"""simple docstring"""





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    str:
							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    """Loading a PyTorch model in Flax, 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

							if not is_sharded:
														_SCREAMING_SNAKE_CASE		:						int			  =    os.path.abspath(__SCREAMING_SNAKE_CASE  )
														logger.info(F"""Loading PyTorch weights from {pt_path}"""  )

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.load(__SCREAMING_SNAKE_CASE     ,	map_location="""cpu"""  )
														logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()  ):,} parameters."""  )

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    convert_pytorch_state_dict_to_flax(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							else:
														# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
														_SCREAMING_SNAKE_CASE		:						Any			  =    convert_pytorch_sharded_state_dict_to_flax(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							return flax_state_dict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)->    (Tuple[str], np.ndarray):

							def is_key_or_prefix_key_in_dict(__SCREAMING_SNAKE_CASE  ) -> bool:
														return len(set(__SCREAMING_SNAKE_CASE  ) & {key, (model_prefix,) + key}  ) > 0

							# layer norm
							_SCREAMING_SNAKE_CASE		:						str			  =    pt_tuple_key[:-1] + ("""scale""",)
							if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__SCREAMING_SNAKE_CASE  ):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer mean
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    pt_tuple_key[:-1] + ("""mean""",)
							if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__SCREAMING_SNAKE_CASE  ):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer var
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pt_tuple_key[:-1] + ("""var""",)
							if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__SCREAMING_SNAKE_CASE  ):
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    pt_tuple_key[:-1] + ("""embedding""",)
							if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__SCREAMING_SNAKE_CASE  ):
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							_SCREAMING_SNAKE_CASE		:						Any			  =    pt_tuple_key[:-1] + ("""kernel""",)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    pt_tensor.transpose(2     ,	3     ,	1     ,	0  )
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    pt_tuple_key[:-1] + ("""kernel""",)
							if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    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		:						Any			  =    pt_tuple_key[:-1] + ("""bias""",)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
							if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    pt_tuple_key[-2] + """_g"""
							elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    pt_tuple_key[-2] + """_v"""
							if name is not None:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    pt_tuple_key[:-3] + (name,)
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							# convert pytorch tensor to numpy
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {k: v.numpy() for k, v in pt_state_dict.items()}

							_SCREAMING_SNAKE_CASE		:						str			  =    flax_model.base_model_prefix

							# use params dict if the model contains batch norm layers
							if "params" in flax_model.params:
														_SCREAMING_SNAKE_CASE		:						str			  =    flax_model.params["""params"""]
							else:
														_SCREAMING_SNAKE_CASE		:						Any			  =    flax_model.params
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    flatten_dict(__SCREAMING_SNAKE_CASE  )

							# add batch_stats keys,values to dict
							if "batch_stats" in flax_model.params:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    flatten_dict(flax_model.params["""batch_stats"""]  )
														random_flax_state_dict.update(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {}

							_SCREAMING_SNAKE_CASE		:						Any			  =    (model_prefix not in flax_model_params) and (
							    model_prefix in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
							)
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    (model_prefix in flax_model_params) and (
							    model_prefix not in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
							)

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

														# remove base model prefix if necessary
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pt_tuple_key[0] == model_prefix
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pt_tuple_key[1:]

														# Correctly rename weight parameters
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    rename_key_and_reshape_tensor(
														    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														# add model prefix if necessary
														_SCREAMING_SNAKE_CASE		:						Any			  =    (model_prefix,) + flax_key in random_flax_state_dict
														if load_base_model_into_model_with_head and require_base_model_prefix:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    (model_prefix,) + flax_key

														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}."""  )

        # add batch stats if the model contains batchnorm layers
														if "batch_stats" in flax_model.params:
																					if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    jnp.asarray(__SCREAMING_SNAKE_CASE  )
																												continue
																					# remove num_batches_tracked key
																					if "num_batches_tracked" in flax_key[-1]:
																												flax_state_dict.pop(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																												continue

