

'''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


_UpperCAmelCase  :		Any  =		logging.get_logger(__name__)




def 	UpperCamelCase   (       lowercase_						:    int	)		->	int:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    Optional[Any]     ,					lowercase_						:    Union[str, Any]	)		->	Tuple:




  '''simple docstring'''



  lowercase									=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)
  ):
    lowercase									=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:
    lowercase									=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:
    lowercase									=pt_tuple_key[:-1] + ('''embedding''',)
    return renamed_pt_tuple_key, pt_tensor

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

  # linear layer
  lowercase									=pt_tuple_key[:-1] + ('''kernel''',)
  if pt_tuple_key[-1] == "weight":
    lowercase									=pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm bias
  lowercase									=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 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    List[str]     ,					lowercase_						:    Dict=4_2	)		->	List[Any]:




  '''simple docstring'''



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

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

  lowercase									=flatten_dict(SCREAMING_SNAKE_CASE_	)
  lowercase									={}

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

    # Correctly rename weight parameters
    lowercase							, lowercase									=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
    lowercase									=jnp.asarray(SCREAMING_SNAKE_CASE_	)

  return unflatten_dict(SCREAMING_SNAKE_CASE_	)







def      A__       (    SCREAMING_SNAKE_CASE_   :      int				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  str:






   """simple docstring"""







   if a < 0 or b < 0:
      raise ValueError('''the value of both inputs must be positive'''						)

   _UpperCAmelCase           =   str(bin(SCREAMING_SNAKE_CASE_						)						)[2:]  # remove the leading "0b"
   _UpperCAmelCase           =   str(bin(SCREAMING_SNAKE_CASE_						)						)[2:]  # remove the leading "0b"

   _UpperCAmelCase           =   max(len(SCREAMING_SNAKE_CASE_						)				,	len(SCREAMING_SNAKE_CASE_						)						)

   return "0b" + "".join(
       str(int(char_a == '''1''' and char_b == '''1'''						)						)
       for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_						)				,	b_binary.zfill(SCREAMING_SNAKE_CASE_						)						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()