'''simple docstring'''




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		:		str    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]  ,				a		:		str    ):





         """simple docstring"""



         return self.lineara(self.batchnorm(self.lineara(a    )    )    )




class     a__      (   unittest.TestCase ):






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



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

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

                 __lowerCamelCase		=  load_offloaded_weight(a  ,				index['''weight''']    )
                 self.assertTrue(torch.equal(a  ,				a    )    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __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(a  ,				a    )
             __lowerCamelCase		=  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]    )    )

         __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(a  ,				a    )
             __lowerCamelCase		=  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
             __lowerCamelCase		=  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    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



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

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



import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


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





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

