


"""simple docstring"""







from __future__ import annotations




class 			UpperCamelCase_     :

	"""simple docstring"""





	def __init__(			self      :    List[str]      ,      UpperCAmelCase__      :    list[list[int]]       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    TypeError(
						    "Matrices must be formed from a list of zero or more lists containing at "
						    "least one and the same number of values, each of which must be of type "
						    "int or float."       )
						if len(_a       ) != 0:
											__SCREAMING_SNAKE_CASE						       =    len(rows[0]       )
											if cols == 0:
																raise error
											for row in rows:
																if len(_a       ) != cols:
																					raise error
																for value in row:
																					if not isinstance(_a      ,      (int, float)       ):
																										raise error
											__SCREAMING_SNAKE_CASE						       =    rows
						else:
											__SCREAMING_SNAKE_CASE						       =    []




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Union[str, Any]:
						return [[row[i] for row in self.rows] for i in range(len(self.rows[0]       )       )]




	@property
	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Optional[Any]:
						return len(self.rows       )




	@property
	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Tuple:
						return len(self.rows[0]       )




	@property
	def   UpperCAmelCase_				(			self      :    str       )	->     List[str]:
						return (self.num_rows, self.num_columns)




	@property
	def   UpperCAmelCase_				(			self      :    str       )	->     int:
						return self.order[0] == self.order[1]




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    [
						    [0 if column_num != row_num else 1 for column_num in range(self.num_rows       )]
						    for row_num in range(self.num_rows       )
						]
						return Matrix(_a       )




	def   UpperCAmelCase_				(			self      :    str       )	->     Any:
						if not self.is_square:
											return 0
						if self.order == (0, 0):
											return 1
						if self.order == (1, 1):
											return int(self.rows[0][0]       )
						if self.order == (2, 2):
											return int(
											    (self.rows[0][0] * self.rows[1][1])
											    - (self.rows[0][1] * self.rows[1][0])       )
						else:
											return sum(
											    self.rows[0][column] * self.cofactors().rows[0][column]
											    for column in range(self.num_columns       )       )




	def   UpperCAmelCase_				(			self      :    int       )	->     Union[str, Any]:
						return bool(self.determinant()       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    int       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    [
						    [
						        self.rows[other_row][other_column]
						        for other_column in range(self.num_columns       )
						        if other_column != column
						    ]
						    for other_row in range(self.num_rows       )
						    if other_row != row
						]
						return Matrix(_a       ).determinant()




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    int       )	->     Tuple:
						if (row + column) % 2 == 0:
											return self.get_minor(_a      ,      _a       )
						return -1 * self.get_minor(_a      ,      _a       )




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     str:
						return Matrix(
						    [
						        [self.get_minor(_a      ,      _a       ) for column in range(self.num_columns       )]
						        for row in range(self.num_rows       )
						    ]       )




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Tuple:
						return Matrix(
						    [
						        [
						            self.minors().rows[row][column]
						            if (row + column) % 2 == 0
						            else self.minors().rows[row][column] * -1
						            for column in range(self.minors().num_columns       )
						        ]
						        for row in range(self.minors().num_rows       )
						    ]       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    [
						    [self.cofactors().rows[column][row] for column in range(self.num_columns       )]
						    for row in range(self.num_rows       )
						]
						return Matrix(_a       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    self.determinant()
						if not determinant:
											raise TypeError("Only matrices with a non-zero determinant have an inverse"       )
						return self.adjugate() * (1 / determinant)




	def __repr__(			self      :    Tuple       )	->     int:
						return str(self.rows       )




	def __str__(			self      :    List[str]       )	->     Tuple:
						if self.num_rows == 0:
											return "[]"
						if self.num_rows == 1:
											return "[[" + ". ".join(str(self.rows[0]       )       ) + "]]"
						return (
						    "["
						    + "\n ".join(
						        [
						            "[" + ". ".join([str(_a       ) for value in row]       ) + ".]"
						            for row in self.rows
						        ]       )
						    + "]"
						)




