



from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					):
			lowercase__					,    lowercase__   					=			coefficient_matrix.shape
			lowercase__					,    lowercase__   					=			constant_matrix.shape

			if rowsa != colsa:
						lowercase__   					=			f'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
						raise ValueError(SCREAMING_SNAKE_CASE_					)

			if colsa != 1:
						lowercase__   					=			f'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
						raise ValueError(SCREAMING_SNAKE_CASE_					)

			if rowsa != rowsa:
						lowercase__   					=			(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    f'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
						)
						raise ValueError(SCREAMING_SNAKE_CASE_					)

			if len(SCREAMING_SNAKE_CASE_					) != rowsa:
						lowercase__   					=			(
						    "Number of initial values must be equal to number of rows in coefficient "
						    f'''matrix but received {len(SCREAMING_SNAKE_CASE_					)} and {rowsa}'''
						)
						raise ValueError(SCREAMING_SNAKE_CASE_					)

			if iterations <= 0:
						raise ValueError("Iterations must be at least 1"					)

			lowercase__   					=			np.concatenate(
			    (coefficient_matrix, constant_matrix)  ,					axis=1					)

			lowercase__					,    lowercase__   					=			table.shape

			strictly_diagonally_dominant(SCREAMING_SNAKE_CASE_					)

			# Iterates the whole matrix for given number of times
			for _ in range(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			[]
						for row in range(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			0
									for col in range(SCREAMING_SNAKE_CASE_					):
												if col == row:
															lowercase__   					=			table[row][col]
												elif col == cols - 1:
															lowercase__   					=			table[row][col]
												else:
															temp += (-1) * table[row][col] * init_val[col]
									lowercase__   					=			(temp + val) / denom
									new_val.append(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			new_val

			return [float(SCREAMING_SNAKE_CASE_					) for i in new_val]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__					,    lowercase__   					=			table.shape

			lowercase__   					=			True

			for i in range(0  ,					SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			0
						for j in range(0  ,					cols - 1					):
									if i == j:
												continue
									else:
												total += table[i][j]

						if table[i][i] <= total:
									raise ValueError("Coefficient matrix is not strictly diagonally dominant"					)

			return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
							import doctest

							doctest.testmod()









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_					)




