


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  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Tuple     =	tempfile.mkdtemp()

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

        __lowerCamelCase:				List[Any]     =	InstructBlipProcessor(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        processor.save_pretrained(self.tmpdirname							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        return AutoProcessor.from_pretrained(self.tmpdirname    ,	**SCREAMING_SNAKE_CASE_							).tokenizer





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	**SCREAMING_SNAKE_CASE_					:				Any							):
        return AutoProcessor.from_pretrained(self.tmpdirname    ,	**SCREAMING_SNAKE_CASE_							).qformer_tokenizer





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Union[str, Any]     =	[np.random.randint(255    ,	size=(3, 30, 400)    ,	dtype=np.uinta							)]

        __lowerCamelCase:				Optional[int]     =	[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_    ,	0    ,	-1							)							) for x in image_inputs]

        return image_inputs





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

        __lowerCamelCase:				Any     =	self.get_tokenizer(bos_token="""(BOS)"""    ,	eos_token="""(EOS)"""							)
        __lowerCamelCase:				Dict     =	self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_    ,	padding_value=1.0							)

        __lowerCamelCase:				int     =	InstructBlipProcessor.from_pretrained(
            self.tmpdirname    ,	bos_token="""(BOS)"""    ,	eos_token="""(EOS)"""    ,	do_normalize=SCREAMING_SNAKE_CASE_    ,	padding_value=1.0							)

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

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				str     =	self.get_image_processor()
        __lowerCamelCase:				Optional[int]     =	self.get_tokenizer()
        __lowerCamelCase:				str     =	self.get_qformer_tokenizer()

        __lowerCamelCase:				Optional[Any]     =	InstructBlipProcessor(
            tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_    ,	qformer_tokenizer=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[Any]     =	self.prepare_image_inputs()

        __lowerCamelCase:				List[Any]     =	image_processor(SCREAMING_SNAKE_CASE_    ,	return_tensors="""np"""							)
        __lowerCamelCase:				Any     =	processor(images=SCREAMING_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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Dict     =	self.get_image_processor()
        __lowerCamelCase:				Union[str, Any]     =	self.get_tokenizer()
        __lowerCamelCase:				Optional[int]     =	self.get_qformer_tokenizer()

        __lowerCamelCase:				int     =	InstructBlipProcessor(
            tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_    ,	qformer_tokenizer=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[Any]     =	"""lower newer"""

        __lowerCamelCase:				str     =	processor(text=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	return_token_type_ids=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	qformer_tokenizer(SCREAMING_SNAKE_CASE_    ,	return_token_type_ids=SCREAMING_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  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				int     =	self.get_image_processor()
        __lowerCamelCase:				Tuple     =	self.get_tokenizer()
        __lowerCamelCase:				Union[str, Any]     =	self.get_qformer_tokenizer()

        __lowerCamelCase:				Any     =	InstructBlipProcessor(
            tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_    ,	qformer_tokenizer=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	"""lower newer"""
        __lowerCamelCase:				List[Any]     =	self.prepare_image_inputs()

        __lowerCamelCase:				str     =	processor(text=SCREAMING_SNAKE_CASE_    ,	images=SCREAMING_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(SCREAMING_SNAKE_CASE_							):
           processor()





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				str     =	self.get_image_processor()
        __lowerCamelCase:				Dict     =	self.get_tokenizer()
        __lowerCamelCase:				Optional[int]     =	self.get_qformer_tokenizer()

        __lowerCamelCase:				List[str]     =	InstructBlipProcessor(
            tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_    ,	qformer_tokenizer=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __lowerCamelCase:				List[Any]     =	processor.batch_decode(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_							)

        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	self.get_image_processor()
        __lowerCamelCase:				Optional[Any]     =	self.get_tokenizer()
        __lowerCamelCase:				Any     =	self.get_qformer_tokenizer()

        __lowerCamelCase:				Union[str, Any]     =	InstructBlipProcessor(
            tokenizer=SCREAMING_SNAKE_CASE_    ,	image_processor=SCREAMING_SNAKE_CASE_    ,	qformer_tokenizer=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	"""lower newer"""
        __lowerCamelCase:				Dict     =	self.prepare_image_inputs()

        __lowerCamelCase:				Tuple     =	processor(text=SCREAMING_SNAKE_CASE_    ,	images=SCREAMING_SNAKE_CASE_							)

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






import math



def     __lowerCAmelCase						(							snake_case	:    int )       ->							bool:
   __lowerCamelCase:				Dict     =	math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )

   return exponent == int(snake_case )



def     __lowerCAmelCase						(							snake_case	:    float = 1 / 12345 )       ->							int:
   __lowerCamelCase:				str     =	0
   __lowerCamelCase:				Optional[Any]     =	0

   __lowerCamelCase:				str     =	3
   while True:
      __lowerCamelCase:				int     =	(integer**2 - 1) / 4
      # if candidate is an integer, then there is a partition for k
      if partition_candidate == int(snake_case ):
         __lowerCamelCase:				Union[str, Any]     =	int(snake_case )
         total_partitions += 1
         if check_partition_perfect(snake_case ):
            perfect_partitions += 1
      if perfect_partitions > 0:
         if perfect_partitions / total_partitions < max_proportion:
            return int(snake_case )
      integer += 1


if __name__ == "__main__":
   print(F"""{solution() = }""")


