



"""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


UpperCamelCase     =							logging.get_logger(__name__)

def       _lowerCamelCase				(							UpperCAmelCase_ :       List[str]     )	-> Tuple:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       int     )	-> 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       _lowerCamelCase				(							UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Dict=42     )	-> Any:






	"""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(__lowerCamelCase     )     )

	A__											=					flatten_dict(__lowerCamelCase     )
	A__											=					{}

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

		# Correctly rename weight parameters
		A__							,							A__											=					rename_key_and_reshape_tensor(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase     )

		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(__lowerCamelCase     )

	return unflatten_dict(__lowerCamelCase     )






'''simple docstring'''



import functools


def A (__lowerCamelCase :list[int]					,      __lowerCamelCase :list[int]						):

						# Validation
						if not isinstance(__lowerCamelCase					,      __lowerCamelCase						) or not all(isinstance(__lowerCamelCase					,      __lowerCamelCase						) for day in days						):
												raise ValueError("""The parameter days should be a list of integers"""						)

						if len(__lowerCamelCase						) != 3 or not all(isinstance(__lowerCamelCase					,      __lowerCamelCase						) for cost in costs						):
												raise ValueError("""The parameter costs should be a list of three integers"""						)

						if len(__lowerCamelCase						) == 0:
												return 0

						if min(__lowerCamelCase						) <= 0:
												raise ValueError("""All days elements should be greater than 0"""						)

						if max(__lowerCamelCase						) >= 366:
												raise ValueError("""All days elements should be less than 366"""						)

						_lowerCAmelCase    		=					set(__lowerCamelCase						)

						@functools.cache
						def dynamic_programming(__lowerCamelCase :int						) -> int:
												if index > 365:
																		return 0

												if index not in days_set:
																		return dynamic_programming(index + 1						)

												return min(
												    costs[0] + dynamic_programming(index + 1						)					,      costs[1] + dynamic_programming(index + 7						)					,      costs[2] + dynamic_programming(index + 30						)					,      )

						return dynamic_programming(1						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
