


'''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


__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a     )						->			List[str]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[int]							      =				R'''\w+[.]\d+'''
					UpperCamelCase__     :Tuple							      =				re.findall(__a				,      __a     )
					for pat in pats:
										UpperCamelCase__     :Dict							      =				key.replace(__a				,      '''_'''.join(pat.split('''.'''     )     )     )
					return key


def     a   (    __a				,      __a				,      __a     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Optional[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)
					):
										UpperCamelCase__     :Tuple							      =				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__     :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:
										UpperCamelCase__     :Optional[int]							      =				pt_tuple_key[:-1] + ('''embedding''',)
										return renamed_pt_tuple_key, pt_tensor

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

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

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

					# old PyTorch layer norm bias
					UpperCamelCase__     :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     a   (    __a				,      __a				,      __a=42     )						->			Optional[Any]:



					'''simple docstring'''






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

					# Step 2: Since the model is stateless, get random Flax params
					UpperCamelCase__     :Optional[int]							      =				flax_model.init_weights(PRNGKey(__a     )     )

					UpperCamelCase__     :int							      =				flatten_dict(__a     )
					UpperCamelCase__     :Union[str, Any]							      =				{}

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

										# Correctly rename weight parameters
										UpperCamelCase__      ,       UpperCamelCase__     :Any							      =				rename_key_and_reshape_tensor(__a				,      __a				,      __a     )

										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__     :Optional[Any]							      =				jnp.asarray(__a     )

					return unflatten_dict(__a     )



'''simple docstring'''
import sys

__snake_case							   =  (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)


def     a   (    __a     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				1
					for digit in s:
										product *= int(__a     )
					return product


def     a   (    __a = N     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				-sys.maxsize - 1
					UpperCamelCase__     :Dict							      =				n[:13]
					UpperCamelCase__     :Dict							      =				13
					while cur_index < len(__a     ) - 13:
										if int(n[cur_index]     ) >= int(substr[0]     ):
															UpperCamelCase__     :Dict							      =				substr[1:] + n[cur_index]
															cur_index += 1
										else:
															UpperCamelCase__     :List[str]							      =				max(__a				,      str_eval(__a     )     )
															UpperCamelCase__     :Union[str, Any]							      =				n[cur_index : cur_index + 13]
															cur_index += 13
					return largest_product


if __name__ == "__main__":
		print(F"""{solution() = }""")