
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


SCREAMING_SNAKE_CASE				:List[Any]									=							logging.get_logger(__name__)


def 		UpperCAmelCase     (      a_		)						->      List[str]:







				"""simple docstring"""



				__A       =					r"\w+[.]\d+"
				__A       =					re.findall(__UpperCamelCase						,  __UpperCamelCase		)
				for pat in pats:
								__A       =					key.replace(__UpperCamelCase						,  "_".join(pat.split("."		)		)		)
				return key


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



				__A       =					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)
				):
								__A       =					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:
								__A       =					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:
								__A       =					pt_tuple_key[:-1] + ("embedding",)
								return renamed_pt_tuple_key, pt_tensor

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

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

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

				# old PyTorch layer norm bias
				__A       =					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     (      a_						,  a_						,  a_=4_2		)						->      Optional[int]:







				"""simple docstring"""



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

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

				__A       =					flatten_dict(__UpperCamelCase		)
				__A       =					{}

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

								# Correctly rename weight parameters
								__A     ,				__A       =					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
								__A       =					jnp.asarray(__UpperCamelCase		)

				return unflatten_dict(__UpperCamelCase		)





def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						A__	  =     _modexpt(__UpperCamelCase			,						exponent // 2			,						__UpperCamelCase       ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(__UpperCamelCase			,						exponent - 1			,						__UpperCamelCase       )) % modulo_value


def A    (							__UpperCamelCase = 1_777			,						__UpperCamelCase = 1_855			,						__UpperCamelCase = 8       )    ->   int:
			A__	  =     base
			for _ in range(1			,						__UpperCamelCase       ):
						A__	  =     _modexpt(__UpperCamelCase			,						__UpperCamelCase			,						10**digits       )

			return result


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




