



'''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       lowercase_	(				self			:  Any     ):
      a						: Union[str, Any]					       =					tempfile.mkdtemp()

      a						: str					       =					BlipImageProcessor()
      a						: str					       =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'     )
      a						: Optional[Any]					       =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'     )

      a						: Dict					       =					InstructBlipProcessor(__snake_case						,      __snake_case						,      __snake_case     )

      processor.save_pretrained(self.tmpdirname     )







    def       lowercase_	(				self			:  Optional[int]						,      **__snake_case			:  str     ):
      return AutoProcessor.from_pretrained(self.tmpdirname						,      **__snake_case     ).tokenizer







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  Optional[Any]     ):
      return AutoProcessor.from_pretrained(self.tmpdirname						,      **__snake_case     ).image_processor







    def       lowercase_	(				self			:  int						,      **__snake_case			:  Dict     ):
      return AutoProcessor.from_pretrained(self.tmpdirname						,      **__snake_case     ).qformer_tokenizer







    def       lowercase_	(				self			:  Union[str, Any]     ):
      shutil.rmtree(self.tmpdirname     )







    def       lowercase_	(				self			:  str     ):
      a						: str					       =					[np.random.randint(2_55						,      size=(3, 30, 4_00)						,      dtype=np.uinta     )]

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

      return image_inputs







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Tuple					       =					InstructBlipProcessor(
          tokenizer=self.get_tokenizer()						,      image_processor=self.get_image_processor()						,      qformer_tokenizer=self.get_qformer_tokenizer()						,      )
      processor.save_pretrained(self.tmpdirname     )

      a						: int					       =					self.get_tokenizer(bos_token='(BOS)'						,      eos_token='(EOS)'     )
      a						: List[Any]					       =					self.get_image_processor(do_normalize=__snake_case						,      padding_value=1.0     )

      a						: str					       =					InstructBlipProcessor.from_pretrained(
          self.tmpdirname						,      bos_token='(BOS)'						,      eos_token='(EOS)'						,      do_normalize=__snake_case						,      padding_value=1.0     )

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

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







    def       lowercase_	(				self			:  Union[str, Any]     ):
      a						: List[str]					       =					self.get_image_processor()
      a						: str					       =					self.get_tokenizer()
      a						: Optional[int]					       =					self.get_qformer_tokenizer()

      a						: Optional[Any]					       =					InstructBlipProcessor(
          tokenizer=__snake_case						,      image_processor=__snake_case						,      qformer_tokenizer=__snake_case     )

      a						: Any					       =					self.prepare_image_inputs()

      a						: Any					       =					image_processor(__snake_case						,      return_tensors='np'     )
      a						: int					       =					processor(images=__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       lowercase_	(				self			:  Union[str, Any]     ):
      a						: int					       =					self.get_image_processor()
      a						: Dict					       =					self.get_tokenizer()
      a						: str					       =					self.get_qformer_tokenizer()

      a						: List[Any]					       =					InstructBlipProcessor(
          tokenizer=__snake_case						,      image_processor=__snake_case						,      qformer_tokenizer=__snake_case     )

      a						: Union[str, Any]					       =					'lower newer'

      a						: Any					       =					processor(text=__snake_case     )

      a						: int					       =					tokenizer(__snake_case						,      return_token_type_ids=__snake_case     )
      a						: int					       =					qformer_tokenizer(__snake_case						,      return_token_type_ids=__snake_case     )

      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       lowercase_	(				self			:  Tuple     ):
      a						: Tuple					       =					self.get_image_processor()
      a						: Tuple					       =					self.get_tokenizer()
      a						: Any					       =					self.get_qformer_tokenizer()

      a						: Tuple					       =					InstructBlipProcessor(
          tokenizer=__snake_case						,      image_processor=__snake_case						,      qformer_tokenizer=__snake_case     )

      a						: Tuple					       =					'lower newer'
      a						: Optional[Any]					       =					self.prepare_image_inputs()

      a						: Tuple					       =					processor(text=__snake_case						,      images=__snake_case     )

      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(__snake_case     ):
        processor()







    def       lowercase_	(				self			:  List[str]     ):
      a						: str					       =					self.get_image_processor()
      a						: int					       =					self.get_tokenizer()
      a						: List[Any]					       =					self.get_qformer_tokenizer()

      a						: Tuple					       =					InstructBlipProcessor(
          tokenizer=__snake_case						,      image_processor=__snake_case						,      qformer_tokenizer=__snake_case     )

      a						: Tuple					       =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      a						: List[str]					       =					processor.batch_decode(__snake_case     )
      a						: Optional[int]					       =					tokenizer.batch_decode(__snake_case     )

      self.assertListEqual(__snake_case						,      __snake_case     )







    def       lowercase_	(				self			:  List[str]     ):
      a						: List[Any]					       =					self.get_image_processor()
      a						: str					       =					self.get_tokenizer()
      a						: List[str]					       =					self.get_qformer_tokenizer()

      a						: Optional[Any]					       =					InstructBlipProcessor(
          tokenizer=__snake_case						,      image_processor=__snake_case						,      qformer_tokenizer=__snake_case     )

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

      a						: Dict					       =					processor(text=__snake_case						,      images=__snake_case     )

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




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase: List[Any]							=						{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: Optional[int]							=						['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: Dict							=						['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     lowerCAmelCase: Tuple							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)