





from __future__ import annotations







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  str ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     text, pattern
						UpperCAmelCase_						,	UpperCAmelCase_								=     len(__lowercase		), len(__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  str		):






						'''simple docstring'''







						for i in range(self.patLen - 1 ,					-1 ,					-1		):
							if char == self.pattern[i]:
								return i
						return -1





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  int		):






						'''simple docstring'''







						for i in range(self.patLen - 1 ,					-1 ,					-1		):
							if self.pattern[i] != self.text[current_pos + i]:
								return current_pos + i
						return -1





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     []
						for i in range(self.textLen - self.patLen + 1		):
							UpperCAmelCase_								=     self.mismatch_in_text(__lowercase		)
							if mismatch_index == -1:
								positions.append(__lowercase		)
							else:
								UpperCAmelCase_								=     self.match_in_pattern(self.text[mismatch_index]		)
								UpperCAmelCase_								=     (
								    mismatch_index - match_index
								)  # shifting index lgtm [py/multiple-definition]
						return positions


UpperCamelCase__   : List[Any]					  = """ABAABA"""
UpperCamelCase__   : Dict					  = """AB"""
UpperCamelCase__   : List[Any]					  = BoyerMooreSearch(text, pattern)
UpperCamelCase__   : int					  = bms.bad_character_heuristic()

if len(positions) == 0:
				print("""No match found""")
else:
				print("""Pattern found in following positions: """)
				print(positions)









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__   : Tuple					  = 5_00_00
UpperCamelCase__   : Any					  = 50_00

UpperCamelCase__  ,			UpperCamelCase__   : Tuple					  = os.path.split(__file__)
UpperCamelCase__   : Union[str, Any]					  = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def    A_(			A				,      A   ):
	for i in range(A   ):
		UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A   ):
	for i in range(0				,      len(A   )				,      A   ):
		UpperCAmelCase_								=     dataset[i : i + batch_size]




@get_duration
def    A_(			A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(A   ):
			UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(0				,      A				,      A   ):
			UpperCAmelCase_								=     dataset[i : i + batch_size]




def    A_(			):
	UpperCAmelCase_								=     {"""num examples""": SPEED_TEST_N_EXAMPLES}
	UpperCAmelCase_								=     [
	    (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""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (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""": 1000}),
	]

	UpperCAmelCase_								=     [
	    (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""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (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""": 1000}),
	]
	with tempfile.TemporaryDirectory() as tmp_dir:
		print("""generating dataset"""   )
		UpperCAmelCase_								=     datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""   )   ), """numbers""": datasets.Value("""float32"""   )}   )
		UpperCAmelCase_								=     generate_example_dataset(
		    os.path.join(A				,      """dataset.arrow"""   )				,      A				,      num_examples=A				,      seq_shapes={"""list""": (100,)}				,      )
		print("""first set of iterations"""   )
		for func, kwargs in functions:
			print(func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(A				,      **A   )

		print("""shuffling dataset"""   )
		UpperCAmelCase_								=     dataset.shuffle()
		print("""Second set of iterations (after shuffling"""   )
		for func, kwargs in functions_shuffled:
			print("""shuffled """				,      func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(
			    A				,      **A   )

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


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


