'''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   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					tempfile.mkdtemp()

    # fmt: off
    __UpperCAmelCase         =					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
    # fmt: on
    __UpperCAmelCase         =					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]	)	)

    __UpperCAmelCase         =					{
        '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,
    }

    __UpperCAmelCase         =					os.path.join(self.tmpdirname			,a	)
    with open(self.image_processor_file			,'w'			,encoding='utf-8'	) as fp:
     json.dump(a			,a	)





   def 			snake_case	(							self:    str			,**a:    Tuple	):
    return BertTokenizer.from_pretrained(self.tmpdirname			,**a	)





   def 			snake_case	(							self:    Any			,**a:    Union[str, Any]	):
    return BertTokenizerFast.from_pretrained(self.tmpdirname			,**a	)





   def 			snake_case	(							self:    Dict			,**a:    Union[str, Any]	):
    return FlavaImageProcessor.from_pretrained(self.tmpdirname			,**a	)





   def 			snake_case	(							self:    List[str]	):
    shutil.rmtree(self.tmpdirname	)





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

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

    return image_inputs





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					self.get_tokenizer()
    __UpperCAmelCase         =					self.get_rust_tokenizer()
    __UpperCAmelCase         =					self.get_image_processor()

    __UpperCAmelCase         =					FlavaProcessor(tokenizer=a			,image_processor=a	)
    processor_slow.save_pretrained(self.tmpdirname	)
    __UpperCAmelCase         =					FlavaProcessor.from_pretrained(self.tmpdirname			,use_fast=a	)

    __UpperCAmelCase         =					FlavaProcessor(tokenizer=a			,image_processor=a	)
    processor_fast.save_pretrained(self.tmpdirname	)
    __UpperCAmelCase         =					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			,a	)
    self.assertIsInstance(processor_fast.tokenizer			,a	)

    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			,a	)
    self.assertIsInstance(processor_fast.image_processor			,a	)





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					FlavaProcessor(tokenizer=self.get_tokenizer()			,image_processor=self.get_image_processor()	)
    processor.save_pretrained(self.tmpdirname	)

    __UpperCAmelCase         =					self.get_tokenizer(bos_token='(BOS)'			,eos_token='(EOS)'	)
    __UpperCAmelCase         =					self.get_image_processor(do_normalize=a			,padding_value=1.0	)

    __UpperCAmelCase         =					FlavaProcessor.from_pretrained(
        self.tmpdirname			,bos_token='(BOS)'			,eos_token='(EOS)'			,do_normalize=a			,padding_value=1.0	)

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

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





   def 			snake_case	(							self:    Union[str, Any]	):
    __UpperCAmelCase         =					self.get_image_processor()
    __UpperCAmelCase         =					self.get_tokenizer()

    __UpperCAmelCase         =					FlavaProcessor(tokenizer=a			,image_processor=a	)

    __UpperCAmelCase         =					self.prepare_image_inputs()

    __UpperCAmelCase         =					image_processor(a			,return_tensors='np'	)
    __UpperCAmelCase         =					processor(images=a			,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(1234	)
    __UpperCAmelCase         =					image_processor(
        a			,return_image_mask=a			,return_codebook_pixels=a			,return_tensors='np'	)
    random.seed(1234	)
    __UpperCAmelCase         =					processor(
        images=a			,return_image_mask=a			,return_codebook_pixels=a			,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 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					self.get_image_processor()
    __UpperCAmelCase         =					self.get_tokenizer()

    __UpperCAmelCase         =					FlavaProcessor(tokenizer=a			,image_processor=a	)

    __UpperCAmelCase         =					'lower newer'

    __UpperCAmelCase         =					processor(text=a	)

    __UpperCAmelCase         =					tokenizer(a	)

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





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					self.get_image_processor()
    __UpperCAmelCase         =					self.get_tokenizer()

