





# Copyright 2021 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

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_snake_case  :   Any						   =  'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def   a_   (			):
   __lowerCAmelCase							     =     _ask_options(
       'In which compute environment are you running?',     ['This machine', 'AWS (Amazon SageMaker)'],     _convert_compute_environment,     )
   if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
      __lowerCAmelCase							     =     get_sagemaker_input()
   else:
      __lowerCAmelCase							     =     get_cluster_input()
   return config

def   a_   (			lowerCAmelCase_				:      Any=None     ):
   if subparsers is not None:
      __lowerCAmelCase							     =     subparsers.add_parser('config',     description=lowerCAmelCase_     )
   else:
      __lowerCAmelCase							     =     argparse.ArgumentParser('Accelerate config command',     description=lowerCAmelCase_     )

   parser.add_argument(
       '--config_file',     default=lowerCAmelCase_,     help=(
           'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
           'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
           'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
           'with \'huggingface\'.'
       ),     )

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

def   a_   (			lowerCAmelCase_				:      Any     ):
   __lowerCAmelCase							     =     get_user_input()
   if args.config_file is not None:
      __lowerCAmelCase							     =     args.config_file
   else:
      if not os.path.isdir(lowerCAmelCase_     ):
         os.makedirs(lowerCAmelCase_     )
      __lowerCAmelCase							     =     default_yaml_config_file

   if config_file.endswith('.json'     ):
      config.to_json_file(lowerCAmelCase_     )
   else:
      config.to_yaml_file(lowerCAmelCase_     )
   print(F"""accelerate configuration saved at {config_file}"""     )

def   a_   (			):
   __lowerCAmelCase							     =     config_command_parser()
   __lowerCAmelCase							     =     parser.parse_args()
   config_command(lowerCAmelCase_     )


if __name__ == "__main__":
     main()








import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[str]					)							->						int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(3_2, 6_4) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return model






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						List[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDConditionModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,  cross_attention_dim=1_0 ,  )
     return model






  @property
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     AutoencoderKL(
         sample_size=(1_2_8, 6_4) ,  in_channels=1 ,  out_channels=1 ,  latent_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,  up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,  )
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return vqvae, unet






  @slow
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_unet.config.sample_size[1] ,  y_res=self.dummy_unet.config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDPMScheduler()
     __lowerCAmelCase							     =     AudioDiffusionPipeline(vqvae=lowerCAmelCase_ ,  unet=self.dummy_unet ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4 ,  return_dict=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output[0][0]

     assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
     assert (
         image.height == self.dummy_unet.config.sample_size[0]
         and image.width == self.dummy_unet.config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.frombuffer(image_from_tuple.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDIMScheduler()
     __lowerCAmelCase							     =     self.dummy_vqvae_and_unet
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=dummy_vqvae_and_unet[1] ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     np.random.uniform(-1 ,  1 ,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(raw_audio=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  start_step=5 ,  steps=1_0					)
     __lowerCAmelCase							     =     output.images[0]

     assert (
         image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
         and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     self.dummy_unet_condition
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=lowerCAmelCase_ ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     torch.rand((1, 1, 1_0)					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  encoding=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.images[0]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





@slow
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
     assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

