






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  _UpperCAmelCase   (			nn.Module      ):

				"""simple docstring"""




				def __init__(	self		:       Optional[Any]    ):

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




				def 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       List[Any]    ):

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



class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     ModelForTest()
								with TemporaryDirectory() as tmp_dir:
												offload_state_dict(lowerCamelCase,    model.state_dict()    )
												lowercase__  					=     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"]:
																lowercase__  					=     os.path.join(lowerCamelCase,    F"""{key}.dat"""    )
																self.assertTrue(os.path.isfile(lowerCamelCase    )    )
																# TODO: add tests on the fact weights are properly loaded




				def 							lowercase__  (	self		:       Tuple    ):

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

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

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




				def 							lowercase__  (	self		:       int    ):

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

								with TemporaryDirectory() as tmp_dir:
												offload_state_dict(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     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]    )    )

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

								with TemporaryDirectory() as tmp_dir:
												offload_state_dict(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     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
												lowercase__  					=     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 							lowercase__  (	self		:       Any    ):

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

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












def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for data in source_data:
								for i, el in enumerate(lowerCamelCase_							):
												if len(lowerCamelCase_							) < i + 1:
																data_lists.append([]							)
												data_lists[i].append(float(lowerCamelCase_							)							)
				return data_lists






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for dlist, weight in zip(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     min(lowerCamelCase_							)
								lowercase__  					=     max(lowerCamelCase_							)

								lowercase__  					=     []
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind))							)
																except ZeroDivisionError:
																				score.append(1							)

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind)							)
																except ZeroDivisionError:
																				score.append(0							)

        # weight not 0 or 1
								else:
												lowercase__  					=     F"""Invalid weight of {weight:f} provided"""
												raise ValueError(lowerCamelCase_							)

								score_lists.append(lowerCamelCase_							)

				return score_lists






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     [0 for i in range(len(score_lists[0]							)							)]

				for slist in score_lists:
								for j, ele in enumerate(lowerCamelCase_							):
												lowercase__  					=     final_scores[j] + ele

				return final_scores






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_data(lowerCamelCase_							)
				lowercase__  					=     calculate_each_score(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     generate_final_scores(lowerCamelCase_							)

				# append scores to source data
				for i, ele in enumerate(lowerCamelCase_							):
								source_data[i].append(lowerCamelCase_							)

				return source_data




