





'''simple docstring'''

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      ):





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							tempfile.mkdtemp()

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

      __SCREAMING_SNAKE_CASE   				=							InstructBlipProcessor(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )

      processor.save_pretrained(self.tmpdirname     )





   def   UpperCAmelCase__  (       self					:      Optional[int]					,**lowerCamelCase					:      Any     ):

      '''simple docstring'''

      return AutoProcessor.from_pretrained(self.tmpdirname					,**lowerCamelCase     ).tokenizer





   def   UpperCAmelCase__  (       self					:      Dict					,**lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      return AutoProcessor.from_pretrained(self.tmpdirname					,**lowerCamelCase     ).image_processor





   def   UpperCAmelCase__  (       self					:      Optional[int]					,**lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      return AutoProcessor.from_pretrained(self.tmpdirname					,**lowerCamelCase     ).qformer_tokenizer





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      shutil.rmtree(self.tmpdirname     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

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

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

      return image_inputs





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							InstructBlipProcessor(
          tokenizer=self.get_tokenizer()					,image_processor=self.get_image_processor()					,qformer_tokenizer=self.get_qformer_tokenizer()					,)
      processor.save_pretrained(self.tmpdirname     )

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

      __SCREAMING_SNAKE_CASE   				=							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					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_image_processor()
      __SCREAMING_SNAKE_CASE   				=							self.get_tokenizer()
      __SCREAMING_SNAKE_CASE   				=							self.get_qformer_tokenizer()

      __SCREAMING_SNAKE_CASE   				=							InstructBlipProcessor(
          tokenizer=lowerCamelCase					,image_processor=lowerCamelCase					,qformer_tokenizer=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE   				=							image_processor(lowerCamelCase					,return_tensors="""np"""     )
      __SCREAMING_SNAKE_CASE   				=							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					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_image_processor()
      __SCREAMING_SNAKE_CASE   				=							self.get_tokenizer()
      __SCREAMING_SNAKE_CASE   				=							self.get_qformer_tokenizer()

      __SCREAMING_SNAKE_CASE   				=							InstructBlipProcessor(
          tokenizer=lowerCamelCase					,image_processor=lowerCamelCase					,qformer_tokenizer=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							"""lower newer"""

      __SCREAMING_SNAKE_CASE   				=							processor(text=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							tokenizer(lowerCamelCase					,return_token_type_ids=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							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					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_image_processor()
      __SCREAMING_SNAKE_CASE   				=							self.get_tokenizer()
      __SCREAMING_SNAKE_CASE   				=							self.get_qformer_tokenizer()

      __SCREAMING_SNAKE_CASE   				=							InstructBlipProcessor(
          tokenizer=lowerCamelCase					,image_processor=lowerCamelCase					,qformer_tokenizer=lowerCamelCase     )

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

      __SCREAMING_SNAKE_CASE   				=							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					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_image_processor()
      __SCREAMING_SNAKE_CASE   				=							self.get_tokenizer()
      __SCREAMING_SNAKE_CASE   				=							self.get_qformer_tokenizer()

      __SCREAMING_SNAKE_CASE   				=							InstructBlipProcessor(
          tokenizer=lowerCamelCase					,image_processor=lowerCamelCase					,qformer_tokenizer=lowerCamelCase     )

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

      __SCREAMING_SNAKE_CASE   				=							processor.batch_decode(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.batch_decode(lowerCamelCase     )

      self.assertListEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_image_processor()
      __SCREAMING_SNAKE_CASE   				=							self.get_tokenizer()
      __SCREAMING_SNAKE_CASE   				=							self.get_qformer_tokenizer()

      __SCREAMING_SNAKE_CASE   				=							InstructBlipProcessor(
          tokenizer=lowerCamelCase					,image_processor=lowerCamelCase					,qformer_tokenizer=lowerCamelCase     )

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

      __SCREAMING_SNAKE_CASE   				=							processor(text=lowerCamelCase					,images=lowerCamelCase     )

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













"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_fnet import FNetTokenizer
else:
				_lowerCAmelCase            =	None

_lowerCAmelCase            =	logging.get_logger(__name__)
_lowerCAmelCase            =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase            =	{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

_lowerCAmelCase            =	'▁'


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_ids""", """token_type_ids"""]
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		FNetTokenizer






						def __init__(		self  :Optional[Any]			, __magic_name__  :Union[str, Any]=None			, __magic_name__  :Any=None			, __magic_name__  :List[str]=False			, __magic_name__  :Any=True			, __magic_name__  :Any=True			, __magic_name__  :Union[str, Any]="<unk>"			, __magic_name__  :List[Any]="[SEP]"			, __magic_name__  :Union[str, Any]="<pad>"			, __magic_name__  :Tuple="[CLS]"			, __magic_name__  :Optional[int]="[MASK]"			, **__magic_name__  :Optional[int]			, )      ->Tuple:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase    :      Union[str, Any]       = (
										    AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__			, normalized=__magic_name__				)
										    if isinstance(__magic_name__			, __magic_name__				)
										    else mask_token
										)

										super().__init__(
										    __magic_name__			, tokenizer_file=__magic_name__			, do_lower_case=__magic_name__			, remove_space=__magic_name__			, keep_accents=__magic_name__			, unk_token=__magic_name__			, sep_token=__magic_name__			, pad_token=__magic_name__			, cls_token=__magic_name__			, mask_token=__magic_name__			, **__magic_name__			, )

										lowercase    :      Tuple       = do_lower_case
										lowercase    :      Tuple       = remove_space
										lowercase    :      List[str]       = keep_accents
										lowercase    :      Union[str, Any]       = vocab_file
										lowercase    :      int       = False if not self.vocab_file else True






						def      __snake_case      (		self  :Any			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      List[str]       = [self.sep_token_id]
										lowercase    :      Any       = [self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






						def      __snake_case      (		self  :int			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Tuple       = [self.sep_token_id]
										lowercase    :      Union[str, Any]       = [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				) * [0] + len(token_ids_a + sep				) * [1]






						def      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				):
														copyfile(self.vocab_file			, __magic_name__				)

										return (out_vocab_file,)
