"""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     :     str= logging.get_logger(__name__)
def   __UpperCAmelCase				(    UpperCAmelCase_    :				int  )							->			List[str]:

	'''simple docstring'''





	__snake_case			:							Dict      		= r'\w+[.]\d+'
	__snake_case			:							Tuple      		= re.findall(A__			,							A__  )
	for pat in pats:
		__snake_case			:							Tuple      		= key.replace(A__			,							'_'.join(pat.split('.'  )  )  )
	return key
def   __UpperCAmelCase				(    UpperCAmelCase_    :				str			,							UpperCAmelCase_    :				str			,							UpperCAmelCase_    :				Dict  )							->			List[Any]:

	'''simple docstring'''





	__snake_case			:							List[str]      		= 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)
	):
		__snake_case			:							Optional[int]      		= 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:
		__snake_case			:							Dict      		= 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:
		__snake_case			:							str      		= pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

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

	# linear layer
	__snake_case			:							Optional[int]      		= pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight":
		__snake_case			:							List[str]      		= pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

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

	# old PyTorch layer norm bias
	__snake_case			:							Dict      		= 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				(    UpperCAmelCase_    :				int			,							UpperCAmelCase_    :				int			,							UpperCAmelCase_    :				int=42  )							->			Optional[Any]:

	'''simple docstring'''





	__snake_case			:							Optional[Any]      		= {k: v.numpy() for k, v in pt_state_dict.items()}

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

	__snake_case			:							List[Any]      		= flatten_dict(A__  )
	__snake_case			:							Optional[Any]      		= {}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		__snake_case			:							Any      		= rename_key(A__  )
		__snake_case			:							List[str]      		= tuple(renamed_pt_key.split('.'  )  )

		# Correctly rename weight parameters
		__snake_case		,				__snake_case			:							List[str]      		= rename_key_and_reshape_tensor(A__			,							A__			,							A__  )

		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
		__snake_case			:							Dict      		= jnp.asarray(A__  )

	return unflatten_dict(A__  )








'''simple docstring'''






from __future__ import annotations

from decimal import Decimal

from numpy import array


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = Decimal

 # Check if the provided matrix has 2 rows and 2 columns
 # since this implementation only works for 2x2 matrices
 if len(A__      ) == 2 and len(matrix[0]      ) == 2 and len(matrix[1]      ) == 2:
  # Calculate the determinant of the matrix
  __lowercase							  = float(
      d(matrix[0][0]      ) * d(matrix[1][1]      ) - d(matrix[1][0]      ) * d(matrix[0][1]      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creates a copy of the matrix with swapped positions of the elements
  __lowercase							  = [[0.0, 0.0], [0.0, 0.0]]
  __lowercase  ,	__lowercase							  = matrix[1][1], matrix[0][0]
  __lowercase  ,	__lowercase							  = -matrix[1][0], -matrix[0][1]

  # Calculate the inverse of the matrix
  return [
      [(float(d(A__      )      ) / determinant) or 0.0 for n in row] for row in swapped_matrix
  ]
 elif (
     len(A__      ) == 3
     and len(matrix[0]      ) == 3
     and len(matrix[1]      ) == 3
     and len(matrix[2]      ) == 3
 ):
  # Calculate the determinant of the matrix using Sarrus rule
  __lowercase							  = float(
      (
          (d(matrix[0][0]      ) * d(matrix[1][1]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][2]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][2]      ) * d(matrix[1][0]      ) * d(matrix[2][1]      ))
      )
      - (
          (d(matrix[0][2]      ) * d(matrix[1][1]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][0]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][0]      ) * d(matrix[1][2]      ) * d(matrix[2][1]      ))
      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creating cofactor matrix
  __lowercase							  = [
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
  ]
  __lowercase							  = (d(matrix[1][1]      ) * d(matrix[2][2]      )) - (
      d(matrix[1][2]      ) * d(matrix[2][1]      )
  )
  __lowercase							  = -(
      (d(matrix[1][0]      ) * d(matrix[2][2]      )) - (d(matrix[1][2]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[1][0]      ) * d(matrix[2][1]      )) - (
      d(matrix[1][1]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][1]      ) * d(matrix[2][2]      )) - (d(matrix[0][2]      ) * d(matrix[2][1]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[2][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[2][1]      )) - (d(matrix[0][1]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[0][1]      ) * d(matrix[1][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[1][1]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[1][2]      )) - (d(matrix[0][2]      ) * d(matrix[1][0]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[1][1]      )) - (
      d(matrix[0][1]      ) * d(matrix[1][0]      )
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    __lowercase							  = cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    inverse_matrix[i][j] /= d(A__      )

        # Calculate the inverse of the matrix
  return [[float(d(A__      )      ) or 0.0 for n in row] for row in inverse_matrix]
 raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''      )

