


import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu







class 			_lowercase (							nn.Module):


       """simple docstring"""




       def __init__(   self							:			Any       ):


             '''simple docstring'''



             super().__init__()
             lowerCamelCase__		:				Union[str, Any]    =  nn.Linear(3					,      4       )
             lowerCamelCase__		:				Dict    =  nn.BatchNormad(4       )
             lowerCamelCase__		:				List[Any]    =  nn.Linear(4					,      5       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase       )       )       )







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Union[str, Any]					,      *__lowerCamelCase							:			Tuple					,      **__lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             return (args[0] + 1,) + args[1:], kwargs







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             return output + 1







class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  ModelForTest()
             lowerCamelCase__		:				List[Any]    =  ModelHook()

             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )
             self.assertEqual(test_model._hf_hook					,      __lowerCamelCase       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "_old_forward"       )       )

             # Check adding the hook did not change the name or the signature
             self.assertEqual(test_model.forward.__name__					,      "forward"       )
             self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       )					,      ["x"]       )

             remove_hook_from_module(__lowerCamelCase       )
             self.assertFalse(hasattr(__lowerCamelCase					,      "_hf_hook"       )       )
             self.assertFalse(hasattr(__lowerCamelCase					,      "_old_forward"       )       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  ModelForTest()
             lowerCamelCase__		:				str    =  ModelHook()

             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase					,      append=__lowerCamelCase       )

             self.assertEqual(isinstance(test_model._hf_hook					,      __lowerCamelCase       )					,      __lowerCamelCase       )
             self.assertEqual(len(test_model._hf_hook.hooks       )					,      2       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "_old_forward"       )       )

             # Check adding the hook did not change the name or the signature
             self.assertEqual(test_model.forward.__name__					,      "forward"       )
             self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       )					,      ["x"]       )

             remove_hook_from_module(__lowerCamelCase       )
             self.assertFalse(hasattr(__lowerCamelCase					,      "_hf_hook"       )       )
             self.assertFalse(hasattr(__lowerCamelCase					,      "_old_forward"       )       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  ModelForTest()
             lowerCamelCase__		:				Optional[int]    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				Optional[Any]    =  test_model(x + 1       )
             lowerCamelCase__		:				Optional[Any]    =  test_model(x + 2       )

             lowerCamelCase__		:				Tuple    =  PreForwardHook()
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  test_model(__lowerCamelCase       )
             self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1E-5       )       )

             # Attaching a hook to a model when it already has one replaces, does not chain
             lowerCamelCase__		:				Tuple    =  PreForwardHook()
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  test_model(__lowerCamelCase       )
             self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1E-5       )       )

             # You need to use the sequential hook to chain two or more hooks
             lowerCamelCase__		:				int    =  SequentialHook(PreForwardHook()					,      PreForwardHook()       )
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  test_model(__lowerCamelCase       )
             assert torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1E-5       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  ModelForTest()
             lowerCamelCase__		:				Union[str, Any]    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				str    =  test_model(__lowerCamelCase       )

             lowerCamelCase__		:				Any    =  PostForwardHook()
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  test_model(__lowerCamelCase       )
             self.assertTrue(torch.allclose(__lowerCamelCase					,      output + 1					,      atol=1E-5       )       )

             # Attaching a hook to a model when it already has one replaces, does not chain
             lowerCamelCase__		:				List[str]    =  PostForwardHook()
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )
             lowerCamelCase__		:				Any    =  test_model(__lowerCamelCase       )
             self.assertTrue(torch.allclose(__lowerCamelCase					,      output + 1					,      atol=1E-5       )       )

             # You need to use the sequential hook to chain two or more hooks
             lowerCamelCase__		:				Optional[int]    =  SequentialHook(PostForwardHook()					,      PostForwardHook()       )
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )

             lowerCamelCase__		:				Any    =  test_model(__lowerCamelCase       )
             assert torch.allclose(__lowerCamelCase					,      output + 2					,      atol=1E-5       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  ModelForTest()
             lowerCamelCase__		:				Dict    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				List[Any]    =  test_model(__lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  PostForwardHook()
             add_hook_to_module(__lowerCamelCase					,      __lowerCamelCase       )
             lowerCamelCase__		:				Dict    =  test_model(__lowerCamelCase       )
             self.assertTrue(torch.allclose(__lowerCamelCase					,      output + 1       )       )
             self.assertTrue(outputa.requires_grad       )

             lowerCamelCase__		:				Optional[int]    =  True
             lowerCamelCase__		:				List[str]    =  test_model(__lowerCamelCase       )
             self.assertFalse(outputa.requires_grad       )





       @require_multi_gpu
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  ModelForTest()
             # Everything is on CPU
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )

             # This will move each submodule on different devices
             add_hook_to_module(model.lineara					,      AlignDevicesHook(execution_device=0       )       )
             add_hook_to_module(model.batchnorm					,      AlignDevicesHook(execution_device=0       )       )
             add_hook_to_module(model.lineara					,      AlignDevicesHook(execution_device=1       )       )

             self.assertEqual(model.lineara.weight.device					,      torch.device(0       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device(0       )       )
             self.assertEqual(model.batchnorm.running_mean.device					,      torch.device(0       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device(1       )       )

             # We can still make a forward pass. The input does not need to be on any particular device
             lowerCamelCase__		:				Any    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				str    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      torch.device(1       )       )

