
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     lowerCamelCase      (       nn.Module					):







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







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE     						= nn.Linear(3   ,     4			)
				SCREAMING_SNAKE_CASE     						= nn.BatchNormad(4			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(4   ,     5			)







	def  snake_case__     (     self      :Any   ,     lowercase      :Optional[int]			)		->      Tuple:







				"""simple docstring"""






				return self.lineara(self.batchnorm(self.lineara(lowercase			)			)			)





class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :Optional[int]			)		->      int:







				"""simple docstring"""






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

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







	def  snake_case__     (     self      :int			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [torch.floataa, torch.floataa, torch.bfloataa]

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

										SCREAMING_SNAKE_CASE     						= load_offloaded_weight(lowercase   ,     index['''weight''']			)
										self.assertTrue(torch.equal(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :str			)		->      Tuple:







				"""simple docstring"""






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

				with TemporaryDirectory() as tmp_dir:
							offload_state_dict(lowercase   ,     lowercase			)
							SCREAMING_SNAKE_CASE     						= OffloadedWeightsLoader(state_dict=lowercase   ,     save_folder=lowercase			)

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

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

				with TemporaryDirectory() as tmp_dir:
							offload_state_dict(lowercase   ,     lowercase			)
							SCREAMING_SNAKE_CASE     						= OffloadedWeightsLoader(state_dict=lowercase   ,     save_folder=lowercase			)

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

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

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







	def  snake_case__     (     self      :Dict			)		->      Any:







				"""simple docstring"""






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

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

import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					   =       logging.get_logger(__name__)





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :List[Any]   ,     *lowercase      :List[str]   ,     **lowercase      :List[Any]			)		->      None:







				"""simple docstring"""






				warnings.warn(
				    '''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use YolosImageProcessor instead.'''   ,     lowercase   ,     )
				super().__init__(*lowercase   ,     **lowercase			)