


'''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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def  a_					(			self		)       ->    str:
          _a						=		tempfile.mkdtemp()

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

          _a						=		BlipaProcessor(__UpperCamelCase     ,							__UpperCamelCase		)

          processor.save_pretrained(self.tmpdirname		)






     def  a_					(			self     ,							**__UpperCamelCase		)       ->    Dict:
          return AutoProcessor.from_pretrained(self.tmpdirname     ,							**__UpperCamelCase		).tokenizer






     def  a_					(			self     ,							**__UpperCamelCase		)       ->    Dict:
          return AutoProcessor.from_pretrained(self.tmpdirname     ,							**__UpperCamelCase		).image_processor






     def  a_					(			self		)       ->    Tuple:
          shutil.rmtree(self.tmpdirname		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		[np.random.randint(255     ,							size=(3, 30, 400)     ,							dtype=np.uinta		)]

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

          return image_inputs






     def  a_					(			self		)       ->    Any:
          _a						=		BlipaProcessor(tokenizer=self.get_tokenizer()     ,							image_processor=self.get_image_processor()		)
          processor.save_pretrained(self.tmpdirname		)

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

          _a						=		BlipaProcessor.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		)






     def  a_					(			self		)       ->    Tuple:
          _a						=		self.get_image_processor()
          _a						=		self.get_tokenizer()

          _a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

          _a						=		self.prepare_image_inputs()

          _a						=		image_processor(__UpperCamelCase     ,							return_tensors="np"		)
          _a						=		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  a_					(			self		)       ->    int:
          _a						=		self.get_image_processor()
          _a						=		self.get_tokenizer()

          _a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

          _a						=		"lower newer"

          _a						=		processor(text=__UpperCamelCase		)

          _a						=		tokenizer(__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase		)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]     ,							encoded_processor[key]		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		self.get_image_processor()
          _a						=		self.get_tokenizer()

          _a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

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

          _a						=		processor(text=__UpperCamelCase     ,							images=__UpperCamelCase		)

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

          # test if it raises when no input is passed
          with pytest.raises(__UpperCamelCase		):
               processor()






     def  a_					(			self		)       ->    Tuple:
          _a						=		self.get_image_processor()
          _a						=		self.get_tokenizer()

          _a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

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

          _a						=		processor.batch_decode(__UpperCamelCase		)
          _a						=		tokenizer.batch_decode(__UpperCamelCase		)

          self.assertListEqual(__UpperCamelCase     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    int:
          _a						=		self.get_image_processor()
          _a						=		self.get_tokenizer()

          _a						=		BlipaProcessor(tokenizer=__UpperCamelCase     ,							image_processor=__UpperCamelCase		)

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

          _a						=		processor(text=__UpperCamelCase     ,							images=__UpperCamelCase		)

          # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
          self.assertListEqual(list(inputs.keys()		)     ,							["pixel_values", "input_ids", "attention_mask"]		)





'''simple docstring'''

lowercase__				     =		256
# Modulus to hash a string
lowercase__				     =		1_000_003






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     bool:







     '''simple docstring'''






     _a						=		len(__lowerCamelCase				)
     _a						=		len(__lowerCamelCase				)
     if p_len > t_len:
          return False

     _a						=		0
     _a						=		0
     _a						=		1

     # Calculating the hash of pattern and substring of text
     for i in range(__lowerCamelCase				):
          _a						=		(ord(pattern[i]				) + p_hash * alphabet_size) % modulus
          _a						=		(ord(text[i]				) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          _a						=		(modulus_power * alphabet_size) % modulus

     for i in range(0      ,  t_len - p_len + 1				):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          _a						=		(
              (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
              + ord(text[i + p_len]				)
          ) % modulus
     return False






def __UpperCamelCase   (			)      ->     None:







     '''simple docstring'''






     _a						=		"abc1abc12"
     _a						=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
     _a						=		"alskfjaldsk23adsfabcabc"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				) and not rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 2)
     _a						=		"ABABX"
     _a						=		"ABABZABABYABABX"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 3)
     _a						=		"AAAB"
     _a						=		"ABAAAAAB"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 4)
     _a						=		"abcdabcy"
     _a						=		"abcxabcdabxabcdabcdabcy"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 5)
     _a						=		"Lü"
     _a						=		"Lüsai"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)
     _a						=		"Lue"
     assert not rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)
     print("Success."				)


if __name__ == "__main__":
   test_rabin_karp()

