




"""simple docstring"""




from __future__ import annotations

from typing import Any






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				pass






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					data
							lowerCamelCase			      =					None





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self
							lowerCamelCase			      =					[]
							while node:
										if node in visited:
													raise ContainsLoopError
										visited.append(_a )
										yield node.data
										lowerCamelCase			      =					node.next_node





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							try:
										list(self )
										return False
							except ContainsLoopError:
										return True


if __name__ == "__main__":
					lowerCAmelCase							:			Tuple     							=							Node(1)
					lowerCAmelCase							:			Optional[Any]     							=							Node(2)
					lowerCAmelCase							:			int     							=							Node(3)
					lowerCAmelCase							:			Union[str, Any]     							=							Node(4)
					print(root_node.has_loop)  # False
					lowerCAmelCase							:			Tuple     							=							root_node.next_node
					print(root_node.has_loop)  # True

					lowerCAmelCase							:			Optional[int]     							=							Node(5)
					lowerCAmelCase							:			str     							=							Node(6)
					lowerCAmelCase							:			List[Any]     							=							Node(5)
					lowerCAmelCase							:			Dict     							=							Node(6)
					print(root_node.has_loop)  # False

					lowerCAmelCase							:			str     							=							Node(1)
					print(root_node.has_loop)  # False









"""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


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:
			try:
						with open(snake_case__       , """rb"""				) as flax_state_f:
									lowerCamelCase			      =					from_bytes(snake_case__       , flax_state_f.read()				)
			except UnpicklingError as e:
						try:
									with open(snake_case__				) 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(snake_case__       , snake_case__				)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:

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

			lowerCamelCase			      =					""""""

			lowerCamelCase			      =					flatten_dict(snake_case__       , sep="""."""				)
			lowerCamelCase			      =					pt_model.state_dict()

			# keep track of unexpected & missing keys
			lowerCamelCase			      =					[]
			lowerCamelCase			      =					set(pt_model_dict.keys()				)

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

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					jnp.transpose(snake_case__       , (3, 2, 0, 1)				)
						elif flax_key_tuple_array[-1] == "kernel":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(snake_case__				):
												lowerCamelCase			      =					(
												    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"""				)
												)

						lowerCamelCase			      =					""".""".join(snake_case__				)

						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
												lowerCamelCase			      =					np.asarray(snake_case__				) if not isinstance(snake_case__       , np.ndarray				) else flax_tensor
												lowerCamelCase			      =					torch.from_numpy(snake_case__				)
												# remove from missing keys
												missing_keys.remove(snake_case__				)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(snake_case__				)

			pt_model.load_state_dict(snake_case__				)

			# re-transform missing_keys to list
			lowerCamelCase			      =					list(snake_case__				)

			if len(snake_case__				) > 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(snake_case__				) > 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



