






import json
from typing import 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_mvp import MvpTokenizer


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)


lowerCamelCase			:					Union[str, Any]         =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase			:					Union[str, Any]         =	{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase			:					Any         =	{
    '''RUCAIBox/mvp''': 1024,
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							VOCAB_FILES_NAMES
      snake_case_  =							PRETRAINED_VOCAB_FILES_MAP
      snake_case_  =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_  =							["""input_ids""", """attention_mask"""]
      snake_case_  =							MvpTokenizer


      def __init__(	self					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase="replace"					,						__UpperCamelCase="<s>"					,						__UpperCamelCase="</s>"					,						__UpperCamelCase="</s>"					,						__UpperCamelCase="<s>"					,						__UpperCamelCase="<unk>"					,						__UpperCamelCase="<pad>"					,						__UpperCamelCase="<mask>"					,						__UpperCamelCase=False					,						__UpperCamelCase=True					,						**__UpperCamelCase					,						)->	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								=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
           if pre_tok_state.get("add_prefix_space"					,						__UpperCamelCase	) != add_prefix_space:
                __lowerCAmelCase								=					getattr(__UpperCamelCase					,						pre_tok_state.pop("type"	)	)
                __lowerCAmelCase								=					add_prefix_space
                __lowerCAmelCase								=					pre_tok_class(**__UpperCamelCase	)

           __lowerCAmelCase								=					add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           __lowerCAmelCase								=					"post_processor"
           __lowerCAmelCase								=					getattr(self.backend_tokenizer					,						__UpperCamelCase					,						__UpperCamelCase	)
           if tokenizer_component_instance:
                __lowerCAmelCase								=					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								=					tuple(state["sep"]	)
                if "cls" in state:
                     __lowerCAmelCase								=					tuple(state["cls"]	)

                __lowerCAmelCase								=					False

                if state.get("add_prefix_space"					,						__UpperCamelCase	) != add_prefix_space:
                     __lowerCAmelCase								=					add_prefix_space
                     __lowerCAmelCase								=					True

                if state.get("trim_offsets"					,						__UpperCamelCase	) != trim_offsets:
                     __lowerCAmelCase								=					trim_offsets
                     __lowerCAmelCase								=					True

                if changes_to_apply:
                     __lowerCAmelCase								=					getattr(__UpperCamelCase					,						state.pop("type"	)	)
                     __lowerCAmelCase								=					component_class(**__UpperCamelCase	)
                     setattr(self.backend_tokenizer					,						__UpperCamelCase					,						__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	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  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Dict:
           __lowerCAmelCase								=					AddedToken(__UpperCamelCase					,						lstrip=__UpperCamelCase					,						rstrip=__UpperCamelCase	) if isinstance(__UpperCamelCase					,						__UpperCamelCase	) else value
           __lowerCAmelCase								=					value


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	BatchEncoding:
           __lowerCAmelCase								=					kwargs.get("is_split_into_words"					,						__UpperCamelCase	)

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    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  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	BatchEncoding:
           __lowerCAmelCase								=					kwargs.get("is_split_into_words"					,						__UpperCamelCase	)

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    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  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None	)->	Tuple[str]:
           __lowerCAmelCase								=					self._tokenizer.model.save(__UpperCamelCase					,						name=__UpperCamelCase	)
           return tuple(__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase=None	)->	Optional[Any]:
           __lowerCAmelCase								=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None	)->	List[int]:
           __lowerCAmelCase								=					[self.sep_token_id]
           __lowerCAmelCase								=					[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]














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 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					tempfile.mkdtemp()

           # fmt: off
           __lowerCAmelCase								=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
           # fmt: on
           __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": 1_8, "width": 1_8},
               "do_normalize": True,
               "image_mean": [0.5, 0.5, 0.5],
               "image_std": [0.5, 0.5, 0.5],
           }
           __lowerCAmelCase								=					os.path.join(self.tmpdirname					,						__UpperCamelCase	)
           with open(self.image_processor_file					,						"w"					,						encoding="utf-8"	) as fp:
                json.dump(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Optional[Any]:
           return BertTokenizer.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Any:
           return ViTImageProcessor.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           shutil.rmtree(self.tmpdirname	)


      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					[np.random.randint(2_5_5					,						size=(3, 3_0, 4_0_0)					,						dtype=np.uinta	)]

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

           return image_inputs


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					self.get_tokenizer()
           __lowerCAmelCase								=					self.get_image_processor()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           processor.save_pretrained(self.tmpdirname	)
           __lowerCAmelCase								=					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  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(
               tokenizer=self.get_tokenizer()					,						image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

           __lowerCAmelCase								=					self.get_tokenizer(bos_token="(BOS)"					,						eos_token="(EOS)"	)
           __lowerCAmelCase								=					self.get_image_processor(do_normalize=__UpperCamelCase					,						padding_value=1.0	)

           __lowerCAmelCase								=					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  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					self.prepare_image_inputs()

           __lowerCAmelCase								=					image_processor(__UpperCamelCase					,						return_tensors="np"	)
           __lowerCAmelCase								=					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  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					"lower newer"

           __lowerCAmelCase								=					processor(text=__UpperCamelCase	)

           __lowerCAmelCase								=					tokenizer(__UpperCamelCase	)

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


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					"lower newer"
           __lowerCAmelCase								=					self.prepare_image_inputs()

           __lowerCAmelCase								=					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  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

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

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

           self.assertListEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					"lower newer"
           __lowerCAmelCase								=					self.prepare_image_inputs()

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

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






