

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def 		_lowercase (     lowercase__=None						):
      if subparsers is not None:
            __lowerCAmelCase					:						List[str]					=					subparsers.add_parser('''env'''						)
      else:
            __lowerCAmelCase					:						List[str]					=					argparse.ArgumentParser('''Accelerate env command'''						)

      parser.add_argument(
          '''--config_file'''   ,	default=lowercase__   ,	help='''The config file to use for the default values in the launching script.'''						)

      if subparsers is not None:
            parser.set_defaults(func=lowercase__						)
      return parser







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Dict					=					torch.__version__
      __lowerCAmelCase					:						Dict					=					torch.cuda.is_available()
      __lowerCAmelCase					:						Union[str, Any]					=					is_xpu_available()
      __lowerCAmelCase					:						int					=					is_npu_available()

      __lowerCAmelCase					:						Tuple					=					'''Not found'''
      # Get the default from the config file.
      if args.config_file is not None or os.path.isfile(lowercase__						):
            __lowerCAmelCase					:						List[Any]					=					load_config_from_file(args.config_file						).to_dict()

      __lowerCAmelCase					:						Optional[Any]					=					{
          '''`Accelerate` version''': version,
          '''Platform''': platform.platform(),
          '''Python version''': platform.python_version(),
          '''Numpy version''': np.__version__,
          '''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
          '''PyTorch XPU available''': str(lowercase__						),
          '''PyTorch NPU available''': str(lowercase__						),
          '''System RAM''': f"""{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB""",
      }
      if pt_cuda_available:
            __lowerCAmelCase					:						List[str]					=					torch.cuda.get_device_name()

      print('''\nCopy-and-paste the text below in your GitHub issue\n'''						)
      print('''\n'''.join([f"""- {prop}: {val}""" for prop, val in info.items()]						)						)

      print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:'''						)
      __lowerCAmelCase					:						Optional[int]					=					(
          '''\n'''.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
          if isinstance(lowercase__   ,	lowercase__						)
          else f"""\t{accelerate_config}"""
      )
      print(lowercase__						)

      __lowerCAmelCase					:						Union[str, Any]					=					accelerate_config

      return info







def 		_lowercase (     ):
      __lowerCAmelCase					:						List[str]					=					env_command_parser()
      __lowerCAmelCase					:						Optional[int]					=					parser.parse_args()
      env_command(lowercase__						)
      return 0


if __name__ == "__main__":
 raise SystemExit(main())





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class   __lowercase      (unittest.TestCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					UNetaDModel(
           block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')      ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')      ,							)
       return model





 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						Any					=					PNDMScheduler()

       __lowerCAmelCase					:						Dict					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						int					=					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					'''google/ddpm-cifar10-32'''

       __lowerCAmelCase					:						Union[str, Any]					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						int					=					PNDMScheduler()

       __lowerCAmelCase					:						Any					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)
       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2


