

"""simple docstring"""




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


A_	:			Dict  	=      logging.get_logger(__name__)

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			int )					->				Dict:
	try:
		with open(lowercase_   ,				"""rb""" ) as flax_state_f:
			A__							:   Optional[Any]					       =		from_bytes(lowercase_   ,				flax_state_f.read() )
	except UnpicklingError as e:
		try:
			with open(lowercase_ ) as f:
				if f.read().startswith("""version""" ):
					raise OSError(
					    """You seem to have cloned a repository without having git-lfs installed. Please"""
					    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
					    """ folder you cloned.""" )
				else:
					raise ValueError from e
		except (UnicodeDecodeError, ValueError):
			raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """ )

	return load_flax_weights_in_pytorch_model(lowercase_   ,				lowercase_ )


def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Dict )					->				Optional[int]:

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
		    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
		    """ instructions.""" )
		raise

	# check if we have bf16 weights
	A__							:   List[Any]					       =		flatten_dict(jax.tree_util.tree_map(lambda lowercase_ : x.dtype == jnp.bfloataa   ,				lowercase_ ) ).values()
	if any(lowercase_ ):
		# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

		# and bf16 is not fully supported in PT yet.
		logger.warning(
		    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
		    """before loading those in PyTorch model.""" )
		A__							:   Union[str, Any]					       =		jax.tree_util.tree_map(
		    lambda lowercase_ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params   ,				lowercase_ )

	A__							:   str					       =		""""""

	A__							:   Tuple					       =		flatten_dict(lowercase_   ,				sep=""".""" )
	A__							:   Dict					       =		pt_model.state_dict()

	# keep track of unexpected & missing keys
	A__							:   int					       =		[]
	A__							:   List[str]					       =		set(pt_model_dict.keys() )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		A__							:   Tuple					       =		flax_key_tuple.split(""".""" )

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			A__							:   Optional[Any]					       =		flax_key_tuple_array[:-1] + ["""weight"""]
			A__							:   Tuple					       =		jnp.transpose(lowercase_   ,				(3, 2, 0, 1) )
		elif flax_key_tuple_array[-1] == "kernel":
			A__							:   int					       =		flax_key_tuple_array[:-1] + ["""weight"""]
			A__							:   str					       =		flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			A__							:   List[str]					       =		flax_key_tuple_array[:-1] + ["""weight"""]

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(lowercase_ ):
				A__							:   List[Any]					       =		(
				    flax_key_tuple_string.replace("""_0"""   ,				""".0""" )
				    .replace("""_1"""   ,				""".1""" )
				    .replace("""_2"""   ,				""".2""" )
				    .replace("""_3"""   ,				""".3""" )
				    .replace("""_4"""   ,				""".4""" )
				    .replace("""_5"""   ,				""".5""" )
				    .replace("""_6"""   ,				""".6""" )
				    .replace("""_7"""   ,				""".7""" )
				    .replace("""_8"""   ,				""".8""" )
				    .replace("""_9"""   ,				""".9""" )
				)

		A__							:   Optional[int]					       =		""".""".join(lowercase_ )

		if flax_key in pt_model_dict:
			if flax_tensor.shape != pt_model_dict[flax_key].shape:
				raise ValueError(
				    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
				    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
			else:
				# add weight to pytorch dict
				A__							:   Tuple					       =		np.asarray(lowercase_ ) if not isinstance(lowercase_   ,				np.ndarray ) else flax_tensor
				A__							:   Tuple					       =		torch.from_numpy(lowercase_ )
				# remove from missing keys
				missing_keys.remove(lowercase_ )
		else:
			# weight is not expected by PyTorch model
			unexpected_keys.append(lowercase_ )

	pt_model.load_state_dict(lowercase_ )

	# re-transform missing_keys to list
	A__							:   str					       =		list(lowercase_ )

	if len(lowercase_ ) > 0:
		logger.warning(
		    """Some weights of the Flax model were not used when initializing the PyTorch model"""
		    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
		    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
		    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
		    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
		    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
		    """ FlaxBertForSequenceClassification model).""" )
	if len(lowercase_ ) > 0:
		logger.warning(
		    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
		    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
		    """ use it for predictions and inference.""" )

	return pt_model






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



