





"""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    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				tempfile.mkdtemp()

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

         lowerCAmelCase__     :Dict											=				InstructBlipProcessor(snake_case_       ,					snake_case_       ,					snake_case_ )

         processor.save_pretrained(self.tmpdirname )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         shutil.rmtree(self.tmpdirname )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				[np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta )]

         lowerCAmelCase__     :Optional[Any]											=				[Image.fromarray(np.moveaxis(snake_case_       ,					0       ,					-1 ) ) for x in image_inputs]

         return image_inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

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

         lowerCAmelCase__     :List[str]											=				self.get_tokenizer(bos_token='(BOS)'       ,					eos_token='(EOS)' )
         lowerCAmelCase__     :int											=				self.get_image_processor(do_normalize=snake_case_       ,					padding_value=1.0 )

         lowerCAmelCase__     :Union[str, Any]											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.get_image_processor()
         lowerCAmelCase__     :List[str]											=				self.get_tokenizer()
         lowerCAmelCase__     :Any											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :List[Any]											=				InstructBlipProcessor(
             tokenizer=snake_case_       ,					image_processor=snake_case_       ,					qformer_tokenizer=snake_case_ )

         lowerCAmelCase__     :Any											=				self.prepare_image_inputs()

         lowerCAmelCase__     :str											=				image_processor(snake_case_       ,					return_tensors='np' )
         lowerCAmelCase__     :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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.get_image_processor()
         lowerCAmelCase__     :List[Any]											=				self.get_tokenizer()
         lowerCAmelCase__     :Optional[int]											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :List[str]											=				InstructBlipProcessor(
             tokenizer=snake_case_       ,					image_processor=snake_case_       ,					qformer_tokenizer=snake_case_ )

         lowerCAmelCase__     :int											=				"""lower newer"""

         lowerCAmelCase__     :Optional[int]											=				processor(text=snake_case_ )

         lowerCAmelCase__     :Optional[int]											=				tokenizer(snake_case_       ,					return_token_type_ids=snake_case_ )
         lowerCAmelCase__     :Dict											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.get_image_processor()
         lowerCAmelCase__     :List[str]											=				self.get_tokenizer()
         lowerCAmelCase__     :Any											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :str											=				InstructBlipProcessor(
             tokenizer=snake_case_       ,					image_processor=snake_case_       ,					qformer_tokenizer=snake_case_ )

         lowerCAmelCase__     :Tuple											=				"""lower newer"""
         lowerCAmelCase__     :Dict											=				self.prepare_image_inputs()

         lowerCAmelCase__     :Optional[int]											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.get_image_processor()
         lowerCAmelCase__     :Dict											=				self.get_tokenizer()
         lowerCAmelCase__     :Tuple											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :Optional[Any]											=				InstructBlipProcessor(
             tokenizer=snake_case_       ,					image_processor=snake_case_       ,					qformer_tokenizer=snake_case_ )

         lowerCAmelCase__     :Any											=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__     :Tuple											=				processor.batch_decode(snake_case_ )
         lowerCAmelCase__     :Union[str, Any]											=				tokenizer.batch_decode(snake_case_ )

         self.assertListEqual(snake_case_       ,					snake_case_ )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.get_image_processor()
         lowerCAmelCase__     :List[str]											=				self.get_tokenizer()
         lowerCAmelCase__     :Optional[Any]											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :Tuple											=				InstructBlipProcessor(
             tokenizer=snake_case_       ,					image_processor=snake_case_       ,					qformer_tokenizer=snake_case_ )

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

         lowerCAmelCase__     :Optional[int]											=				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_torch_available,
)


__a  			=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
