
'''simple docstring'''

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

from ..utils import logging


__snake_case:  Union[str, Any]								=			logging.get_logger(__name__)






def 	_snake_case						(      A_       :    Dict=None     ,		A_       :    Tuple=None   ):
  """simple docstring"""







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




@dataclass
class  _UpperCAmelCase :

     """simple docstring"""

     a_    	=					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"
             )
         }	,)

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

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

     a_    	=					field(
         default=lowerCAmelCase__	,metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}	,)
     a_    	=					field(
         default=lowerCAmelCase__	,metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}	,)
     a_    	=					field(
         default=lowerCAmelCase__	,metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}    )
     a_    	=					field(default=lowerCAmelCase__	,metadata={"help": "Use FP16 to accelerate inference."}    )
     a_    	=					field(default=lowerCAmelCase__	,metadata={"help": "Benchmark training of model"}    )
     a_    	=					field(default=lowerCAmelCase__	,metadata={"help": "Verbose memory tracing"}    )
     a_    	=					field(
         default=lowerCAmelCase__	,metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}	,)
     a_    	=					field(
         default=lowerCAmelCase__	,metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }	,)
     a_    	=					field(default=lowerCAmelCase__	,metadata={"help": "Trace memory line by line"}    )
     a_    	=					field(default=lowerCAmelCase__	,metadata={"help": "Save result to a CSV file"}    )
     a_    	=					field(default=lowerCAmelCase__	,metadata={"help": "Save all print statements in a log file"}    )
     a_    	=					field(default=lowerCAmelCase__	,metadata={"help": "Whether to print environment information"}    )
     a_    	=					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."
             )
         }	,)
     a_    	=					field(
         default=F'''inference_time_{round(time()    )}.csv'''	,metadata={"help": "CSV filename used if saving time results to csv."}	,)
     a_    	=					field(
         default=F'''inference_memory_{round(time()    )}.csv'''	,metadata={"help": "CSV filename used if saving memory results to csv."}	,)
     a_    	=					field(
         default=F'''train_time_{round(time()    )}.csv'''	,metadata={"help": "CSV filename used if saving time results to csv for training."}	,)
     a_    	=					field(
         default=F'''train_memory_{round(time()    )}.csv'''	,metadata={"help": "CSV filename used if saving memory results to csv for training."}	,)
     a_    	=					field(
         default=F'''env_info_{round(time()    )}.csv'''	,metadata={"help": "CSV filename used if saving environment information."}	,)
     a_    	=					field(
         default=F'''log_{round(time()    )}.csv'''	,metadata={"help": "Log filename used if print statements are saved in log."}	,)
     a_    	=					field(default=3	,metadata={"help": "Times an experiment will be run."}    )
     a_    	=					field(
         default=lowerCAmelCase__	,metadata={
             "help": (
                 "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                 " model weights."
             )
         }	,)


     def 							_lowerCAmelCase  (      self ):

       '''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."""							,		UpperCamelCase__							,		)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     @property
     def 							_lowerCAmelCase  (      self ):

       '''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 							_lowerCAmelCase  (      self ):

       '''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




'''simple docstring'''

def 	_snake_case						(      A_       :    int     ,		A_       :    Optional[Any]     ,		A_       :    Optional[int]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							[False] * len(A_   )
  a_      :	List[str]      =							[]
  queue.append(A_   )
  a_      :	Union[str, Any]      =							True

  while queue:
    a_      :	List[Any]      =							queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(A_   )
        a_      :	Any      =							True
        a_      :	List[Any]      =							u

  return visited[t]






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Dict      =							[-1] * (len(A_   ))
  a_      :	List[str]      =							0
  while bfs(A_     ,		A_     ,		A_     ,		A_   ):
    a_      :	Any      =							float("""Inf"""   )
    a_      :	Any      =							sink

    while s != source:
      # Find the minimum value in select path
      a_      :	Any      =							min(A_     ,		graph[parent[s]][s]   )
      a_      :	List[Any]      =							parent[s]

    max_flow += path_flow
    a_      :	Tuple      =							sink

    while v != source:
      a_      :	Tuple      =							parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      a_      :	List[Any]      =							parent[v]
  return max_flow


__snake_case:  List[str]								=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__snake_case							,__snake_case:  int								=			0, 5




print(ford_fulkerson(graph, source, sink))


