





'''simple docstring'''

import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__lowerCAmelCase							:     Dict		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      r"\w+[.]\d+"
    A__    =      re.findall(_lowerCamelCase						,	_lowerCamelCase	)
    for pat in pats:
        A__    =      key.replace(_lowerCamelCase						,	"_".join(pat.split("."	)	)	)
    return key





def 						UpperCamelCase    (							_lowerCamelCase : Tuple						,	_lowerCamelCase : int						,	_lowerCamelCase : Any	):
    A__    =      pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key	)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        A__    =      pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        A__    =      pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        A__    =      pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    A__    =      pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        A__    =      pt_tensor.transpose(2						,	3						,	1						,	0	)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    A__    =      pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        A__    =      pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    A__    =      pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    A__    =      pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : str=42	):
    # Step 1: Convert pytorch tensor to numpy
    A__    =      {k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    A__    =      flax_model.init_weights(PRNGKey(_lowerCamelCase	)	)

    A__    =      flatten_dict(_lowerCamelCase	)
    A__    =      {}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        A__    =      rename_key(_lowerCamelCase	)
        A__    =      tuple(renamed_pt_key.split("."	)	)

        # Correctly rename weight parameters
        A__,     A__    =      rename_key_and_reshape_tensor(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."	)

        # also add unexpected weight so that warning is thrown
        A__    =      jnp.asarray(_lowerCamelCase	)

    return unflatten_dict(_lowerCamelCase	)










'''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))



