





from __future__ import annotations



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE	)
      # We need to create solution object to save path.
      SCREAMING_SNAKE_CASE		=						[[0 for _ in range(_SCREAMING_SNAKE_CASE	)] for _ in range(_SCREAMING_SNAKE_CASE	)]
      SCREAMING_SNAKE_CASE		=						run_maze(_SCREAMING_SNAKE_CASE ,						0 ,						0 ,						_SCREAMING_SNAKE_CASE	)
      if solved:
            print("""\n""".join(str(_SCREAMING_SNAKE_CASE	) for row in solutions	)	)
      else:
            print("""No solution exists!"""	)
      return solved



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE	)
      # Final check point.
      if i == j == (size - 1):
            SCREAMING_SNAKE_CASE		=						1
            return True

      SCREAMING_SNAKE_CASE		=						(not i < 0) and (not j < 0)  # Check lower bounds
      SCREAMING_SNAKE_CASE		=						(i < size) and (j < size)  # Check upper bounds

      if lower_flag and upper_flag:
            # check for already visited and block points.
            SCREAMING_SNAKE_CASE		=						(not solutions[i][j]) and (not maze[i][j])
            if block_flag:
                  # check visited
                  SCREAMING_SNAKE_CASE		=						1

                  # check for directions
                  if (
                      run_maze(_SCREAMING_SNAKE_CASE ,						i + 1 ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
                      or run_maze(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						j + 1 ,						_SCREAMING_SNAKE_CASE	)
                      or run_maze(_SCREAMING_SNAKE_CASE ,						i - 1 ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
                      or run_maze(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						j - 1 ,						_SCREAMING_SNAKE_CASE	)
                  ):
                        return True

                  SCREAMING_SNAKE_CASE		=						0
                  return False
      return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()









import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   List[str]:


      '''simple docstring'''


      return field(default_factory=lambda: default ,						metadata=_SCREAMING_SNAKE_CASE	)



@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	List[str]			=			list_field(
         default=[]							,      metadata={
             "help": (
                 "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                 " of all available models"
             )
         }							,      )

     __snake_case    :	List[int]			=			list_field(
         default=[8]							,      metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}   )

     __snake_case    :	List[int]			=			list_field(
         default=[8, 32, 128, 512]							,      metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}							,      )

     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Use FP16 to accelerate inference."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Benchmark training of model"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Verbose memory tracing"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,      )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Trace memory line by line"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save result to a CSV file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save all print statements in a log file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to print environment information"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                 " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                 " for debugging / testing and on TPU."
             )
         }							,      )
     __snake_case    :	str			=			field(
         default=F"inference_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"inference_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"env_info_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving environment information."}							,      )
     __snake_case    :	str			=			field(
         default=F"log_{round(time()   )}.csv"							,      metadata={"help": "Log filename used if print statements are saved in log."}							,      )
     __snake_case    :	int			=			field(default=3							,      metadata={"help": "Times an experiment will be run."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                 " model weights."
             )
         }							,      )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Dict:




           '''simple docstring'''


           warnings.warn(
               F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""						,lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self       )						,indent=2       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           if len(self.models       ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""       )
           return self.models






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		int:




           '''simple docstring'''


           if not self.multi_process:
                 return False
           elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""       )
                 return False
           else:
                 return True


