






'''simple docstring'''



import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)
class 				__SCREAMING_SNAKE_CASE							(				nn.Module  ):




      def __init__(	self   )				->List[Any]:



         '''simple docstring'''
         super().__init__()
         __a					  =     nn.Linear(3			, 4   )
         __a					  =     nn.BatchNormad(4   )
         __a					  =     nn.Linear(4			, 5   )






      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         return self.lineara(self.batchnorm(self.lineara(lowerCamelCase   )   )   )







class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     ModelForTest()
         with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowerCamelCase			, model.state_dict()   )
            __a					  =     os.path.join(lowerCamelCase			, 'index.json'   )
            self.assertTrue(os.path.isfile(lowerCamelCase   )   )
            # TODO: add tests on what is inside the index

            for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
               __a					  =     os.path.join(lowerCamelCase			, F"""{key}.dat"""   )
               self.assertTrue(os.path.isfile(lowerCamelCase   )   )
               # TODO: add tests on the fact weights are properly loaded




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     [torch.floataa, torch.floataa, torch.bfloataa]

         for dtype in dtypes:
            __a					  =     torch.randn(2			, 3			, dtype=lowerCamelCase   )
            with TemporaryDirectory() as tmp_dir:
               __a					  =     offload_weight(lowerCamelCase			, 'weight'			, lowerCamelCase			, {}   )
               __a					  =     os.path.join(lowerCamelCase			, 'weight.dat'   )
               self.assertTrue(os.path.isfile(lowerCamelCase   )   )
               self.assertDictEqual(lowerCamelCase			, {'weight': {'shape': [2, 3], 'dtype': str(lowerCamelCase   ).split('.'   )[1]}}   )

               __a					  =     load_offloaded_weight(lowerCamelCase			, index['weight']   )
               self.assertTrue(torch.equal(lowerCamelCase			, lowerCamelCase   )   )




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     ModelForTest()
         __a					  =     model.state_dict()
         __a					  =     {k: v for k, v in state_dict.items() if 'linear2' not in k}
         __a					  =     {k: v for k, v in state_dict.items() if 'linear2' in k}

         with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowerCamelCase			, lowerCamelCase   )
            __a					  =     OffloadedWeightsLoader(state_dict=lowerCamelCase			, save_folder=lowerCamelCase   )

            # Every key is there with the right value
            self.assertEqual(sorted(lowerCamelCase   )			, sorted(state_dict.keys()   )   )
            for key, param in state_dict.items():
               self.assertTrue(torch.allclose(lowerCamelCase			, weight_map[key]   )   )

         __a					  =     {k: v for k, v in state_dict.items() if 'weight' in k}
         __a					  =     {k: v for k, v in state_dict.items() if 'weight' not in k}

         with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowerCamelCase			, lowerCamelCase   )
            __a					  =     OffloadedWeightsLoader(state_dict=lowerCamelCase			, save_folder=lowerCamelCase   )

            # Every key is there with the right value
            self.assertEqual(sorted(lowerCamelCase   )			, sorted(state_dict.keys()   )   )
            for key, param in state_dict.items():
               self.assertTrue(torch.allclose(lowerCamelCase			, weight_map[key]   )   )

         with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowerCamelCase			, lowerCamelCase   )
            # Duplicates are removed
            __a					  =     OffloadedWeightsLoader(state_dict=lowerCamelCase			, save_folder=lowerCamelCase   )

            # Every key is there with the right value
            self.assertEqual(sorted(lowerCamelCase   )			, sorted(state_dict.keys()   )   )
            for key, param in state_dict.items():
               self.assertTrue(torch.allclose(lowerCamelCase			, weight_map[key]   )   )






      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     {'a.1': 0, 'a.10': 1, 'a.2': 2}
         __a					  =     extract_submodules_state_dict(lowerCamelCase			, ['a.1', 'a.2']   )
         self.assertDictEqual(lowerCamelCase			, {'a.1': 0, 'a.2': 2}   )

         __a					  =     {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
         __a					  =     extract_submodules_state_dict(lowerCamelCase			, ['a.1', 'a.2']   )
         self.assertDictEqual(lowerCamelCase			, {'a.1.a': 0, 'a.2.a': 2}   )







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       np.inf     )
      __a					  =     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)     )
         __a					  =     new_cost_f
         __a					  =     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            __a					  =     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __a					  =     -1

   __a					  =     set()
   __a					  =     set()
   __a					  =     {source: 0}
   __a					  =     {destination: 0}
   __a					  =     {source: None}
   __a					  =     {destination: None}
   __a					  =     PriorityQueue()
   __a					  =     PriorityQueue()

   __a					  =     np.inf

   queue_forward.put((0, source)     )
   queue_backward.put((0, destination)     )

   if source == destination:
      return 0

   while not queue_forward.empty() and not queue_backward.empty():
      __a  ,      __a					  =     queue_forward.get()
      visited_forward.add(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
         break

   if shortest_distance != np.inf:
      __a					  =     shortest_distance
   return shortest_path_distance


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()