																					# also add unexpected weight so that warning is thrown
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    jnp.asarray(__SCREAMING_SNAKE_CASE  )

														else:
																					# also add unexpected weight so that warning is thrown
																					_SCREAMING_SNAKE_CASE		:						str			  =    jnp.asarray(__SCREAMING_SNAKE_CASE  )

							return unflatten_dict(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							import torch

							# Load the index
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    {}
							for shard_file in shard_filenames:
														# load using msgpack utils
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.load(__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    {k: v.numpy() for k, v in pt_state_dict.items()}

														_SCREAMING_SNAKE_CASE		:						Dict			  =    flax_model.base_model_prefix

														# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
														if "batch_stats" in flax_model.params:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    flax_model.params["""params"""]

																					_SCREAMING_SNAKE_CASE		:						Any			  =    flatten_dict(__SCREAMING_SNAKE_CASE  )
																					random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]  )  )
														else:
																					_SCREAMING_SNAKE_CASE		:						int			  =    flax_model.params
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    flatten_dict(__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						Any			  =    (model_prefix not in flax_model_params) and (
														    model_prefix in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
														)
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    (model_prefix in flax_model_params) and (
														    model_prefix not in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
														)
														# Need to change some parameters name to match Flax names
														for pt_key, pt_tensor in pt_state_dict.items():
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tuple(pt_key.split("""."""  )  )

																					# remove base model prefix if necessary
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    pt_tuple_key[0] == model_prefix
																					if load_model_with_head_into_base_model and has_base_model_prefix:
																												_SCREAMING_SNAKE_CASE		:						Tuple			  =    pt_tuple_key[1:]

																					# Correctly rename weight parameters
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    rename_key_and_reshape_tensor(
																					    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																					# add model prefix if necessary
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    (model_prefix,) + flax_key in random_flax_state_dict
																					if load_base_model_into_model_with_head and require_base_model_prefix:
																												_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    (model_prefix,) + flax_key

																					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}."""  )

            # add batch stats if the model contains batchnorm layers
																					if "batch_stats" in flax_model.params:
																												if "mean" in flax_key[-1]:
																																			_SCREAMING_SNAKE_CASE		:						Any			  =    jnp.asarray(__SCREAMING_SNAKE_CASE  )
																																			continue
																												if "var" in flax_key[-1]:
																																			_SCREAMING_SNAKE_CASE		:						Dict			  =    jnp.asarray(__SCREAMING_SNAKE_CASE  )
																																			continue
																												# remove num_batches_tracked key
																												if "num_batches_tracked" in flax_key[-1]:
																																			flax_state_dict.pop(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																																			continue

																												# also add unexpected weight so that warning is thrown
																												_SCREAMING_SNAKE_CASE		:						Any			  =    jnp.asarray(__SCREAMING_SNAKE_CASE  )

																					else:
																												# also add unexpected weight so that warning is thrown
																												_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    jnp.asarray(__SCREAMING_SNAKE_CASE  )
							return unflatten_dict(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Any			  =    os.path.abspath(__SCREAMING_SNAKE_CASE  )
							logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""  )

							# import correct flax class
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    getattr(__SCREAMING_SNAKE_CASE     ,	"""Flax""" + model.__class__.__name__  )

							# load flax weight dict
							with open(__SCREAMING_SNAKE_CASE     ,	"""rb"""  ) as state_f:
														try:
																					_SCREAMING_SNAKE_CASE		:						int			  =    from_bytes(__SCREAMING_SNAKE_CASE     ,	state_f.read()  )
														except UnpicklingError:
																					raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """  )

							return load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:

							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    """Loading a 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
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    flatten_dict(jax.tree_util.tree_map(lambda __SCREAMING_SNAKE_CASE  : x.dtype == jnp.bfloataa     ,	__SCREAMING_SNAKE_CASE  )  ).values()
							if any(__SCREAMING_SNAKE_CASE  ):
														# convert all weights to fp32 if the 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."""  )
														_SCREAMING_SNAKE_CASE		:						int			  =    jax.tree_util.tree_map(
														    lambda __SCREAMING_SNAKE_CASE  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    flatten_dict(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    pt_model.state_dict()

