






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_ :						Tuple      =      logging.get_logger(__name__)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	R"""\w+[.]\d+"""
	UpperCamelCase     :int          =	re.findall(__magic_name__	,			__magic_name__					)
	for pat in pats:
		UpperCamelCase     :List[str]          =	key.replace(__magic_name__	,			"""_""".join(pat.split("""."""					)					)					)
	return key

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Optional[int]					)		->			Tuple:






	"""simple docstring"""



	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     :List[str]          =	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     :Optional[Any]          =	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     :Optional[Any]          =	pt_tuple_key[:-1] + ("""embedding""",)
		return renamed_pt_tuple_key, pt_tensor

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

	# linear layer
	UpperCamelCase     :Dict          =	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     :str          =	pt_tuple_key[:-1] + ("""weight""",)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	UpperCamelCase     :Optional[int]          =	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_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  Tuple=42					)		->			Union[str, Any]:






	"""simple docstring"""



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

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

	UpperCamelCase     :Any          =	flatten_dict(__magic_name__					)
	UpperCamelCase     :Optional[int]          =	{}

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

		# Correctly rename weight parameters
		UpperCamelCase     ,      UpperCamelCase     :Tuple          =	rename_key_and_reshape_tensor(__magic_name__	,			__magic_name__	,			__magic_name__					)

		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     :List[Any]          =	jnp.asarray(__magic_name__					)

	return unflatten_dict(__magic_name__					)








"""simple docstring"""





import string







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				None:
  for key in range(len(string.ascii_uppercase ) ):
    lowerCamelCase_         =						''
    for symbol in message:
      if symbol in string.ascii_uppercase:
        lowerCamelCase_         =						string.ascii_uppercase.find(_lowerCamelCase )
        lowerCamelCase_         =						num - key
        if num < 0:
          lowerCamelCase_         =						num + len(string.ascii_uppercase )
        lowerCamelCase_         =						translated + string.ascii_uppercase[num]
      else:
        lowerCamelCase_         =						translated + symbol
    print(F'''Decryption using Key #{key}: {translated}''' )







def      lowerCamelCase__       (							)      ->				None:
  lowerCamelCase_         =						input('Encrypted message: ' )
  lowerCamelCase_         =						message.upper()
  decrypt(_lowerCamelCase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




