
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


lowerCamelCase     :							Optional[Any]    =logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     List[str]:
     UpperCamelCase__      :       int	    =  r"\w+[.]\d+"
     UpperCamelCase__      :       Any	    =  re.findall(__lowerCamelCase ,   __lowerCamelCase      )
     for pat in pats:
          UpperCamelCase__      :       Dict	    =  key.replace(__lowerCamelCase ,   "_".join(pat.split("."      )      )      )
     return key







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[int]:
     UpperCamelCase__      :       Optional[Any]	    =  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)
     ):
          UpperCamelCase__      :       int	    =  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:
          UpperCamelCase__      :       int	    =  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:
          UpperCamelCase__      :       str	    =  pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

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

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

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

     # old PyTorch layer norm bias
     UpperCamelCase__      :       Tuple	    =  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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=42      )			->     str:
     # Step 1: Convert pytorch tensor to numpy
     UpperCamelCase__      :       Optional[int]	    =  {k: v.numpy() for k, v in pt_state_dict.items()}

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

     UpperCamelCase__      :       List[str]	    =  flatten_dict(__lowerCamelCase      )
     UpperCamelCase__      :       List[Any]	    =  {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCamelCase__      :       Optional[int]	    =  rename_key(__lowerCamelCase      )
          UpperCamelCase__      :       Optional[Any]	    =  tuple(renamed_pt_key.split("."      )      )

          # Correctly rename weight parameters
          UpperCamelCase__      :       Optional[Any]	    =  rename_key_and_reshape_tensor(__lowerCamelCase ,   __lowerCamelCase ,   __lowerCamelCase      )

          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
          UpperCamelCase__      :       Tuple	    =  jnp.asarray(__lowerCamelCase      )

     return unflatten_dict(__lowerCamelCase      )







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     :							List[Any]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}





class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Union[str, Any]				=			'''bit'''
 _lowerCAmelCase	:	List[str]				=			['''preactivation''', '''bottleneck''']
 _lowerCAmelCase	:	Any				=			['''SAME''', '''VALID''']







 def __init__(		self :       str							,       SCREAMING_SNAKE_CASE :       Tuple=3							,       SCREAMING_SNAKE_CASE :       str=64							,       SCREAMING_SNAKE_CASE :       List[Any]=[2_56, 5_12, 10_24, 20_48]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=[3, 4, 6, 3]							,       SCREAMING_SNAKE_CASE :       str="preactivation"							,       SCREAMING_SNAKE_CASE :       Any="relu"							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=32							,       SCREAMING_SNAKE_CASE :       Tuple=0.0							,       SCREAMING_SNAKE_CASE :       Dict=False							,       SCREAMING_SNAKE_CASE :       int=32							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       List[str]=None							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      if layer_type not in self.layer_types:
           raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
      if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
                UpperCamelCase__      :       Any	    =  global_padding.upper()
           else:
                raise ValueError(F'Padding strategy {global_padding} not supported'						)
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       Dict	    =  embedding_size
      UpperCamelCase__      :       Tuple	    =  hidden_sizes
      UpperCamelCase__      :       Any	    =  depths
      UpperCamelCase__      :       Optional[int]	    =  layer_type
      UpperCamelCase__      :       int	    =  hidden_act
      UpperCamelCase__      :       str	    =  global_padding
      UpperCamelCase__      :       Any	    =  num_groups
      UpperCamelCase__      :       str	    =  drop_path_rate
      UpperCamelCase__      :       Optional[Any]	    =  embedding_dynamic_padding
      UpperCamelCase__      :       Tuple	    =  output_stride
      UpperCamelCase__      :       List[str]	    =  width_factor

      UpperCamelCase__      :       Any	    =  ["stem"] + [F'stage{idx}' for idx in range(1							,       len(SCREAMING_SNAKE_CASE						) + 1						)]
      UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,       out_indices=SCREAMING_SNAKE_CASE							,       stage_names=self.stage_names						)