							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    (pt_model.base_model_prefix in flax_state) and (
							    pt_model.base_model_prefix not in {k.split("""."""  )[0] for k in pt_model_dict.keys()}
							)
							_SCREAMING_SNAKE_CASE		:						Dict			  =    (pt_model.base_model_prefix not in flax_state) and (
							    pt_model.base_model_prefix in {k.split("""."""  )[0] for k in pt_model_dict.keys()}
							)

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

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														_SCREAMING_SNAKE_CASE		:						Any			  =    flax_key_tuple[0] == pt_model.base_model_prefix
														_SCREAMING_SNAKE_CASE		:						Any			  =    """.""".join((pt_model.base_model_prefix,) + flax_key_tuple  ) in pt_model_dict

														# adapt flax_key to prepare for loading from/to base model only
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    flax_key_tuple[1:]
														elif load_base_model_into_model_with_head and require_base_model_prefix:
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    (pt_model.base_model_prefix,) + flax_key_tuple

														# rename flax weights to PyTorch format
														if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__SCREAMING_SNAKE_CASE  ) not in pt_model_dict:
																					# conv layer
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    flax_key_tuple[:-1] + ("""weight""",)
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    jnp.transpose(__SCREAMING_SNAKE_CASE     ,	(3, 2, 0, 1)  )
														elif flax_key_tuple[-1] == "kernel" and ".".join(__SCREAMING_SNAKE_CASE  ) not in pt_model_dict:
																					# linear layer
																					_SCREAMING_SNAKE_CASE		:						Any			  =    flax_key_tuple[:-1] + ("""weight""",)
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    flax_tensor.T
														elif flax_key_tuple[-1] in ["scale", "embedding"]:
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    flax_key_tuple[:-1] + ("""weight""",)

														# adding batch stats from flax batch norm to pt
														elif "mean" in flax_key_tuple[-1]:
																					_SCREAMING_SNAKE_CASE		:						int			  =    flax_key_tuple[:-1] + ("""running_mean""",)
														elif "var" in flax_key_tuple[-1]:
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    flax_key_tuple[:-1] + ("""running_var""",)

														if "batch_stats" in flax_state:
																					_SCREAMING_SNAKE_CASE		:						Any			  =    """.""".join(flax_key_tuple[1:]  )  # Remove the params/batch_stats header
														else:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    """.""".join(__SCREAMING_SNAKE_CASE  )

														# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
														_SCREAMING_SNAKE_CASE		:						Any			  =    {}
														# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
														for key in pt_model_dict:
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    key.split("""."""  )
																					_SCREAMING_SNAKE_CASE		:						int			  =    None
																					if key_components[-3::2] == ["parametrizations", "original0"]:
																												_SCREAMING_SNAKE_CASE		:						List[str]			  =    key_components[-2] + """_g"""
																					elif key_components[-3::2] == ["parametrizations", "original1"]:
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    key_components[-2] + """_v"""
																					if name is not None:
																												_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    key_components[:-3] + [name]
																												_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """.""".join(__SCREAMING_SNAKE_CASE  )
																												_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    key

														if flax_key in special_pt_names:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    special_pt_names[flax_key]

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

							pt_model.load_state_dict(__SCREAMING_SNAKE_CASE  )

							# re-transform missing_keys to list
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    list(__SCREAMING_SNAKE_CASE  )

							if len(__SCREAMING_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)."""  )
							else:
														logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""  )
							if len(__SCREAMING_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."""  )
							else:
														logger.warning(
														    F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
														    """If your task is similar to the task the model of the checkpoint was trained on, """
														    F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""  )

							return pt_model



"""simple docstring"""





def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:

							if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														raise TypeError("""only integers accepted as input"""  )
							else:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    str(abs(__SCREAMING_SNAKE_CASE  )  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    [list(__SCREAMING_SNAKE_CASE  ) for char in range(len(__SCREAMING_SNAKE_CASE  )  )]
														for index in range(len(__SCREAMING_SNAKE_CASE  )  ):
																					num_transpositions[index].pop(__SCREAMING_SNAKE_CASE  )
														return max(
														    int("""""".join(list(__SCREAMING_SNAKE_CASE  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