             # We can add a general hook to put back output on same device as input.
             add_hook_to_module(__lowerCamelCase					,      AlignDevicesHook(io_same_device=__lowerCamelCase       )       )
             lowerCamelCase__		:				List[str]    =  torch.randn(2					,      3       ).to(0       )
             lowerCamelCase__		:				Union[str, Any]    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      torch.device(0       )       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  ModelForTest()

             # Everything is on CPU
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )

             # This will move each submodule on different devices
             lowerCamelCase__		:				Union[str, Any]    =  {"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

             add_hook_to_module(model.lineara					,      AlignDevicesHook(**__lowerCamelCase       )       )
             add_hook_to_module(model.batchnorm					,      AlignDevicesHook(**__lowerCamelCase       )       )
             add_hook_to_module(model.lineara					,      AlignDevicesHook(**__lowerCamelCase       )       )

             # Parameters have been offloaded, so on the meta device
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             # Buffers are not included in the offload by default, so are on the execution device
             lowerCamelCase__		:				Dict    =  torch.device(hook_kwargs["execution_device"]       )
             self.assertEqual(model.batchnorm.running_mean.device					,      __lowerCamelCase       )

             lowerCamelCase__		:				List[Any]    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				Optional[Any]    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      __lowerCamelCase       )

             # Removing hooks loads back the weights in the model.
             remove_hook_from_module(model.lineara       )
             remove_hook_from_module(model.batchnorm       )
             remove_hook_from_module(model.lineara       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )

             # Now test with buffers included in the offload
             lowerCamelCase__		:				Union[str, Any]    =  {
                 "execution_device": 0 if torch.cuda.is_available() else "cpu",
                 "offload": True,
                 "offload_buffers": True,
             }

             add_hook_to_module(model.lineara					,      AlignDevicesHook(**__lowerCamelCase       )       )
             add_hook_to_module(model.batchnorm					,      AlignDevicesHook(**__lowerCamelCase       )       )
             add_hook_to_module(model.lineara					,      AlignDevicesHook(**__lowerCamelCase       )       )

             # Parameters have been offloaded, so on the meta device, buffers included
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.running_mean.device					,      torch.device("meta"       )       )

             lowerCamelCase__		:				Optional[Any]    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				Any    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      __lowerCamelCase       )

             # Removing hooks loads back the weights in the model.
             remove_hook_from_module(model.lineara       )
             remove_hook_from_module(model.batchnorm       )
             remove_hook_from_module(model.lineara       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  ModelForTest()

             # Everything is on CPU
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )

             # This will move each submodule on different devices
             lowerCamelCase__		:				Optional[int]    =  0 if torch.cuda.is_available() else "cpu"
             attach_align_device_hook(__lowerCamelCase					,      execution_device=__lowerCamelCase					,      offload=__lowerCamelCase       )

             # Parameters have been offloaded, so on the meta device
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             # Buffers are not included in the offload by default, so are on the execution device
             lowerCamelCase__		:				Union[str, Any]    =  torch.device(__lowerCamelCase       )
             self.assertEqual(model.batchnorm.running_mean.device					,      __lowerCamelCase       )

             lowerCamelCase__		:				int    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				Union[str, Any]    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      __lowerCamelCase       )

             # Removing hooks loads back the weights in the model.
             remove_hook_from_submodules(__lowerCamelCase       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )

             # Now test with buffers included in the offload
             attach_align_device_hook(__lowerCamelCase					,      execution_device=__lowerCamelCase					,      offload=__lowerCamelCase					,      offload_buffers=__lowerCamelCase       )

             # Parameters have been offloaded, so on the meta device, buffers included
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.running_mean.device					,      torch.device("meta"       )       )

             lowerCamelCase__		:				int    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				Any    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      __lowerCamelCase       )

             # Removing hooks loads back the weights in the model.
             remove_hook_from_submodules(__lowerCamelCase       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  ModelForTest()

             # Everything is on CPU
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )

             # This will move each submodule on different devices
             lowerCamelCase__		:				Dict    =  0 if torch.cuda.is_available() else "cpu"
             attach_align_device_hook(
                 __lowerCamelCase					,      execution_device=__lowerCamelCase					,      offload=__lowerCamelCase					,      weights_map=model.state_dict()       )

             # Parameters have been offloaded, so on the meta device
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             # Buffers are not included in the offload by default, so are on the execution device
             lowerCamelCase__		:				Tuple    =  torch.device(__lowerCamelCase       )
             self.assertEqual(model.batchnorm.running_mean.device					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				List[str]    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      __lowerCamelCase       )

             # Removing hooks loads back the weights in the model.
             remove_hook_from_submodules(__lowerCamelCase       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )

             # Now test with buffers included in the offload
             attach_align_device_hook(
                 __lowerCamelCase					,      execution_device=__lowerCamelCase					,      offload=__lowerCamelCase					,      weights_map=model.state_dict()					,      offload_buffers=__lowerCamelCase					,      )

             # Parameters have been offloaded, so on the meta device, buffers included
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("meta"       )       )
             self.assertEqual(model.batchnorm.running_mean.device					,      torch.device("meta"       )       )

             lowerCamelCase__		:				Union[str, Any]    =  torch.randn(2					,      3       )
             lowerCamelCase__		:				int    =  model(__lowerCamelCase       )
             self.assertEqual(output.device					,      __lowerCamelCase       )

             # Removing hooks loads back the weights in the model.
             remove_hook_from_submodules(__lowerCamelCase       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.batchnorm.weight.device					,      torch.device("cpu"       )       )
             self.assertEqual(model.lineara.weight.device					,      torch.device("cpu"       )       )






from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


