
"""simple docstring"""





from ....utils import logging


_a		       =							logging.get_logger(__name__)


class A_	(UpperCAmelCase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=None				,   lowercase_=2048  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    config.__dict__
     UpperCAmelCase_					:					Any			     =    modal_hidden_size
     if num_labels:
        UpperCAmelCase_					:					Union[str, Any]			     =    num_labels










"""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


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



def 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    try:
        with open(__a    ,				'''rb'''							) as flax_state_f:
            SCREAMING_SNAKE_CASE_      :  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 					_A			(__a    ,				__a							) ->       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
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flatten_dict(jax.tree_util.tree_map(lambda __a							: 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.'''							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jax.tree_util.tree_map(
            lambda __a							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params    ,				__a							)

    SCREAMING_SNAKE_CASE_      :  int    						=		''''''

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

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

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        SCREAMING_SNAKE_CASE_      :  Any    						=		flax_key_tuple.split('''.'''							)

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

        SCREAMING_SNAKE_CASE_      :  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
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.asarray(__a							) if not isinstance(__a    ,				np.ndarray							) else flax_tensor
                SCREAMING_SNAKE_CASE_      :  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
    SCREAMING_SNAKE_CASE_      :  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


