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_bart import BartTokenizer


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE										=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_SCREAMING_SNAKE_CASE										=						{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE										=						{
    'facebook/bart-base': 1_024,
    'facebook/bart-large': 1_024,
    'facebook/bart-large-mnli': 1_024,
    'facebook/bart-large-cnn': 1_024,
    'facebook/bart-large-xsum': 1_024,
    'yjernite/bart_eli5': 1_024,
}



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		VOCAB_FILES_NAMES
 lowerCamelCase	:Optional[Any]    			=		PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase	:Optional[Any]    			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase	:List[str]    			=		['''input_ids''', '''attention_mask''']
 lowerCamelCase	:List[str]    			=		BartTokenizer



 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_		,				)  ->					int:
        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_		,				)

        _A        =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("""add_prefix_space"""		,				lowerCAmelCase_							) != add_prefix_space:
               _A        =  getattr(lowerCAmelCase_		,				pre_tok_state.pop("""type"""							)							)
               _A        =  add_prefix_space
               _A        =  pre_tok_class(**lowerCAmelCase_							)

        _A        =  add_prefix_space

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

               _A        =  False

               if state.get("""add_prefix_space"""		,				lowerCAmelCase_							) != add_prefix_space:
                      _A        =  add_prefix_space
                      _A        =  True

               if state.get("""trim_offsets"""		,				lowerCAmelCase_							) != trim_offsets:
                      _A        =  trim_offsets
                      _A        =  True

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



 @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		,				lowerCAmelCase_							)  ->					Optional[int]:
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else value
        _A        =  value



 def      UpperCAmelCase							(					self		,				*lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					BatchEncoding:
        _A        =  kwargs.get("""is_split_into_words"""		,				lowerCAmelCase_							)

        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(*lowerCAmelCase_		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				*lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					BatchEncoding:
        _A        =  kwargs.get("""is_split_into_words"""		,				lowerCAmelCase_							)

        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(*lowerCAmelCase_		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					Tuple[str]:
        _A        =  self._tokenizer.model.save(lowerCAmelCase_		,				name=lowerCAmelCase_							)
        return tuple(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=None							)  ->					List[Any]:
        _A        =  [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		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:
        _A        =  [self.sep_token_id]
        _A        =  [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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )



@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

        _A        =  BlipImageProcessor()
        _A        =  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
        _A        =  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).tokenizer



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Dict:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).image_processor



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



 def      UpperCAmelCase							(					self							)  ->					int:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  [np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta							)]

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

        return image_inputs



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname							)

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

        _A        =  InstructBlipProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="""(BOS)"""		,				eos_token="""(EOS)"""		,				do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

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

        self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,				lowerCAmelCase_							)
        self.assertIsInstance(processor.qformer_tokenizer		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(images=lowerCAmelCase_		,				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:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  qformer_tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

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

        for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor["""qformer_""" + key]							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

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

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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

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



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

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

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

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

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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




