





'''simple docstring'''

import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.framework.errors_impl import ResourceExhaustedError

  from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
  import pyanvml.pyanvml as nvml

__lowerCAmelCase							:     str		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : bool						,	_lowerCamelCase : bool	):
    def run_func(_lowerCamelCase : List[Any]	):
        @wraps(_lowerCamelCase	)
        def run_in_eager_mode(*_lowerCamelCase : Optional[int]						,	**_lowerCamelCase : str	):
            return func(*_lowerCamelCase						,	**_lowerCamelCase	)

        @wraps(_lowerCamelCase	)
        @tf.function(experimental_compile=_lowerCamelCase	)
        def run_in_graph_mode(*_lowerCamelCase : Tuple						,	**_lowerCamelCase : int	):
            return func(*_lowerCamelCase						,	**_lowerCamelCase	)

        if do_eager_mode is True:
            if use_xla is not False:
                raise ValueError(
                    "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."	)
            return run_in_eager_mode
        else:
            return run_in_graph_mode

    return run_func





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int						,	_lowerCamelCase : int	):
    A__    =      random.Random()
    A__    =      [rng.randint(0						,	vocab_size - 1	) for i in range(batch_size * sequence_length	)]
    return tf.constant(_lowerCamelCase						,	shape=(batch_size, sequence_length)						,	dtype=tf.intaa	)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    "TensorFlow"
 @property
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     return tf.__version__
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:int							)->							float:
     # initialize GPU on separate process
     A__    =      self.args.strategy
     if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."							)
     A__    =      self._prepare_inference_func(lowercase_						,	lowercase_						,	lowercase_							)
     return self._measure_speed(_inference							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:int							)->							float:
     A__    =      self.args.strategy
     if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."							)
     A__    =      self._prepare_train_func(lowercase_						,	lowercase_						,	lowercase_							)
     return self._measure_speed(_train							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:int							)->							[Memory, Optional[MemorySummary]]:
     # initialize GPU on separate process
     if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,	lowercase_							)
     A__    =      self.args.strategy
     if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."							)
     A__    =      self._prepare_inference_func(lowercase_						,	lowercase_						,	lowercase_							)
     return self._measure_memory(_inference							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:int							)->							[Memory, Optional[MemorySummary]]:
     if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,	lowercase_							)
     A__    =      self.args.strategy
     if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."							)

     A__    =      self._prepare_train_func(lowercase_						,	lowercase_						,	lowercase_							)
     return self._measure_memory(_train							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:int							)->							Callable[[], None]:
     A__    =      self.config_dict[model_name]

     if self.args.fpaa:
         raise NotImplementedError("Mixed precision is currently not supported."							)

     A__    =      (
         hasattr(lowercase_						,	"architectures"							)
         and isinstance(config.architectures						,	lowercase_							)
         and len(config.architectures							) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
             A__    =      "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
             A__    =      __import__("transformers"						,	fromlist=[model_class]							)
             A__    =      getattr(lowercase_						,	lowercase_							)
             A__    =      model_cls(lowercase_							)
         except ImportError:
             raise ImportError(
                 F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                 " set `--only_pretrain_model` or `args.only_pretrain_model=True`."							)
     else:
         A__    =      TF_MODEL_MAPPING[config.__class__](lowercase_							)

     # encoder-decoder has vocab size saved differently
     A__    =      config.vocab_size if hasattr(lowercase_						,	"vocab_size"							) else config.encoder.vocab_size
     A__    =      random_input_ids(lowercase_						,	lowercase_						,	lowercase_							)

     @run_with_tf_optimizations(self.args.eager_mode						,	self.args.use_xla							)
     def encoder_decoder_forward():
         return model(lowercase_						,	decoder_input_ids=lowercase_						,	training=lowercase_							)

     @run_with_tf_optimizations(self.args.eager_mode						,	self.args.use_xla							)
     def encoder_forward():
         return model(lowercase_						,	training=lowercase_							)

     A__    =      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

     return _inference
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:str						,	lowercase_				:int						,	lowercase_				:int							)->							Callable[[], None]:
     A__    =      self.config_dict[model_name]

     if self.args.eager_mode is not False:
         raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."							)

     if self.args.fpaa:
         raise NotImplementedError("Mixed precision is currently not supported."							)

     A__    =      (
         hasattr(lowercase_						,	"architectures"							)
         and isinstance(config.architectures						,	lowercase_							)
         and len(config.architectures							) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
             A__    =      "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
             A__    =      __import__("transformers"						,	fromlist=[model_class]							)
             A__    =      getattr(lowercase_						,	lowercase_							)
             A__    =      model_cls(lowercase_							)
         except ImportError:
             raise ImportError(
                 F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                 " set `--only_pretrain_model` or `args.only_pretrain_model=True`."							)
     else:
         A__    =      TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase_							)

