





'''simple docstring'''


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)


def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   R"\w+[.]\d+"
    _lowerCAmelCase						      =   re.findall(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    for pat in pats:
        _lowerCAmelCase						      =   key.replace(SCREAMING_SNAKE_CASE_		,							"_".join(pat.split("."     )     )     )
    return key


def 						__a(SCREAMING_SNAKE_CASE_	:       List[Any]		,							SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key     )
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        _lowerCAmelCase						      =   pt_tensor.transpose(2		,							3		,							1		,							0     )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        _lowerCAmelCase						      =   pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    _lowerCAmelCase						      =   pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor





def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       Any=42     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   {k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    _lowerCAmelCase						      =   flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE_     )     )

    _lowerCAmelCase						      =   flatten_dict(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   {}

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

        # Correctly rename weight parameters
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

        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}.'''     )

        # also add unexpected weight so that warning is thrown
        _lowerCAmelCase						      =   jnp.asarray(SCREAMING_SNAKE_CASE_     )

    return unflatten_dict(SCREAMING_SNAKE_CASE_     )




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


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

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