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    list[int]      ,      UpperCAmelCase__      :    int | None = None       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    TypeError("Row must be a list containing all ints and/or floats"       )
						if not isinstance(_a      ,      _a       ):
											raise type_error
						for value in row:
											if not isinstance(_a      ,      (int, float)       ):
																raise type_error
						if len(_a       ) != self.num_columns:
											raise ValueError(
											    "Row must be equal in length to the other rows in the matrix"       )
						if position is None:
											self.rows.append(_a       )
						else:
											__SCREAMING_SNAKE_CASE						       =    self.rows[0:position] + [row] + self.rows[position:]




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    list[int]      ,      UpperCAmelCase__      :    int | None = None       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    TypeError(
						    "Column must be a list containing all ints and/or floats"       )
						if not isinstance(_a      ,      _a       ):
											raise type_error
						for value in column:
											if not isinstance(_a      ,      (int, float)       ):
																raise type_error
						if len(_a       ) != self.num_rows:
											raise ValueError(
											    "Column must be equal in length to the other columns in the matrix"       )
						if position is None:
											__SCREAMING_SNAKE_CASE						       =    [self.rows[i] + [column[i]] for i in range(self.num_rows       )]
						else:
											__SCREAMING_SNAKE_CASE						       =    [
											    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
											    for i in range(self.num_rows       )
											]




	def __eq__(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    object       )	->     str:
						if not isinstance(_a      ,      _a       ):
											return NotImplemented
						return self.rows == other.rows




	def __ne__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    object       )	->     Optional[Any]:
						return not self == other




	def __neg__(			self      :    List[str]       )	->     Optional[int]:
						return self * -1




	def __add__(			self      :    int      ,      UpperCAmelCase__      :    Matrix       )	->     List[str]:
						if self.order != other.order:
											raise ValueError("Addition requires matrices of the same order"       )
						return Matrix(
						    [
						        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns       )]
						        for i in range(self.num_rows       )
						    ]       )




	def __sub__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    Matrix       )	->     Optional[int]:
						if self.order != other.order:
											raise ValueError("Subtraction requires matrices of the same order"       )
						return Matrix(
						    [
						        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns       )]
						        for i in range(self.num_rows       )
						    ]       )




	def __mul__(			self      :    Tuple      ,      UpperCAmelCase__      :    Matrix | int | float       )	->     List[str]:
						if isinstance(_a      ,      (int, float)       ):
											return Matrix(
											    [[int(element * other       ) for element in row] for row in self.rows]       )
						elif isinstance(_a      ,      _a       ):
											if self.num_columns != other.num_rows:
																raise ValueError(
																    "The number of columns in the first matrix must "
																    "be equal to the number of rows in the second"       )
											return Matrix(
											    [
											        [Matrix.dot_product(_a      ,      _a       ) for column in other.columns()]
											        for row in self.rows
											    ]       )
						else:
											raise TypeError(
											    "A Matrix can only be multiplied by an int, float, or another matrix"       )




	def __pow__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    int       )	->     Tuple:
						if not isinstance(_a      ,      _a       ):
											raise TypeError("A Matrix can only be raised to the power of an int"       )
						if not self.is_square:
											raise ValueError("Only square matrices can be raised to a power"       )
						if other == 0:
											return self.identity()
						if other < 0:
											if self.is_invertable():
																return self.inverse() ** (-other)
											raise ValueError(
											    "Only invertable matrices can be raised to a negative power"       )
						__SCREAMING_SNAKE_CASE						       =    self
						for _ in range(other - 1       ):
											result *= self
						return result




	@classmethod
	def   UpperCAmelCase_				(			cls      :    Tuple      ,      UpperCAmelCase__      :    list[int]      ,      UpperCAmelCase__      :    list[int]       )	->     str:
						return sum(row[i] * column[i] for i in range(len(_a       )       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""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


a__    :							Union[str, Any]            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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

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

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

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

					# old PyTorch layer norm bias
					__SCREAMING_SNAKE_CASE						       =    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__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=42       ):




					'''simple docstring'''


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

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

					__SCREAMING_SNAKE_CASE						       =    flatten_dict(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    {}

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

										# Correctly rename weight parameters
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    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
										__SCREAMING_SNAKE_CASE						       =    jnp.asarray(lowerCAmelCase_       )

					return unflatten_dict(lowerCAmelCase_       )
