
lowerCamelCase_  :	Optional[Any] 				=					2_56
# Modulus to hash a string
lowerCamelCase_  :	Union[str, Any] 				=					1_00_00_03







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		bool:
				UpperCamelCase_:							Dict		=  len(lowerCamelCase  )
				UpperCamelCase_:							str		=  len(lowerCamelCase  )
				if p_len > t_len:
								return False

				UpperCamelCase_:							str		=  0
				UpperCamelCase_:							int		=  0
				UpperCamelCase_:							Optional[Any]		=  1

				# Calculating the hash of pattern and substring of text
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  (ord(pattern[i]  ) + p_hash * alphabet_size) % modulus
								UpperCamelCase_:							Optional[Any]		=  (ord(text[i]  ) + text_hash * alphabet_size) % modulus
								if i == p_len - 1:
												continue
								UpperCamelCase_:							List[Any]		=  (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
								UpperCamelCase_:							Optional[Any]		=  (
								    (text_hash - ord(text[i]  ) * modulus_power) * alphabet_size
								    + ord(text[i + p_len]  )
								) % modulus
				return False







def     A__   (  )					->		None:
				UpperCamelCase_:							List[Any]		=  """abc1abc12"""
				UpperCamelCase_:							Optional[int]		=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
				UpperCamelCase_:							List[Any]		=  """alskfjaldsk23adsfabcabc"""
				assert rabin_karp(lowerCamelCase       ,      lowerCamelCase  ) and not rabin_karp(lowerCamelCase       ,      lowerCamelCase  )

				# Test 2)
				UpperCamelCase_:							str		=  """ABABX"""
				UpperCamelCase_:							Optional[int]		=  """ABABZABABYABABX"""
				assert rabin_karp(lowerCamelCase       ,      lowerCamelCase  )

				# Test 3)
				UpperCamelCase_:							int		=  """AAAB"""
				UpperCamelCase_:							Any		=  """ABAAAAAB"""
				assert rabin_karp(lowerCamelCase       ,      lowerCamelCase  )

				# Test 4)
				UpperCamelCase_:							List[str]		=  """abcdabcy"""
				UpperCamelCase_:							Any		=  """abcxabcdabxabcdabcdabcy"""
				assert rabin_karp(lowerCamelCase       ,      lowerCamelCase  )

				# Test 5)
				UpperCamelCase_:							List[Any]		=  """Lü"""
				UpperCamelCase_:							Tuple		=  """Lüsai"""
				assert rabin_karp(lowerCamelCase       ,      lowerCamelCase  )
				UpperCamelCase_:							Union[str, Any]		=  """Lue"""
				assert not rabin_karp(lowerCamelCase       ,      lowerCamelCase  )
				print("""Success."""  )


if __name__ == "__main__":
							test_rabin_karp()








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_  :	Any 				=					5_00_00
lowerCamelCase_  :	Any 				=					50_00

lowerCamelCase_				,    lowerCamelCase_  :	Tuple 				=					os.path.split(__file__)
lowerCamelCase_  :	int 				=					os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(0       ,      len(lowerCamelCase  )       ,      lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  dataset[i : i + batch_size]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							List[str]		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(0       ,      lowerCamelCase       ,      lowerCamelCase  ):
												UpperCamelCase_:							Union[str, Any]		=  dataset[i : i + batch_size]







def     A__   (  )					->		Tuple:
				UpperCamelCase_:							int		=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:							Union[str, Any]		=  [
				    (read, {"""length""": SMALL_TEST}),
				    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
				]

				UpperCamelCase_:							Tuple		=  [
				    (read, {"""length""": SMALL_TEST}),
				    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print("""generating dataset"""  )
								UpperCamelCase_:							int		=  datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
								UpperCamelCase_:							Optional[int]		=  generate_example_dataset(
								    os.path.join(lowerCamelCase       ,      """dataset.arrow"""  )       ,      lowerCamelCase       ,      num_examples=lowerCamelCase       ,      seq_shapes={"""list""": (1_00,)}       ,      )
								print("""first set of iterations"""  )
								for func, kwargs in functions:
												print(func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(lowerCamelCase       ,      **lowerCamelCase  )

								print("""shuffling dataset"""  )
								UpperCamelCase_:							Dict		=  dataset.shuffle()
								print("""Second set of iterations (after shuffling"""  )
								for func, kwargs in functions_shuffled:
												print("""shuffled """       ,      func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(
												    lowerCamelCase       ,      **lowerCamelCase  )

				with open(lowerCamelCase       ,      """wb"""  ) as f:
								f.write(json.dumps(lowerCamelCase  ).encode("""utf-8"""  )  )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_iterating()






