					if token_ids_a is None:
									return bos_token_id + ([0] * len(_SCREAMING_SNAKE_CASE						)) + eos_token_id
					return (
					    bos_token_id
					    + ([0] * len(_SCREAMING_SNAKE_CASE						))
					    + eos_token_id
					    + bos_token_id
					    + ([0] * len(_SCREAMING_SNAKE_CASE						))
					    + eos_token_id
					)





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					List[int]    ,					_SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None						)	->  List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [self.bos_token_id] if self.add_bos_token else []
					SCREAMING_SNAKE_CASE	:       int 							=    [self.eos_token_id] if self.add_eos_token else []

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [0] * len(bos_token_id + token_ids_a + eos_token_id						)

					if token_ids_a is not None:
									output += [1] * len(bos_token_id + token_ids_a + eos_token_id						)

					return output







	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					"Conversation"						)	->  List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    list(conversation.iter_texts()						)
					if not all([is_user for is_user, msg in dialogue[::2]]						) or not all(
					    [not is_user for is_user, msg in dialogue[1::2]]						):
									raise ValueError(
									    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'						)

					SCREAMING_SNAKE_CASE	:       List[int] 							=    []
					if len(conversation.past_user_inputs						) > 0:
									if not conversation.past_user_inputs[0].startswith(_SCREAMING_SNAKE_CASE						) or E_SYS not in conversation.past_user_inputs[0]:
													SCREAMING_SNAKE_CASE	:       str 							=    (
													    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
													)
					elif not dialogue[0][1].startswith(_SCREAMING_SNAKE_CASE						) or E_SYS not in dialogue[0][1]:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

					dialog_tokens += sum(
					    [
					        [self.bos_token_id]
					        + self.encode(
					            f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """    ,					add_special_tokens=_SCREAMING_SNAKE_CASE						)
					        + [self.eos_token_id]
					        for prompt, answer in zip(dialogue[::2]    ,					dialogue[1::2]						)
					    ]    ,					[]    ,					)
					if not (dialogue[-1][0]):
									raise ValueError(f"""Last message must be from user, got {dialogue[-1]["role"]}"""						)
					dialog_tokens += [self.bos_token_id] + self.encode(
					    f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""    ,					add_special_tokens=_SCREAMING_SNAKE_CASE						)
					return dialog_tokens






"""simple docstring"""

import json
import os
import random
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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import FlavaImageProcessor, FlavaProcessor
							from transformers.models.flava.image_processing_flava import (
							    FLAVA_CODEBOOK_MEAN,
							    FLAVA_CODEBOOK_STD,
							    FLAVA_IMAGE_MEAN,
							    FLAVA_IMAGE_STD,
							)
@require_vision
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					int						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    tempfile.mkdtemp()

					# fmt: off
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
					# fmt: on
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES['vocab_file']						)

					with open(self.vocab_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									fp.write(''.join([x + '\n' for x in vocab_tokens]						)						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    {
					    'image_mean': FLAVA_IMAGE_MEAN,
					    'image_std': FLAVA_IMAGE_STD,
					    'do_normalize': True,
					    'do_resize': True,
					    'size': 224,
					    'do_center_crop': True,
					    'crop_size': 224,
					    'input_size_patches': 14,
					    'total_mask_patches': 75,
					    'mask_group_max_patches': None,
					    'mask_group_min_patches': 16,
					    'mask_group_min_aspect_ratio': 0.3,
					    'mask_group_max_aspect_ratio': None,
					    'codebook_do_resize': True,
					    'codebook_size': 112,
					    'codebook_do_center_crop': True,
					    'codebook_crop_size': 112,
					    'codebook_do_map_pixels': True,
					    'codebook_do_normalize': True,
					    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
					    'codebook_image_std': FLAVA_CODEBOOK_STD,
					}

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    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							:					Any    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  str:





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					int    ,					**_SCREAMING_SNAKE_CASE							:					Any						)	->  Union[str, Any]:





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					**_SCREAMING_SNAKE_CASE							:					Dict						)	->  str:





					"""simple docstring"""


					return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  str:





					"""simple docstring"""


					shutil.rmtree(self.tmpdirname						)





	def  _lowerCAmelCase    (			self							:					int						)	->  Optional[Any]:





					"""simple docstring"""


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

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

					return image_inputs





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    self.get_tokenizer()
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.get_rust_tokenizer()
					SCREAMING_SNAKE_CASE	:       str 							=    self.get_image_processor()

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_slow.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor.from_pretrained(self.tmpdirname    ,					use_fast=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_fast.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    FlavaProcessor.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							:					Optional[Any]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    FlavaProcessor(tokenizer=self.get_tokenizer()    ,					image_processor=self.get_image_processor()						)
					processor.save_pretrained(self.tmpdirname						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_tokenizer(bos_token='(BOS)'    ,					eos_token='(EOS)'						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    FlavaProcessor.from_pretrained(
					    self.tmpdirname    ,					bos_token='(BOS)'    ,					eos_token='(EOS)'    ,					do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

					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							:					List[Any]						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Any 							=    image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					SCREAMING_SNAKE_CASE	:       str 							=    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						)

					# With rest of the args
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       str 							=    image_processor(
					    _SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    processor(
					    images=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_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							:					Any						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    'lower newer'

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    tokenizer(_SCREAMING_SNAKE_CASE						)

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





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

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

					# add extra args
					SCREAMING_SNAKE_CASE	:       Any 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(
					    list(inputs.keys()						)    ,					[
					        'input_ids',
					        'token_type_ids',
					        'attention_mask',
					        'pixel_values',
					        'codebook_pixel_values',
					        'bool_masked_pos',
					    ]    ,					)

					# test if it raises when no input is passed
					with pytest.raises(_SCREAMING_SNAKE_CASE						):
									processor()





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Tuple 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

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

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    processor.batch_decode(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					int						)	->  Any:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Any 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Dict 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

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





