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


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







def     _a				(      lowercase__  :			List[Any] ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				Dict			=		r'\w+[.]\d+'
					SCREAMING_SNAKE_CASE__				:				Dict			=		re.findall(lowercase__     ,  lowercase__ )
					for pat in pats:
										SCREAMING_SNAKE_CASE__				:				int			=		key.replace(lowercase__     ,  '_'.join(pat.split('.' ) ) )
					return key







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			int     ,  lowercase__  :			List[Any] ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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)
					):
										SCREAMING_SNAKE_CASE__				:				int			=		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:
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		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:
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		pt_tuple_key[:-1] + ('embedding',)
										return renamed_pt_tuple_key, pt_tensor

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

					# linear layer
					SCREAMING_SNAKE_CASE__				:				int			=		pt_tuple_key[:-1] + ('kernel',)
					if pt_tuple_key[-1] == "weight":
										SCREAMING_SNAKE_CASE__				:				Dict			=		pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

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

					# old PyTorch layer norm bias
					SCREAMING_SNAKE_CASE__				:				Dict			=		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     _a				(      lowercase__  :			Tuple     ,  lowercase__  :			Tuple     ,  lowercase__  :			List[str]=42 ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{k: v.numpy() for k, v in pt_state_dict.items()}

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

					SCREAMING_SNAKE_CASE__				:				Any			=		flatten_dict(lowercase__ )
					SCREAMING_SNAKE_CASE__				:				List[Any]			=		{}

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										SCREAMING_SNAKE_CASE__				:				Any			=		rename_key(lowercase__ )
										SCREAMING_SNAKE_CASE__				:				Dict			=		tuple(renamed_pt_key.split('.' ) )

										# Correctly rename weight parameters
										SCREAMING_SNAKE_CASE__				:				Dict			=		rename_key_and_reshape_tensor(lowercase__     ,  lowercase__     ,  lowercase__ )

										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
										SCREAMING_SNAKE_CASE__				:				Dict			=		jnp.asarray(lowercase__ )

					return unflatten_dict(lowercase__ )




import heapq as hq
import math
from collections.abc import Iterator

class       snake_case					:




  def __init__(      self     :	str     , a_     :	str				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		str(id_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		None
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
       SCREAMING_SNAKE_CASE__				:				Any			=		[]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}  # {vertex:distance}




  def __lt__(      self     :	int     , a_     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       return self.key < other.key




  def __repr__(      self     :	Any				)->       Dict:




       """simple docstring"""
       return self.id




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	int				)->       List[str]:




       """simple docstring"""
       self.neighbors.append(a_				)




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		weight







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Tuple     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     graph[a - 1].add_neighbor(graph[b - 1] )
     graph[b - 1].add_neighbor(graph[a - 1] )
     # add the edges:
     graph[a - 1].add_edge(graph[b - 1]     ,  lowercase__ )
     graph[b - 1].add_edge(graph[a - 1]     ,  lowercase__ )







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     for u in graph:
          SCREAMING_SNAKE_CASE__				:				Dict			=		math.inf
          SCREAMING_SNAKE_CASE__				:				str			=		None
     SCREAMING_SNAKE_CASE__				:				List[str]			=		0
     SCREAMING_SNAKE_CASE__				:				int			=		graph[:]
     while q:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		min(lowercase__ )
          q.remove(lowercase__ )
          for v in u.neighbors:
               if (v in q) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				int			=		u
                    SCREAMING_SNAKE_CASE__				:				Any			=		u.edges[v.id]
     for i in range(1     ,  len(lowercase__ ) ):
          a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
     return a







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     for u in graph:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		math.inf
          SCREAMING_SNAKE_CASE__				:				int			=		None
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0

     SCREAMING_SNAKE_CASE__				:				Tuple			=		list(lowercase__ )
     hq.heapify(lowercase__ )

     while h:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hq.heappop(lowercase__ )
          for v in u.neighbors:
               if (v in h) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				List[str]			=		u
                    SCREAMING_SNAKE_CASE__				:				Dict			=		u.edges[v.id]
                    hq.heapify(lowercase__ )

     for i in range(1     ,  len(lowercase__ ) ):
          yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def     _a				(      ):







     '''simple docstring'''




if __name__ == "__main__":
      import doctest

      doctest.testmod()



