




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	:	str				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   snake_case				:    Optional[Any]           =	R"""\w+[.]\d+"""
   snake_case				:    List[str]           =	re.findall(lowercase						,lowercase					)
   for pat in pats:
      snake_case				:    List[Any]           =	key.replace(lowercase						,"""_""".join(pat.split("""."""					)					)					)
   return key




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Dict:
   snake_case				:    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)
   ):
      snake_case				:    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:
      snake_case				:    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:
      snake_case				:    Any           =	pt_tuple_key[:-1] + ("""embedding""",)
      return renamed_pt_tuple_key, pt_tensor

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

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

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

   # old PyTorch layer norm bias
   snake_case				:    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__ (   lowercase						,lowercase						,lowercase=42					)  ->       Any:
   # Step 1: Convert pytorch tensor to numpy
   snake_case				:    int           =	{k: v.numpy() for k, v in pt_state_dict.items()}

   # Step 2: Since the model is stateless, get random Flax params
   snake_case				:    List[str]           =	flax_model.init_weights(PRNGKey(lowercase					)					)

   snake_case				:    Any           =	flatten_dict(lowercase					)
   snake_case				:    Optional[Any]           =	{}

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      snake_case				:    Tuple           =	rename_key(lowercase					)
      snake_case				:    List[str]           =	tuple(renamed_pt_key.split("""."""					)					)

      # Correctly rename weight parameters
      snake_case     , snake_case				:    Optional[Any]           =	rename_key_and_reshape_tensor(lowercase						,lowercase						,lowercase					)

      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
      snake_case				:    Union[str, Any]           =	jnp.asarray(lowercase					)

   return unflatten_dict(lowercase					)











from collections import defaultdict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       bool:
   snake_case				:    List[str]           =	first_str.lower().strip()
   snake_case				:    List[str]           =	second_str.lower().strip()

   # Remove whitespace
   snake_case				:    Any           =	first_str.replace(""" """						,""""""					)
   snake_case				:    List[str]           =	second_str.replace(""" """						,""""""					)

   # Strings of different lengths are not anagrams
   if len(lowercase					) != len(lowercase					):
      return False

   # Default values for count should be 0
   snake_case				:    defaultdict[str, int]           =	defaultdict(lowercase					)

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(lowercase					)					):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       lowerCamelCase	:	List[Any]				=   input('Enter the first string ').strip()
       lowerCamelCase	:	Optional[int]				=   input('Enter the second string ').strip()

       lowerCamelCase	:	Optional[Any]				=   check_anagrams(input_a, input_b)
       print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





