






'''simple docstring'''







from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





		_UpperCamelCase				  =					42
		_UpperCamelCase				  =					42




class 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  [[] for _ in range(_lowerCAmelCase       )]
								lowerCamelCase__         =  size






		def __getitem__(      self   ,_lowerCAmelCase       ):
								return iter(self._graph[vertex]       )






		@property
		def 			UpperCamelCase_					(      self       ):
								return self._size






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								if weight not in (0, 1):
														raise ValueError("""Edge weight must be either 0 or 1."""       )

								if to_vertex < 0 or to_vertex >= self.size:
														raise ValueError("""Vertex indexes must be in [0; size)."""       )

								self._graph[from_vertex].append(Edge(_lowerCAmelCase   ,_lowerCAmelCase       )       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  deque([start_vertex]       )
								lowerCamelCase__         =  [None] * self.size
								lowerCamelCase__         =  0

								while queue:
														lowerCamelCase__         =  queue.popleft()
														lowerCamelCase__         =  distances[current_vertex]
														if current_distance is None:
																				continue

														for edge in self[current_vertex]:
																				lowerCamelCase__         =  current_distance + edge.weight
																				lowerCamelCase__         =  distances[edge.destination_vertex]
																				if (
																				    isinstance(_lowerCAmelCase   ,_lowerCAmelCase       )
																				    and new_distance >= dest_vertex_distance
																				):
																										continue
																				lowerCamelCase__         =  new_distance
																				if edge.weight == 0:
																										queue.appendleft(edge.destination_vertex       )
																				else:
																										queue.append(edge.destination_vertex       )

								if distances[finish_vertex] is None:
														raise ValueError("""No path from start_vertex to finish_vertex."""       )

								return distances[finish_vertex]


if __name__ == "__main__":
	import doctest

	doctest.testmod()









'''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


UpperCamelCase    :      Union[str, Any]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  R"""\w+[.]\d+"""
						lowerCamelCase__         =  re.findall(__lowerCAmelCase   ,    __lowerCAmelCase						)
						for pat in pats:
												lowerCamelCase__         =  key.replace(__lowerCAmelCase   ,    """_""".join(pat.split("""."""						)						)						)
						return key
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple						):
						lowerCamelCase__         =  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)
						):
												lowerCamelCase__         =  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:
												lowerCamelCase__         =  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:
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""embedding""",)
												return renamed_pt_tuple_key, pt_tensor

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

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

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

						# old PyTorch layer norm bias
						lowerCamelCase__         =  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__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple=42						):
						# Step 1: Convert pytorch tensor to numpy
						lowerCamelCase__         =  {k: v.numpy() for k, v in pt_state_dict.items()}

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

						lowerCamelCase__         =  flatten_dict(__lowerCAmelCase						)
						lowerCamelCase__         =  {}

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

												# Correctly rename weight parameters
												lowerCamelCase__       ,			lowerCamelCase__         =  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
												lowerCamelCase__         =  jnp.asarray(__lowerCAmelCase						)

						return unflatten_dict(__lowerCAmelCase						)

