



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_:Optional[int]             =						logging.get_logger(__name__)




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Tuple:



    """simple docstring"""






    try:
        with open(__a      ,     """rb"""		) as flax_state_f:
            A :       Optional[int]          =   from_bytes(__a      ,     flax_state_f.read()		)
    except UnpicklingError as e:
        try:
            with open(__a		) 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(__a      ,     __a		)




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  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
    A :       Optional[int]          =   flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase		: x.dtype == jnp.bfloataa      ,     __a		)		).values()
    if any(__a		):
        # 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 :       Optional[Any]          =   jax.tree_util.tree_map(
            lambda _lowerCAmelCase		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params      ,     __a		)

    A :       int          =   ''''''

    A :       str          =   flatten_dict(__a      ,     sep="""."""		)
    A :       List[Any]          =   pt_model.state_dict()

    # keep track of unexpected & missing keys
    A :       str          =   []
    A :       Any          =   set(pt_model_dict.keys()		)

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

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            A :       Any          =   flax_key_tuple_array[:-1] + ['''weight''']
            A :       Optional[Any]          =   jnp.transpose(__a      ,     (3, 2, 0, 1)		)
        elif flax_key_tuple_array[-1] == "kernel":
            A :       Tuple          =   flax_key_tuple_array[:-1] + ['''weight''']
            A :       Optional[int]          =   flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            A :       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(__a		):
                A :       List[str]          =   (
                    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[Any]          =   '''.'''.join(__a		)

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

    pt_model.load_state_dict(__a		)

    # re-transform missing_keys to list
    A :       int          =   list(__a		)

    if len(__a		) > 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(__a		) > 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"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


