


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 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       tempfile.mkdtemp()

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

       a						:str 							=       InstructBlipProcessor(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

       processor.save_pretrained(self.tmpdirname    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  **_lowerCamelCase    ):
       return AutoProcessor.from_pretrained(self.tmpdirname					,  **_lowerCamelCase    ).tokenizer




 def        SCREAMING_SNAKE_CASE__				(		self					,  **_lowerCamelCase    ):
       return AutoProcessor.from_pretrained(self.tmpdirname					,  **_lowerCamelCase    ).image_processor




 def        SCREAMING_SNAKE_CASE__				(		self					,  **_lowerCamelCase    ):
       return AutoProcessor.from_pretrained(self.tmpdirname					,  **_lowerCamelCase    ).qformer_tokenizer




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       shutil.rmtree(self.tmpdirname    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       [np.random.randint(255					,  size=(3, 30, 400)					,  dtype=np.uinta    )]

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

       return image_inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       InstructBlipProcessor(
           tokenizer=self.get_tokenizer()					,  image_processor=self.get_image_processor()					,  qformer_tokenizer=self.get_qformer_tokenizer()					,  )
       processor.save_pretrained(self.tmpdirname    )

       a						:List[str] 							=       self.get_tokenizer(bos_token='''(BOS)'''					,  eos_token='''(EOS)'''    )
       a						:Tuple 							=       self.get_image_processor(do_normalize=_lowerCamelCase					,  padding_value=1.0    )

       a						:Union[str, Any] 							=       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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       self.get_image_processor()
       a						:Tuple 							=       self.get_tokenizer()
       a						:Union[str, Any] 							=       self.get_qformer_tokenizer()

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

       a						:Optional[int] 							=       self.prepare_image_inputs()

       a						:Optional[int] 							=       image_processor(_lowerCamelCase					,  return_tensors='''np'''    )
       a						:int 							=       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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.get_image_processor()
       a						:Any 							=       self.get_tokenizer()
       a						:Dict 							=       self.get_qformer_tokenizer()

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

       a						:str 							=       '''lower newer'''

       a						:Any 							=       processor(text=_lowerCamelCase    )

       a						:List[Any] 							=       tokenizer(_lowerCamelCase					,  return_token_type_ids=_lowerCamelCase    )
       a						:Optional[int] 							=       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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.get_image_processor()
       a						:str 							=       self.get_tokenizer()
       a						:Union[str, Any] 							=       self.get_qformer_tokenizer()

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

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

       a						:Tuple 							=       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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       self.get_image_processor()
       a						:Union[str, Any] 							=       self.get_tokenizer()
       a						:int 							=       self.get_qformer_tokenizer()

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

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

       a						:Optional[Any] 							=       processor.batch_decode(_lowerCamelCase    )
       a						:List[str] 							=       tokenizer.batch_decode(_lowerCamelCase    )

       self.assertListEqual(_lowerCamelCase					,  _lowerCamelCase    )




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

       a						:List[str] 							=       InstructBlipProcessor(
           tokenizer=_lowerCamelCase					,  image_processor=_lowerCamelCase					,  qformer_tokenizer=_lowerCamelCase    )

       a						:int 							=       '''lower newer'''
       a						:Tuple 							=       self.prepare_image_inputs()

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

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			  = {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = [
           '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''InstructBlipQFormerModel''',
           '''InstructBlipPreTrainedModel''',
           '''InstructBlipForConditionalGeneration''',
           '''InstructBlipVisionModel''',
       ]

if TYPE_CHECKING:
       from .configuration_instructblip import (
           INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           InstructBlipConfig,
           InstructBlipQFormerConfig,
           InstructBlipVisionConfig,
       )
       from .processing_instructblip import InstructBlipProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_instructblip import (
                  INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  InstructBlipForConditionalGeneration,
                  InstructBlipPreTrainedModel,
                  InstructBlipQFormerModel,
                  InstructBlipVisionModel,
              )

else:
       import sys

       snake_case_			  = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

