





'''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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Tuple     ):



    '''simple docstring'''





    try:
        with open(SCREAMING_SNAKE_CASE_		,							"rb"     ) as flax_state_f:
            _lowerCAmelCase						      =   from_bytes(SCREAMING_SNAKE_CASE_		,							flax_state_f.read()     )
    except UnpicklingError as e:
        try:
            with open(SCREAMING_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(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )





def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''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
    _lowerCAmelCase						      =   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 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 SCREAMING_SNAKE_CASE_     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params		,							SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   ""

    _lowerCAmelCase						      =   flatten_dict(SCREAMING_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(SCREAMING_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(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE_     ) if not isinstance(SCREAMING_SNAKE_CASE_		,							np.ndarray     ) else flax_tensor
                _lowerCAmelCase						      =   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
    _lowerCAmelCase						      =   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)."     )
    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."     )

    return pt_model







'''simple docstring'''
def A__       (	UpperCAmelCase_ = 6_0_0_8_5_1_4_7_5_1_4_3				):

						try:
												_UpperCamelCase   :						str												=  int(UpperCAmelCase_				)
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.'				)
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.'				)
						_UpperCamelCase   :						int												=  1
						_UpperCamelCase   :						Optional[Any]												=  2
						while i * i <= n:
												while n % i == 0:
																		_UpperCamelCase   :						Union[str, Any]												=  i
																		n //= i
												i += 1
						if n > 1:
												_UpperCamelCase   :						Dict												=  n
						return int(UpperCAmelCase_				)


if __name__ == "__main__":
					print(F"""{solution() = }""")


