






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       ):







  """simple docstring"""




  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[int]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      List[Any]       =   BlipImageProcessor()
        SCREAMING_SNAKE_CASE :      List[str]       =   GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   InstructBlipProcessor(_UpperCAmelCase				,				_UpperCAmelCase				,				_UpperCAmelCase		)

        processor.save_pretrained(self.tmpdirname		)


  def  _lowercase      (			self    : Tuple				,				**UpperCAmelCase__    : Union[str, Any]		)					->List[Any]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Any				,				**UpperCAmelCase__    : int		)					->str:
        """simple docstring"""







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


  def  _lowercase      (			self    : int				,				**UpperCAmelCase__    : List[Any]		)					->Union[str, Any]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Dict		)					->Union[str, Any]:
        """simple docstring"""







        shutil.rmtree(self.tmpdirname		)


  def  _lowercase      (			self    : Union[str, Any]		)					->List[str]:
        """simple docstring"""







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

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

        return image_inputs


  def  _lowercase      (			self    : str		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   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 :      Dict       =   self.get_tokenizer(bos_token="""(BOS)"""				,				eos_token="""(EOS)"""		)
        SCREAMING_SNAKE_CASE :      Tuple       =   self.get_image_processor(do_normalize=_UpperCAmelCase				,				padding_value=1.0		)

        SCREAMING_SNAKE_CASE :      Tuple       =   InstructBlipProcessor.from_pretrained(
            self.tmpdirname				,				bos_token="""(BOS)"""				,				eos_token="""(EOS)"""				,				do_normalize=_UpperCAmelCase				,				padding_value=1.0		)

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

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


  def  _lowercase      (			self    : List[str]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.get_image_processor()
        SCREAMING_SNAKE_CASE :      str       =   self.get_tokenizer()
        SCREAMING_SNAKE_CASE :      str       =   self.get_qformer_tokenizer()

        SCREAMING_SNAKE_CASE :      str       =   InstructBlipProcessor(
            tokenizer=_UpperCAmelCase				,				image_processor=_UpperCAmelCase				,				qformer_tokenizer=_UpperCAmelCase		)

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

        SCREAMING_SNAKE_CASE :      str       =   image_processor(_UpperCAmelCase				,				return_tensors="""np"""		)
        SCREAMING_SNAKE_CASE :      Dict       =   processor(images=_UpperCAmelCase				,				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    : Any		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.get_image_processor()
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_tokenizer()
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_qformer_tokenizer()

        SCREAMING_SNAKE_CASE :      Dict       =   InstructBlipProcessor(
            tokenizer=_UpperCAmelCase				,				image_processor=_UpperCAmelCase				,				qformer_tokenizer=_UpperCAmelCase		)

        SCREAMING_SNAKE_CASE :      List[str]       =   '''lower newer'''

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   processor(text=_UpperCAmelCase		)

        SCREAMING_SNAKE_CASE :      Any       =   tokenizer(_UpperCAmelCase				,				return_token_type_ids=_UpperCAmelCase		)
        SCREAMING_SNAKE_CASE :      Tuple       =   qformer_tokenizer(_UpperCAmelCase				,				return_token_type_ids=_UpperCAmelCase		)

        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    : Optional[Any]		)					->str:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      Tuple       =   InstructBlipProcessor(
            tokenizer=_UpperCAmelCase				,				image_processor=_UpperCAmelCase				,				qformer_tokenizer=_UpperCAmelCase		)

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

        SCREAMING_SNAKE_CASE :      Any       =   processor(text=_UpperCAmelCase				,				images=_UpperCAmelCase		)

        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(_UpperCAmelCase		):
              processor()


  def  _lowercase      (			self    : Optional[Any]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self.get_image_processor()
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.get_tokenizer()
        SCREAMING_SNAKE_CASE :      Dict       =   self.get_qformer_tokenizer()

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   InstructBlipProcessor(
            tokenizer=_UpperCAmelCase				,				image_processor=_UpperCAmelCase				,				qformer_tokenizer=_UpperCAmelCase		)

        SCREAMING_SNAKE_CASE :      Dict       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        SCREAMING_SNAKE_CASE :      Dict       =   processor.batch_decode(_UpperCAmelCase		)
        SCREAMING_SNAKE_CASE :      Any       =   tokenizer.batch_decode(_UpperCAmelCase		)

        self.assertListEqual(_UpperCAmelCase				,				_UpperCAmelCase		)




  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[Any]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      List[Any]       =   InstructBlipProcessor(
            tokenizer=_UpperCAmelCase				,				image_processor=_UpperCAmelCase				,				qformer_tokenizer=_UpperCAmelCase		)

        SCREAMING_SNAKE_CASE :      List[str]       =   '''lower newer'''
        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   processor(text=_UpperCAmelCase				,				images=_UpperCAmelCase		)

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







from typing import Any

import numpy as np






def 					__lowercase      (     _A			)     ->						bool:
      return np.array_equal(_A						,    matrix.conjugate().T			)






def 					__lowercase      (     _A						,    _A			)     ->						Any:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   v.conjugate().T
      SCREAMING_SNAKE_CASE :      List[Any]       =   v_star.dot(_A			)
      assert isinstance(_A						,    np.ndarray			)
      return (v_star_dot.dot(_A			)) / (v_star.dot(_A			))






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      Any       =   np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   np.array([[1], [2], [3]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      print(rayleigh_quotient(_A						,    _A			)			)

      SCREAMING_SNAKE_CASE :      Any       =   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      assert rayleigh_quotient(_A						,    _A			) == float(3			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()




