





'''simple docstring'''




import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_					,			unittest.TestCase							):
    _A :			List[str]						   =       AutoencoderKL
    _A :			Optional[int]						   =       """sample"""
    _A :			Dict						   =       1e-2



    @property
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   4
     __UpperCAmelCase					=   3
     __UpperCAmelCase					=   (32, 32)

     __UpperCAmelCase					=   floats_tensor((batch_size, num_channels) + sizes					).to(__A					)

     return {"sample": image}



    @property
    def 					__lowerCamelCase						(  self					):
     return (3, 32, 32)



    @property
    def 					__lowerCamelCase						(  self					):
     return (3, 32, 32)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   {
         'block_out_channels': [32, 64],
         'in_channels': 3,
         'out_channels': 3,
         'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
         'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
         'latent_channels': 4,
     }
     __UpperCAmelCase					=   self.dummy_input
     return init_dict, inputs_dict



    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     pass



    @unittest.skipIf(torch_device == 'mps'     ,			'Gradient checkpointing skipped on MPS'					)
    def 					__lowerCamelCase						(  self					):
     # enable deterministic behavior for gradient checkpointing
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.prepare_init_args_and_inputs_for_common()
     __UpperCAmelCase					=   self.model_class(**__A					)
     model.to(__A					)

     assert not model.is_gradient_checkpointing and model.training

     __UpperCAmelCase					=   model(**__A					).sample
     # run the backwards pass on the model. For backwards pass, for simplicity purpose,
     # we won't calculate the loss and rather backprop on out.sum()
     model.zero_grad()

     __UpperCAmelCase					=   torch.randn_like(__A					)
     __UpperCAmelCase					=   (out - labels).mean()
     loss.backward()

     # re-instantiate the model now enabling gradient checkpointing
     __UpperCAmelCase					=   self.model_class(**__A					)
     # clone model
     model_a.load_state_dict(model.state_dict()					)
     model_a.to(__A					)
     model_a.enable_gradient_checkpointing()

     assert model_a.is_gradient_checkpointing and model_a.training

     __UpperCAmelCase					=   model_a(**__A					).sample
     # run the backwards pass on the model. For backwards pass, for simplicity purpose,
     # we won't calculate the loss and rather backprop on out.sum()
     model_a.zero_grad()
     __UpperCAmelCase					=   (out_a - labels).mean()
     loss_a.backward()

     # compare the output and parameters gradients
     self.assertTrue((loss - loss_a).abs() < 1E-5					)
     __UpperCAmelCase					=   dict(model.named_parameters()					)
     __UpperCAmelCase					=   dict(model_a.named_parameters()					)
     for name, param in named_params.items():
      self.assertTrue(torch_all_close(param.grad.data     ,			named_params_a[name].grad.data     ,			atol=5E-5					)					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'     ,			output_loading_info=__A					)
     self.assertIsNotNone(__A					)
     self.assertEqual(len(loading_info['missing_keys']					)     ,			0					)

     model.to(__A					)
     __UpperCAmelCase					=   model(**self.dummy_input					)

     assert image is not None, "Make sure output is not None"




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'					)
     __UpperCAmelCase					=   model.to(__A					)
     model.eval()

     if torch_device == "mps":
      __UpperCAmelCase					=   torch.manual_seed(0					)
     else:
      __UpperCAmelCase					=   torch.Generator(device=__A					).manual_seed(0					)

     __UpperCAmelCase					=   torch.randn(
         1     ,			model.config.in_channels     ,			model.config.sample_size     ,			model.config.sample_size     ,			generator=torch.manual_seed(0					)     ,			)
     __UpperCAmelCase					=   image.to(__A					)
     with torch.no_grad():
      __UpperCAmelCase					=   model(__A     ,			sample_posterior=__A     ,			generator=__A					).sample

     __UpperCAmelCase					=   output[0, -1, -3:, -3:].flatten().cpu()

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     if torch_device == "mps":
      __UpperCAmelCase					=   torch.tensor(
          [
              -4.0078E-01,
              -3.8323E-04,
              -1.2681E-01,
              -1.1462E-01,
              2.0095E-01,
              1.0893E-01,
              -8.8247E-02,
              -3.0361E-01,
              -9.8644E-03,
          ]					)
     elif torch_device == "cpu":
      __UpperCAmelCase					=   torch.tensor(
          [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]					)
     else:
      __UpperCAmelCase					=   torch.tensor(
          [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]					)

     self.assertTrue(torch_all_close(__A     ,			__A     ,			rtol=1E-2					)					)






