


"""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 SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	tempfile.mkdtemp()

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

       __lowercase									=	InstructBlipProcessor(A_      ,       A_      ,       A_							)

       processor.save_pretrained(self.tmpdirname							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       **A_	:	str							):






       '''simple docstring'''


       return AutoProcessor.from_pretrained(self.tmpdirname      ,       **A_							).tokenizer
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       **A_	:	int							):






       '''simple docstring'''


       return AutoProcessor.from_pretrained(self.tmpdirname      ,       **A_							).image_processor
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       **A_	:	Union[str, Any]							):






       '''simple docstring'''


       return AutoProcessor.from_pretrained(self.tmpdirname      ,       **A_							).qformer_tokenizer
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       shutil.rmtree(self.tmpdirname							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


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

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

       return image_inputs
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


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

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

       __lowercase									=	InstructBlipProcessor.from_pretrained(
           self.tmpdirname      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       do_normalize=A_      ,       padding_value=1.0							)

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

       self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string()							)
       self.assertIsInstance(processor.image_processor      ,       A_							)
       self.assertIsInstance(processor.qformer_tokenizer      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


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

       __lowercase									=	InstructBlipProcessor(
           tokenizer=A_      ,       image_processor=A_      ,       qformer_tokenizer=A_							)

       __lowercase									=	self.prepare_image_inputs()

       __lowercase									=	image_processor(A_      ,       return_tensors="""np"""							)
       __lowercase									=	processor(images=A_      ,       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	:	Tuple							):






       '''simple docstring'''


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

       __lowercase									=	InstructBlipProcessor(
           tokenizer=A_      ,       image_processor=A_      ,       qformer_tokenizer=A_							)

       __lowercase									=	"""lower newer"""

       __lowercase									=	processor(text=A_							)

       __lowercase									=	tokenizer(A_      ,       return_token_type_ids=A_							)
       __lowercase									=	qformer_tokenizer(A_      ,       return_token_type_ids=A_							)

       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	:	Optional[Any]							):






       '''simple docstring'''


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

       __lowercase									=	InstructBlipProcessor(
           tokenizer=A_      ,       image_processor=A_      ,       qformer_tokenizer=A_							)

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

       __lowercase									=	processor(text=A_      ,       images=A_							)

       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(A_							):
        processor()
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


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

       __lowercase									=	InstructBlipProcessor(
           tokenizer=A_      ,       image_processor=A_      ,       qformer_tokenizer=A_							)

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

       __lowercase									=	processor.batch_decode(A_							)
       __lowercase									=	tokenizer.batch_decode(A_							)

       self.assertListEqual(A_      ,       A_							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


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

       __lowercase									=	InstructBlipProcessor(
           tokenizer=A_      ,       image_processor=A_      ,       qformer_tokenizer=A_							)

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

       __lowercase									=	processor(text=A_      ,       images=A_							)

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




"""simple docstring"""

# Algorithm for the pigeonhole sorting




def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	min(UpperCamelCase__ )  # min() finds the minimum value
 __lowercase									=	max(UpperCamelCase__ )  # max() finds the maximum value

 __lowercase									=	max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __lowercase									=	[0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(UpperCamelCase__				,   UpperCamelCase__ ), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __lowercase									=	0
 for count in range(UpperCamelCase__ ):
  while holes[count] > 0:
   holes[count] -= 1
   __lowercase									=	count + min_val
   i += 1


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(UpperCamelCase__ )
 print("""Sorted order is:"""				,   """ """.join(UpperCamelCase__ ) )


if __name__ == "__main__":
       main()
