




"""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				=							logging.get_logger(__name__)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			try:
						with open(UpperCamelCase					,							"rb"				) as flax_state_f:
									__UpperCAmelCase						:			str												=       from_bytes(UpperCamelCase					,							flax_state_f.read()				)
			except UnpicklingError as e:
						try:
									with open(UpperCamelCase				) 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(UpperCamelCase					,							UpperCamelCase				)





def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""

			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
			__UpperCAmelCase						:			int												=       flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase				: x.dtype == jnp.bfloataa					,							UpperCamelCase				)				).values()
			if any(UpperCamelCase				):
						# 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."				)
						__UpperCAmelCase						:			Any												=       jax.tree_util.tree_map(
						    lambda UpperCamelCase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params					,							UpperCamelCase				)

			__UpperCAmelCase						:			List[str]												=       """"""

			__UpperCAmelCase						:			Dict												=       flatten_dict(UpperCamelCase					,							sep="."				)
			__UpperCAmelCase						:			int												=       pt_model.state_dict()

			# keep track of unexpected & missing keys
			__UpperCAmelCase						:			Tuple												=       []
			__UpperCAmelCase						:			Union[str, Any]												=       set(pt_model_dict.keys()				)

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

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

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(UpperCamelCase				):
												__UpperCAmelCase						:			Dict												=       (
												    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"				)
												)

						__UpperCAmelCase						:			List[Any]												=       """.""".join(UpperCamelCase				)

						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
												__UpperCAmelCase						:			Any												=       np.asarray(UpperCamelCase				) if not isinstance(UpperCamelCase					,							np.ndarray				) else flax_tensor
												__UpperCAmelCase						:			List[str]												=       torch.from_numpy(UpperCamelCase				)
												# remove from missing keys
												missing_keys.remove(UpperCamelCase				)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(UpperCamelCase				)

			pt_model.load_state_dict(UpperCamelCase				)

			# re-transform missing_keys to list
			__UpperCAmelCase						:			Optional[int]												=       list(UpperCamelCase				)

			if len(UpperCamelCase				) > 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(UpperCamelCase				) > 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 collections import defaultdict





def 				A__ (snake_case   :		int				)     ->						int:
					__UpperCamelCase					:	Optional[int]             =      1
					__UpperCamelCase					:	Tuple             =      True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(snake_case				)
					if ret % 2 == 0:
										cuts.append(snake_case				)
					return ret





def 				A__ ()     ->						str:
					dfs(1				)


if __name__ == "__main__":
	a__     ,							a__		     =     10, 9
	a__		     =     defaultdict(list)
	a__		     =     {}
	a__		     =     []
	a__		     =     0
	a__		     =     [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
	for u, v in edges:
		tree[u].append(v)
		tree[v].append(u)
	even_tree()
	print(len(cuts) - 1)






