




"""simple docstring"""




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
			import tensorflow as tf


a_        =       logging.get_logger(__name__)

@dataclass
class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=[
		    "no_inference",
		    "no_cuda",
		    "no_tpu",
		    "no_speed",
		    "no_memory",
		    "no_env_print",
		    "no_multi_process",
		]
		def __init__(						self				,						**UpperCamelCase_       )						->       Union[str, Any]:
					for deprecated_arg in self.deprecated_args:
								if deprecated_arg in kwargs:
											__lowercase			:  Tuple								=       deprecated_arg[3:]
											__lowercase			:  Any								=       not kwargs.pop(UpperCamelCase_       )
											logger.warning(
											    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
											    F""" {positive_arg}={kwargs[positive_arg]}"""       )
					__lowercase			:  List[Any]								=       kwargs.pop('''tpu_name'''				,						self.tpu_name       )
					__lowercase			:  Union[str, Any]								=       kwargs.pop('''device_idx'''				,						self.device_idx       )
					__lowercase			:  Optional[int]								=       kwargs.pop('''eager_mode'''				,						self.eager_mode       )
					__lowercase			:  Optional[int]								=       kwargs.pop('''use_xla'''				,						self.use_xla       )
					super().__init__(**UpperCamelCase_       )

		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Name of TPU"}   ,					)
		UpperCamelCase							=field(
		    default=0   ,					metadata={"help": "CPU / GPU device index. Defaults to 0."}   ,					)
		UpperCamelCase							=field(default=snake_case   ,					metadata={"help": "Benchmark models in eager model."}		)
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={
		        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
		    }   ,					)
		@cached_property
		def 	_lowerCamelCase							(						self       )						->       Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
					requires_backends(self				,						['''tf''']       )
					__lowercase			:  Optional[Any]								=       None
					if self.tpu:
								try:
											if self.tpu_name:
														__lowercase			:  Tuple								=       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
											else:
														__lowercase			:  List[str]								=       tf.distribute.cluster_resolver.TPUClusterResolver()
								except ValueError:
											__lowercase			:  List[str]								=       None
					return tpu
		@cached_property
		def 	_lowerCamelCase							(						self       )						->       Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
					requires_backends(self				,						['''tf''']       )
					if self.is_tpu:
								tf.config.experimental_connect_to_cluster(self._setup_tpu       )
								tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

								__lowercase			:  Any								=       tf.distribute.TPUStrategy(self._setup_tpu       )
					else:
								# currently no multi gpu is allowed
								if self.is_gpu:
											# TODO: Currently only single GPU is supported
											tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,						'''GPU'''       )
											__lowercase			:  Dict								=       tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""       )
								else:
											tf.config.set_visible_devices([]				,						'''GPU'''       )  # disable GPU
											__lowercase			:  str								=       tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""       )

					return strategy
		@property
		def 	_lowerCamelCase							(						self       )						->       bool:
					requires_backends(self				,						['''tf''']       )
					return self._setup_tpu is not None
		@property
		def 	_lowerCamelCase							(						self       )						->       "tf.distribute.Strategy":
					requires_backends(self				,						['''tf''']       )
					return self._setup_strategy
		@property
		def 	_lowerCamelCase							(						self       )						->       Optional[int]:
					requires_backends(self				,						['''tf''']       )
					return tf.config.list_physical_devices('''GPU'''       )
		@property
		def 	_lowerCamelCase							(						self       )						->       int:
					requires_backends(self				,						['''tf''']       )
					if self.cuda:
								return len(self.gpu_list       )
					return 0
		@property
		def 	_lowerCamelCase							(						self       )						->       bool:
					return self.n_gpu > 0






from __future__ import annotations

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							text, pattern
              __snake_case	,    __snake_case       	=							len(_UpperCamelCase   ), len(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							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 			a		(		self   )      ->							list[int]:






              """simple docstring"""


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






UpperCamelCase__		 =     '''ABAABA'''
UpperCamelCase__		 =     '''AB'''
UpperCamelCase__		 =     BoyerMooreSearch(text, pattern)
UpperCamelCase__		 =     bms.bad_character_heuristic()

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

