

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							):


					'''simple docstring'''






					def __init__(					self     ):
						super().__init__()
						A__				:       Dict	  =						nn.Linear(3       ,			4     )
						A__				:       int	  =						nn.BatchNormad(4     )
						A__				:       Tuple	  =						nn.Linear(4       ,			5     )






					def     __A     (					self       ,			A__     ):
						return self.lineara(self.batchnorm(self.lineara(A__     )     )     )



class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       Dict	  =						ModelForTest()
						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(A__       ,			model.state_dict()     )
							A__				:       Any	  =						os.path.join(A__       ,			"""index.json"""     )
							self.assertTrue(os.path.isfile(A__     )     )
							# TODO: add tests on what is inside the index

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






					def     __A     (					self     ):
						A__				:       List[str]	  =						[torch.floataa, torch.floataa, torch.bfloataa]

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

								A__				:       Dict	  =						load_offloaded_weight(A__       ,			index["""weight"""]     )
								self.assertTrue(torch.equal(A__       ,			A__     )     )






					def     __A     (					self     ):
						A__				:       Tuple	  =						ModelForTest()
						A__				:       Optional[int]	  =						model.state_dict()
						A__				:       Dict	  =						{k: v for k, v in state_dict.items() if """linear2""" not in k}
						A__				:       Optional[int]	  =						{k: v for k, v in state_dict.items() if """linear2""" in k}

						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(A__       ,			A__     )
							A__				:       Tuple	  =						OffloadedWeightsLoader(state_dict=A__       ,			save_folder=A__     )

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

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

						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(A__       ,			A__     )
							A__				:       Any	  =						OffloadedWeightsLoader(state_dict=A__       ,			save_folder=A__     )

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

						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(A__       ,			A__     )
							# Duplicates are removed
							A__				:       List[Any]	  =						OffloadedWeightsLoader(state_dict=A__       ,			save_folder=A__     )

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






					def     __A     (					self     ):
						A__				:       List[Any]	  =						{"""a.1""": 0, """a.10""": 1, """a.2""": 2}
						A__				:       List[Any]	  =						extract_submodules_state_dict(A__       ,			["""a.1""", """a.2"""]     )
						self.assertDictEqual(A__       ,			{"""a.1""": 0, """a.2""": 2}     )

						A__				:       Optional[int]	  =						{"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
						A__				:       List[str]	  =						extract_submodules_state_dict(A__       ,			["""a.1""", """a.2"""]     )
						self.assertDictEqual(A__       ,			{"""a.1.a""": 0, """a.2.a""": 2}     )






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



