



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 						__a (     nn.Module				):



	def __init__(						self							:    Union[str, Any])->							Any:
						super().__init__()
						__lowerCAmelCase	   =nn.Linear(3				,	4)
						__lowerCAmelCase	   =nn.BatchNormad(4)
						__lowerCAmelCase	   =nn.Linear(4				,	5)



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    int)->							str:
						return self.lineara(self.batchnorm(self.lineara(snake_case_)))






class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Optional[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =ModelForTest()
						with TemporaryDirectory() as tmp_dir:
											offload_state_dict(snake_case_				,	model.state_dict())
											__lowerCAmelCase	   =os.path.join(snake_case_				,	"""index.json""")
											self.assertTrue(os.path.isfile(snake_case_))
											# TODO: add tests on what is inside the index

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



	def 		UpperCamelCase							(						self							:    Any)->							int:
						__lowerCAmelCase	   =[torch.floataa, torch.floataa, torch.bfloataa]

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

																__lowerCAmelCase	   =load_offloaded_weight(snake_case_				,	index["""weight"""])
																self.assertTrue(torch.equal(snake_case_				,	snake_case_))



	def 		UpperCamelCase							(						self							:    List[Any])->							List[Any]:
						__lowerCAmelCase	   =ModelForTest()
						__lowerCAmelCase	   =model.state_dict()
						__lowerCAmelCase	   ={k: v for k, v in state_dict.items() if """linear2""" not in k}
						__lowerCAmelCase	   ={k: v for k, v in state_dict.items() if """linear2""" in k}

						with TemporaryDirectory() as tmp_dir:
											offload_state_dict(snake_case_				,	snake_case_)
											__lowerCAmelCase	   =OffloadedWeightsLoader(state_dict=snake_case_				,	save_folder=snake_case_)

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

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

						with TemporaryDirectory() as tmp_dir:
											offload_state_dict(snake_case_				,	snake_case_)
											__lowerCAmelCase	   =OffloadedWeightsLoader(state_dict=snake_case_				,	save_folder=snake_case_)

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

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

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



	def 		UpperCamelCase							(						self							:    Optional[int])->							Any:
						__lowerCAmelCase	   ={"""a.1""": 0, """a.10""": 1, """a.2""": 2}
						__lowerCAmelCase	   =extract_submodules_state_dict(snake_case_				,	["""a.1""", """a.2"""])
						self.assertDictEqual(snake_case_				,	{"""a.1""": 0, """a.2""": 2})

						__lowerCAmelCase	   ={"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
						__lowerCAmelCase	   =extract_submodules_state_dict(snake_case_				,	["""a.1""", """a.2"""])
						self.assertDictEqual(snake_case_				,	{"""a.1.a""": 0, """a.2.a""": 2})









import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
				lowercase_	       =			{
				    '''linear''': PIL.Image.Resampling.BILINEAR,
				    '''bilinear''': PIL.Image.Resampling.BILINEAR,
				    '''bicubic''': PIL.Image.Resampling.BICUBIC,
				    '''lanczos''': PIL.Image.Resampling.LANCZOS,
				    '''nearest''': PIL.Image.Resampling.NEAREST,
				}
else:
				lowercase_	       =			{
				    '''linear''': PIL.Image.LINEAR,
				    '''bilinear''': PIL.Image.BILINEAR,
				    '''bicubic''': PIL.Image.BICUBIC,
				    '''lanczos''': PIL.Image.LANCZOS,
				    '''nearest''': PIL.Image.NEAREST,
				}






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						int:
					__lowerCAmelCase	   =(images / 2 + 0.5).clamp(0				,   1	)
					__lowerCAmelCase	   =images.cpu().permute(0				,   2				,   3				,   1	).float().numpy()
					__lowerCAmelCase	   =numpy_to_pil(__lowerCamelCase	)
					return images






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						str:
					if images.ndim == 3:
										__lowerCAmelCase	   =images[None, ...]
					__lowerCAmelCase	   =(images * 255).round().astype("""uint8"""	)
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										__lowerCAmelCase	   =[Image.fromarray(image.squeeze()				,   mode="""L"""	) for image in images]
					else:
										__lowerCAmelCase	   =[Image.fromarray(__lowerCamelCase	) for image in images]

					return pil_images