     # encoder-decoder has vocab size saved differently
     A__    =      config.vocab_size if hasattr(lowercase_						,	"vocab_size"							) else config.encoder.vocab_size
     A__    =      random_input_ids(lowercase_						,	lowercase_						,	lowercase_							)

     @run_with_tf_optimizations(self.args.eager_mode						,	self.args.use_xla							)
     def encoder_decoder_train():
         A__    =      model(lowercase_						,	decoder_input_ids=lowercase_						,	labels=lowercase_						,	training=lowercase_							)[0]
         A__    =      tf.gradients(lowercase_						,	model.trainable_variables							)
         return gradients

     @run_with_tf_optimizations(self.args.eager_mode						,	self.args.use_xla							)
     def encoder_train():
         A__    =      model(lowercase_						,	labels=lowercase_						,	training=lowercase_							)[0]
         A__    =      tf.gradients(lowercase_						,	model.trainable_variables							)
         return gradients

     A__    =      encoder_decoder_train if config.is_encoder_decoder else encoder_train

     return _train
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:Union[str, Any]							)->							float:
     with self.args.strategy.scope():
         try:
             if self.args.is_tpu or self.args.use_xla:
                 # run additional 10 times to stabilize compilation for tpu
                 logger.info("Do inference on TPU. Running model 5 times to stabilize compilation"							)
                 timeit.repeat(lowercase_						,	repeat=1						,	number=5							)

             # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
             A__    =      timeit.repeat(
                 lowercase_						,	repeat=self.args.repeat						,	number=10						,	)

             return min(lowercase_							) / 1_0.0
         except ResourceExhaustedError as e:
             self.print_fn(F"Doesn't fit on GPU. {e}"							)



 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Callable[[], None]							)->							[Memory, MemorySummary]:
     logger.info(
         "Note that TensorFlow allocates more memory than "
         "it might need to speed up computation. "
         "The memory reported here corresponds to the memory "
         "reported by `nvidia-smi`, which can vary depending "
         "on total available memory on the GPU that is used."							)
     with self.args.strategy.scope():
         try:
             if self.args.trace_memory_line_by_line:
                 if not self.args.eager_mode:
                     raise ValueError(
                         "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
                         " consumption line by line."							)
                 A__    =      start_memory_tracing("transformers"							)

             if self.args.is_tpu:
                 # tpu
                 raise NotImplementedError(
                     "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
                     " with `args.memory=False`"							)
             elif self.args.is_gpu:
                 # gpu
                 if not is_pyanvml_available():
                     logger.warning(
                         "py3nvml not installed, we won't log GPU memory usage. "
                         "Install py3nvml (pip install py3nvml) to log information about GPU."							)
                     A__    =      "N/A"
                 else:
                     logger.info(
                         "Measuring total GPU usage on GPU device. Make sure to not have additional processes"
                         " running on the same GPU."							)
                     # init nvml
                     nvml.nvmlInit()
                     func()
                     A__    =      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
                     A__    =      nvml.nvmlDeviceGetMemoryInfo(lowercase_							)
                     A__    =      meminfo.used
                     A__    =      Memory(lowercase_							)
                     # shutdown nvml
                     nvml.nvmlShutdown()
             else:
                 # cpu
                 if self.args.trace_memory_line_by_line:
                     logger.info(
                         "When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
                         " TensorFlow."							)
                     A__    =      None
                 else:
                     A__    =      measure_peak_memory_cpu(lowercase_							)
                     A__    =      Memory(lowercase_							) if isinstance(lowercase_						,	lowercase_							) else memory_bytes
             if self.args.trace_memory_line_by_line:
                 A__    =      stop_memory_tracing(lowercase_							)
                 if memory is None:
                     A__    =      summary.total
             else:
                 A__    =      None

             return memory, summary
         except ResourceExhaustedError as e:
             self.print_fn(F"Doesn't fit on GPU. {e}"							)
             return "N/A", None










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple	):
    # Return True if there is node that has not iterated.
    A__    =      [False] * len(_lowerCamelCase	)
    A__    =      []
    queue.append(_lowerCamelCase	)
    A__    =      True

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

    return visited[t]





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]	):
    # This array is filled by BFS and to store path
    A__    =      [-1] * (len(_lowerCamelCase	))
    A__    =      0
    while bfs(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      float("Inf"	)
        A__    =      sink

        while s != source:
            # Find the minimum value in select path
            A__    =      min(_lowerCamelCase						,	graph[parent[s]][s]	)
            A__    =      parent[s]

        max_flow += path_flow
        A__    =      sink

        while v != source:
            A__    =      parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            A__    =      parent[v]
    return max_flow


__lowerCAmelCase							:     Any		     =[
    [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],
]

__lowerCAmelCase     ,					__lowerCAmelCase							:     int		     =0, 5



print(ford_fulkerson(graph, source, sink))



