




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


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      r'''\w+[.]\d+'''
     UpperCAmelCase_      :							int       =      re.findall(_lowercase			,						_lowercase				)
     for pat in pats:
          UpperCAmelCase_      :							List[Any]       =      key.replace(_lowercase			,						'''_'''.join(pat.split('''.'''				)				)				)
     return key







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      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[Any]       =      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_      :							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_      :							Tuple       =      pt_tuple_key[:-1] + ('''embedding''',)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCAmelCase_      :							List[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_      :							Tuple       =      pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCAmelCase_      :							Any       =      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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=42				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      {k: v.numpy() for k, v in pt_state_dict.items()}

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

     UpperCAmelCase_      :							List[Any]       =      flatten_dict(_lowercase				)
     UpperCAmelCase_      :							int       =      {}

     # 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(_lowercase				)
          UpperCAmelCase_      :							List[str]       =      tuple(renamed_pt_key.split('''.'''				)				)

          # Correctly rename weight parameters
          UpperCAmelCase_      :							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
          UpperCAmelCase_      :							int       =      jnp.asarray(_lowercase				)

     return unflatten_dict(_lowercase				)





def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def lowerCamelCase__       (      _lowercase			,						_lowercase=0				):

     '''simple docstring'''


     return sorted(_lowercase			,						key=lambda _lowercase				: x[column]				)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(points_counts - 1				):
          for j in range(i + 1			,						_lowercase				):
               UpperCAmelCase_      :							Optional[int]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[Any]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(min(6			,						points_counts - 1				)			,						_lowercase				):
          for j in range(max(0			,						i - 6				)			,						_lowercase				):
               UpperCAmelCase_      :							List[str]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[int]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase			,						_lowercase				)

     # recursion
     UpperCAmelCase_      :							Optional[int]       =      points_counts // 2
     UpperCAmelCase_      :							List[Any]       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[:mid]			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
     UpperCAmelCase_      :							Union[str, Any]       =      min(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							str       =      []
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
               cross_strip.append(_lowercase				)

     UpperCAmelCase_      :							Optional[Any]       =      dis_between_closest_in_strip(
         _lowercase			,						len(_lowercase				)			,						_lowercase				)
     return min(_lowercase			,						_lowercase				)







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      column_based_sort(_lowercase			,						column=0				)
     UpperCAmelCase_      :							List[Any]       =      column_based_sort(_lowercase			,						column=1				)
     return (
         closest_pair_of_points_sqr(
             _lowercase			,						_lowercase			,						_lowercase				)
     ) ** 0.5


if __name__ == "__main__":
     __a          =			[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
     print('Distance:', closest_pair_of_points(points, len(points)))