


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 :   Union[str, Any]							      =				logging.get_logger(__name__)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[int]:
    try:
        with open(lowerCamelCase_							,				'rb'				) as flax_state_f:
            _lowercase :							List[str]   	=	from_bytes(lowerCamelCase_							,				flax_state_f.read()				)
    except UnpicklingError as e:
        try:
            with open(lowerCamelCase_				) 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(lowerCamelCase_							,				lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		List[str]:

    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
    _lowercase :							Union[str, Any]   	=	flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase_				: x.dtype == jnp.bfloataa							,				lowerCamelCase_				)				).values()
    if any(lowerCamelCase_				):
        # 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.'				)
        _lowercase :							int   	=	jax.tree_util.tree_map(
            lambda lowerCamelCase_				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params							,				lowerCamelCase_				)

    _lowercase :							Optional[int]   	=	''

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

    # keep track of unexpected & missing keys
    _lowercase :							Tuple   	=	[]
    _lowercase :							List[Any]   	=	set(pt_model_dict.keys()				)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        _lowercase :							str   	=	flax_key_tuple.split('.'				)

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

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(lowerCamelCase_				):
                _lowercase :							Union[str, Any]   	=	(
                    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'				)
                )

        _lowercase :							Dict   	=	'.'.join(lowerCamelCase_				)

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

    pt_model.load_state_dict(lowerCamelCase_				)

    # re-transform missing_keys to list
    _lowercase :							Optional[int]   	=	list(lowerCamelCase_				)

    if len(lowerCamelCase_				) > 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(lowerCamelCase_				) > 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






def        UpperCamelCase_(   lowerCamelCase_				)					->		int:
    assert (
        isinstance(lowerCamelCase_							,				lowerCamelCase_				) and number_of_steps > 0
    ), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
    if number_of_steps == 1:
        return 1
    _lowercase	,       _lowercase :							Dict   	=	1, 1
    for _ in range(number_of_steps - 1				):
        _lowercase	,       _lowercase :							Tuple   	=	current + previous, current
    return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()


