


'''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


a_						:       List[str]      			=				logging.get_logger(__name__)





def __snake_case      (   UpperCAmelCase_		:		List[str]	):
 lowerCamelCase_  				=						r"\w+[.]\d+"
 lowerCamelCase_  				=						re.findall(UpperCAmelCase_      ,       UpperCAmelCase_	)
 for pat in pats:
  lowerCamelCase_  				=						key.replace(UpperCAmelCase_      ,       "_".join(pat.split("."	)	)	)
 return key





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict	):
 lowerCamelCase_  				=						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)
 ):
  lowerCamelCase_  				=						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:
  lowerCamelCase_  				=						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:
  lowerCamelCase_  				=						pt_tuple_key[:-1] + ("embedding",)
  return renamed_pt_tuple_key, pt_tensor

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

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

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

 # old PyTorch layer norm bias
 lowerCamelCase_  				=						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 __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		str=42	):
 # Step 1: Convert pytorch tensor to numpy
 lowerCamelCase_  				=						{k: v.numpy() for k, v in pt_state_dict.items()}

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

 lowerCamelCase_  				=						flatten_dict(UpperCAmelCase_	)
 lowerCamelCase_  				=						{}

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

  # Correctly rename weight parameters
  lowerCamelCase_				,lowerCamelCase_  				=						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
  lowerCamelCase_  				=						jnp.asarray(UpperCAmelCase_	)

 return unflatten_dict(UpperCAmelCase_	)







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 return "\n".join(
     F'''{number} * {i} = {number * i}''' for i in range(1      ,       number_of_terms + 1	)	)


if __name__ == "__main__":
     print(multiplication_table(number=5, number_of_terms=10))



