





'''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					:		Dict		=							logging.get_logger(__name__)




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[str]					     =     R'''\w+[.]\d+'''
      _snake_case		:	Dict					     =     re.findall(lowerCAmelCase_			,      lowerCAmelCase_	)
      for pat in pats:
            _snake_case		:	int					     =     key.replace(lowerCAmelCase_			,      '''_'''.join(pat.split('''.'''	)	)	)
      return key




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[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		:	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:
            _snake_case		:	Union[str, 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		:	Union[str, Any]					     =     pt_tuple_key[:-1] + ('''embedding''',)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      _snake_case		:	int					     =     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		:	Union[str, Any]					     =     pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      _snake_case		:	List[str]					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=42	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     {k: v.numpy() for k, v in pt_state_dict.items()}

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

      _snake_case		:	Dict					     =     flatten_dict(lowerCAmelCase_	)
      _snake_case		:	int					     =     {}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            _snake_case		:	Union[str, Any]					     =     rename_key(lowerCAmelCase_	)
            _snake_case		:	List[str]					     =     tuple(renamed_pt_key.split('''.'''	)	)

            # Correctly rename weight parameters
            _snake_case     ,							_snake_case		:	Dict					     =     rename_key_and_reshape_tensor(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

            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		:	Optional[int]					     =     jnp.asarray(lowerCAmelCase_	)

      return unflatten_dict(lowerCAmelCase_	)









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     0
      if start < end:
            _snake_case		:	List[Any]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Any					     =     a[end]
            _snake_case		:	List[str]					     =     a[pivot]
            _snake_case		:	Optional[int]					     =     temp

            _snake_case     ,							_snake_case		:	List[Any]					     =     _in_place_partition(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      lowerCAmelCase_			,      p - 1	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      p + 1			,      lowerCAmelCase_	)
      return count




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     0
      _snake_case		:	Optional[int]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     a[end]
      _snake_case		:	Optional[Any]					     =     a[pivot]
      _snake_case		:	Union[str, Any]					     =     temp
      _snake_case		:	Union[str, Any]					     =     start - 1
      for index in range(lowerCAmelCase_			,      lowerCAmelCase_	):
            count += 1
            if a[index] < a[end]:  # check if current val is less than pivot value
                  _snake_case		:	Optional[int]					     =     new_pivot_index + 1
                  _snake_case		:	Optional[Any]					     =     a[new_pivot_index]
                  _snake_case		:	Tuple					     =     a[index]
                  _snake_case		:	str					     =     temp

      _snake_case		:	Any					     =     a[new_pivot_index + 1]
      _snake_case		:	str					     =     a[end]
      _snake_case		:	Optional[int]					     =     temp
      return new_pivot_index + 1, count


UpperCAmelCase					:		Dict		=							TemporaryFile()
UpperCAmelCase					:		Dict		=							1_0_0  # 1000 elements are to be sorted


UpperCAmelCase,       UpperCAmelCase					:		str		=							0, 1  # mean and standard deviation
UpperCAmelCase					:		Optional[Any]		=							np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase					:		int		=							np.load(outfile)
UpperCAmelCase					:		Optional[int]		=							len(M) - 1
UpperCAmelCase					:		str		=							_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)
print(z)


