

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   int=()     ,					SCREAMING_SNAKE_CASE_		:   int=None     ,					SCREAMING_SNAKE_CASE_		:   Dict="no"     ,					SCREAMING_SNAKE_CASE_		:   Dict="29500"      ):



					'''simple docstring'''





					lowercase__   :       Optional[int]				=	False
					lowercase__   :       Optional[Any]				=	False
					if any(key.startswith('KAGGLE'      ) for key in os.environ.keys()      ):
										lowercase__   :       Optional[Any]				=	True
					elif "IPython" in sys.modules:
										lowercase__   :       Dict				=	'google.colab' in str(sys.modules['IPython'].get_ipython()      )

					try:
										lowercase__   :       Union[str, Any]				=	PrecisionType(mixed_precision.lower()      )
					except ValueError:
										raise ValueError(
										    f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""      )

					if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'     ,					SCREAMING_SNAKE_CASE_      ) is not None):
										# TPU launch
										import torch_xla.distributed.xla_multiprocessing as xmp

										if len(AcceleratorState._shared_state      ) > 0:
															raise ValueError(
															    'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
															    'your training function. Restart your notebook and make sure no cells initializes an '
															    '`Accelerator`.'      )
										if num_processes is None:
															lowercase__   :       Tuple				=	8

										lowercase__   :       str				=	PrepareForLaunch(SCREAMING_SNAKE_CASE_     ,					distributed_type='TPU'      )
										print(f"""Launching a training on {num_processes} TPU cores."""      )
										xmp.spawn(SCREAMING_SNAKE_CASE_     ,					args=SCREAMING_SNAKE_CASE_     ,					nprocs=SCREAMING_SNAKE_CASE_     ,					start_method='fork'      )
					elif in_colab:
										# No need for a distributed launch otherwise as it's either CPU or one GPU.
										if torch.cuda.is_available():
															print('Launching training on one GPU.'      )
										else:
															print('Launching training on one CPU.'      )
										function(*SCREAMING_SNAKE_CASE_      )
					else:
										if num_processes is None:
															raise ValueError(
															    'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'      )

										if num_processes > 1:
															# Multi-GPU launch
															from torch.multiprocessing import start_processes
															from torch.multiprocessing.spawn import ProcessRaisedException

															if len(AcceleratorState._shared_state      ) > 0:
																				raise ValueError(
																				    'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
																				    'inside your training function. Restart your notebook and make sure no cells initializes an '
																				    '`Accelerator`.'      )

															if torch.cuda.is_initialized():
																				raise ValueError(
																				    'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
																				    'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
																				    'function.'      )

															# torch.distributed will expect a few environment variable to be here. We set the ones common to each
															# process here (the other ones will be set be the launcher).
															with patch_environment(
															    world_size=SCREAMING_SNAKE_CASE_     ,					master_addr='127.0.01'     ,					master_port=SCREAMING_SNAKE_CASE_     ,					mixed_precision=SCREAMING_SNAKE_CASE_      ):
																				lowercase__   :       Dict				=	PrepareForLaunch(SCREAMING_SNAKE_CASE_     ,					distributed_type='MULTI_GPU'      )
																				print(f"""Launching training on {num_processes} GPUs."""      )
																				try:
																									start_processes(SCREAMING_SNAKE_CASE_     ,					args=SCREAMING_SNAKE_CASE_     ,					nprocs=SCREAMING_SNAKE_CASE_     ,					start_method='fork'      )
																				except ProcessRaisedException as e:
																									if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																														raise RuntimeError(
																														    'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
																														    'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
																														    'Please review your imports and test them when running the `notebook_launcher()` to identify '
																														    'which one is problematic.'      ) from e

										else:
															# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
															if is_mps_available():
																				lowercase__   :       List[Any]				=	'1'
																				print('Launching training on MPS.'      )
															elif torch.cuda.is_available():
																				print('Launching training on one GPU.'      )
															else:
																				print('Launching training on CPU.'      )
															function(*SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]=()     ,					SCREAMING_SNAKE_CASE_		:   Dict=2      ):



					'''simple docstring'''





					from torch.multiprocessing import start_processes

					with tempfile.NamedTemporaryFile() as tmp_file:
										# torch.distributed will expect a few environment variable to be here. We set the ones common to each
										# process here (the other ones will be set be the launcher).
										with patch_environment(
										    world_size=SCREAMING_SNAKE_CASE_     ,					master_addr='127.0.01'     ,					master_port='29500'     ,					accelerate_mixed_precision='no'     ,					accelerate_debug_rdv_file=tmp_file.name     ,					accelerate_use_cpu='yes'     ,					):
															lowercase__   :       Union[str, Any]				=	PrepareForLaunch(SCREAMING_SNAKE_CASE_     ,					debug=SCREAMING_SNAKE_CASE_      )
															start_processes(SCREAMING_SNAKE_CASE_     ,					args=SCREAMING_SNAKE_CASE_     ,					nprocs=SCREAMING_SNAKE_CASE_     ,					start_method='fork'      )




from __future__ import annotations


class 				SCREAMING_SNAKE_CASE__ :





    def __init__(				self						,       a						,       a):
         lowercase__				,			lowercase__   :       Dict				=	text, pattern
         lowercase__				,			lowercase__   :       Any				=	len(a), len(a)





    def 				snake_case_    (				self						,       a):

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





    def 				snake_case_    (				self						,       a):

         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 				snake_case_    (				self):
         # searches pattern in text and returns index positions
         lowercase__   :       Any				=	[]
         for i in range(self.textLen - self.patLen + 1):
              lowercase__   :       Optional[Any]				=	self.mismatch_in_text(a)
              if mismatch_index == -1:
                   positions.append(a)
              else:
                   lowercase__   :       Optional[int]				=	self.match_in_pattern(self.text[mismatch_index])
                   lowercase__   :       Optional[Any]				=	(
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
         return positions



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

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

