


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


lowercase_	   =     logging.get_logger(__name__)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
	__lowerCamelCase		:							Tuple		 =						r'\w+[.]\d+'
	__lowerCamelCase		:							List[str]		 =						re.findall(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
	for pat in pats:
		__lowerCamelCase		:							Optional[int]		 =						key.replace(SCREAMING_SNAKE_CASE__      ,	'_'.join(pat.split('.'						)						)						)
	return key
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
	__lowerCamelCase		:							str		 =						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		:							Union[str, 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:
		__lowerCamelCase		:							Optional[int]		 =						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		:							List[Any]		 =						pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

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

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

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

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

	# Step 2: Since the model is stateless, get random Flax params
	__lowerCamelCase		:							List[Any]		 =						flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE__						)						)

	__lowerCamelCase		:							Tuple		 =						flatten_dict(SCREAMING_SNAKE_CASE__						)
	__lowerCamelCase		:							Any		 =						{}

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

		# Correctly rename weight parameters
		__lowerCamelCase		:							Any		 =						rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

		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		:							int		 =						jnp.asarray(SCREAMING_SNAKE_CASE__						)

	return unflatten_dict(SCREAMING_SNAKE_CASE__						)





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						prime_factors(SCREAMING_SNAKE_CASE__						)
 if is_square_free(SCREAMING_SNAKE_CASE__						):
  return -1 if len(SCREAMING_SNAKE_CASE__						) % 2 else 1
 return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()

