






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      return math.sqrt(sum(pow(a - b       ,							2) for a, b in zip(_UpperCamelCase       ,							_UpperCamelCase)))
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list[list[list[float] | float]]:
      """simple docstring"""

      if dataset.ndim != value_array.ndim:
            UpperCamelCase					       =  (
                'Wrong input data\'s dimensions... '
                F'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
            )
            raise ValueError(_UpperCamelCase)

      try:
            if dataset.shape[1] != value_array.shape[1]:
                  UpperCamelCase					       =  (
                      'Wrong input data\'s shape... '
                      F'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
                  )
                  raise ValueError(_UpperCamelCase)
      except IndexError:
            if dataset.ndim != value_array.ndim:
                  raise TypeError('Wrong shape')

      if dataset.dtype != value_array.dtype:
            UpperCamelCase					       =  (
                'Input data have different datatype... '
                F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
            )
            raise TypeError(_UpperCamelCase)

      UpperCamelCase					       =  []

      for value in value_array:
            UpperCamelCase					       =  euclidean(_UpperCamelCase       ,							dataset[0])
            UpperCamelCase					       =  dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  UpperCamelCase					       =  euclidean(_UpperCamelCase       ,							_UpperCamelCase)

                  if dist > temp_dist:
                        UpperCamelCase					       =  temp_dist
                        UpperCamelCase					       =  dataset_value.tolist()

            answer.append([vector, dist])

      return answer
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      return np.dot(_UpperCamelCase       ,							_UpperCamelCase) / (norm(_UpperCamelCase) * norm(_UpperCamelCase))


if __name__ == "__main__":
       import doctest

       doctest.testmod()











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


__magic_name__   :  Optional[int]        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  r'\w+[.]\d+'
      UpperCamelCase					       =  re.findall(_UpperCamelCase       ,							_UpperCamelCase)
      for pat in pats:
            UpperCamelCase					       =  key.replace(_UpperCamelCase       ,							'_'.join(pat.split('.')))
      return key
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

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

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

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

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

      # old PyTorch layer norm bias
      UpperCamelCase					       =  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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=42) ->			Optional[Any]:
      """simple docstring"""

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

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

      UpperCamelCase					       =  flatten_dict(_UpperCamelCase)
      UpperCamelCase					       =  {}

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

            # Correctly rename weight parameters
            UpperCamelCase       ,  UpperCamelCase					       =  rename_key_and_reshape_tensor(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

            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					       =  jnp.asarray(_UpperCamelCase)

      return unflatten_dict(_UpperCamelCase)