    __UpperCAmelCase         =					FlavaProcessor(tokenizer=a			,image_processor=a	)

    __UpperCAmelCase         =					'lower newer'
    __UpperCAmelCase         =					self.prepare_image_inputs()

    __UpperCAmelCase         =					processor(text=a			,images=a	)

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

    # add extra args
    __UpperCAmelCase         =					processor(text=a			,images=a			,return_codebook_pixels=a			,return_image_mask=a	)

    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(a	):
     processor()





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					self.get_image_processor()
    __UpperCAmelCase         =					self.get_tokenizer()

    __UpperCAmelCase         =					FlavaProcessor(tokenizer=a			,image_processor=a	)

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

    __UpperCAmelCase         =					processor.batch_decode(a	)
    __UpperCAmelCase         =					tokenizer.batch_decode(a	)

    self.assertListEqual(a			,a	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					self.get_image_processor()
    __UpperCAmelCase         =					self.get_tokenizer()

    __UpperCAmelCase         =					FlavaProcessor(tokenizer=a			,image_processor=a	)

    __UpperCAmelCase         =					'lower newer'
    __UpperCAmelCase         =					self.prepare_image_inputs()

    __UpperCAmelCase         =					processor(text=a			,images=a	)

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




'''simple docstring'''




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_longformer import LongformerTokenizer


_UpperCamelCase				:		int       			=		logging.get_logger(__name__)

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

_UpperCamelCase				:		int       			=		{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase				:		List[str]       			=		{
    'allenai/longformer-base-4096': 40_96,
    'allenai/longformer-large-4096': 40_96,
    'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
    'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
    'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  VOCAB_FILES_NAMES
   __lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
   __lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCamelCase    =  ['''input_ids''', '''attention_mask''']
   __lowerCamelCase    =  LongformerTokenizer





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

    __UpperCAmelCase         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
    if pre_tok_state.get('add_prefix_space'			,a	) != add_prefix_space:
     __UpperCAmelCase         =					getattr(a			,pre_tok_state.pop('type'	)	)
     __UpperCAmelCase         =					add_prefix_space
     __UpperCAmelCase         =					pre_tok_class(**a	)

    __UpperCAmelCase         =					add_prefix_space

    __UpperCAmelCase         =					'post_processor'
    __UpperCAmelCase         =					getattr(self.backend_tokenizer			,a			,a	)
    if tokenizer_component_instance:
     __UpperCAmelCase         =					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         =					tuple(state['sep']	)
     if "cls" in state:
      __UpperCAmelCase         =					tuple(state['cls']	)

     __UpperCAmelCase         =					False

     if state.get('add_prefix_space'			,a	) != add_prefix_space:
      __UpperCAmelCase         =					add_prefix_space
      __UpperCAmelCase         =					True

     if state.get('trim_offsets'			,a	) != trim_offsets:
      __UpperCAmelCase         =					trim_offsets
      __UpperCAmelCase         =					True

     if changes_to_apply:
      __UpperCAmelCase         =					getattr(a			,state.pop('type'	)	)
      __UpperCAmelCase         =					component_class(**a	)
      setattr(self.backend_tokenizer			,a			,a	)





   @property
   def 			snake_case	(							self:    List[Any]	):
    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 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase         =					AddedToken(a			,lstrip=a			,rstrip=a	) if isinstance(a			,a	) else value
    __UpperCAmelCase         =					value





   def 			snake_case	(							self:    Optional[int]			,*a:    Dict			,**a:    Dict	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)
    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(*a			,**a	)





   def 			snake_case	(							self:    str			,*a:    Dict			,**a:    str	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)

    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(*a			,**a	)





   def 			snake_case	(							self:    List[str]			,a:    str			,a:    Optional[str] = None	):
    __UpperCAmelCase         =					self._tokenizer.model.save(a			,name=a	)
    return tuple(a	)





   def 			snake_case	(							self:    Tuple			,a:    Dict			,a:    Tuple=None	):
    __UpperCAmelCase         =					[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 			snake_case	(							self:    int			,a:    List[int]			,a:    Optional[List[int]] = None	):
    __UpperCAmelCase         =					[self.sep_token_id]
    __UpperCAmelCase         =					[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]



