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:			str	       =       logging.get_logger(__name__)
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Tuple						= r'''\w+[.]\d+'''
 lowercase__    :   Tuple						= re.findall(_UpperCamelCase		,					_UpperCamelCase					)
 for pat in pats:
  lowercase__    :   Any						= key.replace(_UpperCamelCase		,					'''_'''.join(pat.split('''.'''					)					)					)
 return key
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Tuple						= 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)
 ):
  lowercase__    :   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:
  lowercase__    :   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:
  lowercase__    :   Union[str, Any]						= pt_tuple_key[:-1] + ('''embedding''',)
  return renamed_pt_tuple_key, pt_tensor

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

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

 # old PyTorch layer norm weight
 lowercase__    :   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
 lowercase__    :   Optional[Any]						= 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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=42					):
 # Step 1: Convert pytorch tensor to numpy
 lowercase__    :   int						= {k: v.numpy() for k, v in pt_state_dict.items()}

 # Step 2: Since the model is stateless, get random Flax params
 lowercase__    :   Union[str, Any]						= flax_model.init_weights(PRNGKey(_UpperCamelCase					)					)

 lowercase__    :   str						= flatten_dict(_UpperCamelCase					)
 lowercase__    :   Tuple						= {}

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

  # Correctly rename weight parameters
  lowercase__			,							lowercase__    :   Tuple						= 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
  lowercase__    :   Optional[Any]						= jnp.asarray(_UpperCamelCase					)

 return unflatten_dict(_UpperCamelCase					)


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

