



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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			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)
			):
						lowercase__   					=			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:
						lowercase__   					=			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:
						lowercase__   					=			pt_tuple_key[:-1] + ("embedding",)
						return renamed_pt_tuple_key, pt_tensor

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

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

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

			# old PyTorch layer norm bias
			lowercase__   					=			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	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=42					):
			# Step 1: Convert pytorch tensor to numpy
			lowercase__   					=			{k: v.numpy() for k, v in pt_state_dict.items()}

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

			lowercase__   					=			flatten_dict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}

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

						# Correctly rename weight parameters
						lowercase__					,    lowercase__   					=			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
						lowercase__   					=			jnp.asarray(SCREAMING_SNAKE_CASE_					)

			return unflatten_dict(SCREAMING_SNAKE_CASE_					)









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			1  # To kept the Calculated Value
			# Since C(n, k) = C(n, n-k)
			if k > (n - k):
						lowercase__   					=			n - k
			# Calculate C(n,k)
			for i in range(SCREAMING_SNAKE_CASE_					):
						result *= n - i
						result //= i + 1
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return binomial_coefficient(2 * node_count  ,					SCREAMING_SNAKE_CASE_					) // (node_count + 1)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if n < 0:
						raise ValueError("factorial() not defined for negative values"					)
			lowercase__   					=			1
			for i in range(1  ,					n + 1					):
						result *= i
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return catalan_number(SCREAMING_SNAKE_CASE_					) * factorial(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							lowercase_						=       int(input("""Enter the number of nodes: """).strip() or 0)
							if node_count <= 0:
														raise ValueError("""We need some nodes to work with.""")
							print(
							    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
							    F'binary trees and {catalan_number(node_count)} binary search trees.'
							)