@slow
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     return f'gaussian_noise_s={seed}_shape={"_".join([str(__A					) for s in shape]					)}.npy'



    def 					__lowerCamelCase						(  self					):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



    def 					__lowerCamelCase						(  self     ,			__A=0     ,			__A=(4, 3, 512, 512)     ,			__A=False					):
     __UpperCAmelCase					=   torch.floataa if fpaa else torch.floataa
     __UpperCAmelCase					=   torch.from_numpy(load_hf_numpy(self.get_file_format(__A     ,			__A					)					)					).to(__A					).to(__A					)
     return image



    def 					__lowerCamelCase						(  self     ,			__A="CompVis/stable-diffusion-v1-4"     ,			__A=False					):
     __UpperCAmelCase					=   'fp16' if fpaa else None
     __UpperCAmelCase					=   torch.floataa if fpaa else torch.floataa

     __UpperCAmelCase					=   AutoencoderKL.from_pretrained(
         __A     ,			subfolder='vae'     ,			torch_dtype=__A     ,			revision=__A     ,			)
     model.to(__A					).eval()

     return model



    def 					__lowerCamelCase						(  self     ,			__A=0					):
     if torch_device == "mps":
      return torch.manual_seed(__A					)
     return torch.Generator(device=__A					).manual_seed(__A					)



    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]					)
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model()
     __UpperCAmelCase					=   self.get_sd_image(__A					)
     __UpperCAmelCase					=   self.get_generator(__A					)

     with torch.no_grad():
      __UpperCAmelCase					=   model(__A     ,			generator=__A     ,			sample_posterior=__A					).sample

     assert sample.shape == image.shape

     __UpperCAmelCase					=   sample[-1, -2:, -2:, :2].flatten().float().cpu()
     __UpperCAmelCase					=   torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice					)

     assert torch_all_close(__A     ,			__A     ,			atol=3E-3					)



    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
            [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
            # fmt: on
        ]					)
    @require_torch_gpu
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model(fpaa=__A					)
     __UpperCAmelCase					=   self.get_sd_image(__A     ,			fpaa=__A					)
     __UpperCAmelCase					=   self.get_generator(__A					)

     with torch.no_grad():
      __UpperCAmelCase					=   model(__A     ,			generator=__A     ,			sample_posterior=__A					).sample

     assert sample.shape == image.shape

     __UpperCAmelCase					=   sample[-1, -2:, :2, -2:].flatten().float().cpu()
     __UpperCAmelCase					=   torch.tensor(__A					)

     assert torch_all_close(__A     ,			__A     ,			atol=1E-2					)



    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]					)
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model()
     __UpperCAmelCase					=   self.get_sd_image(__A					)

     with torch.no_grad():
      __UpperCAmelCase					=   model(__A					).sample

     assert sample.shape == image.shape

     __UpperCAmelCase					=   sample[-1, -2:, -2:, :2].flatten().float().cpu()
     __UpperCAmelCase					=   torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice					)

     assert torch_all_close(__A     ,			__A     ,			atol=3E-3					)



    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
            [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
            # fmt: on
        ]					)
    @require_torch_gpu
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model()
     __UpperCAmelCase					=   self.get_sd_image(__A     ,			shape=(3, 4, 64, 64)					)

     with torch.no_grad():
      __UpperCAmelCase					=   model.decode(__A					).sample

     assert list(sample.shape					) == [3, 3, 512, 512]

     __UpperCAmelCase					=   sample[-1, -2:, :2, -2:].flatten().cpu()
     __UpperCAmelCase					=   torch.tensor(__A					)

     assert torch_all_close(__A     ,			__A     ,			atol=1E-3					)



    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
            [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
            # fmt: on
        ]					)
    @require_torch_gpu
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model(fpaa=__A					)
     __UpperCAmelCase					=   self.get_sd_image(__A     ,			shape=(3, 4, 64, 64)     ,			fpaa=__A					)

     with torch.no_grad():
      __UpperCAmelCase					=   model.decode(__A					).sample

     assert list(sample.shape					) == [3, 3, 512, 512]

     __UpperCAmelCase					=   sample[-1, -2:, :2, -2:].flatten().float().cpu()
     __UpperCAmelCase					=   torch.tensor(__A					)

     assert torch_all_close(__A     ,			__A     ,			atol=5E-3					)



    @parameterized.expand([(13,), (16,), (27,)]					)
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,			reason='xformers is not required when using PyTorch 2.0.'					)
    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model(fpaa=__A					)
     __UpperCAmelCase					=   self.get_sd_image(__A     ,			shape=(3, 4, 64, 64)     ,			fpaa=__A					)

     with torch.no_grad():
      __UpperCAmelCase					=   model.decode(__A					).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
      __UpperCAmelCase					=   model.decode(__A					).sample

     assert list(sample.shape					) == [3, 3, 512, 512]

     assert torch_all_close(__A     ,			__A     ,			atol=1E-1					)



    @parameterized.expand([(13,), (16,), (37,)]					)
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,			reason='xformers is not required when using PyTorch 2.0.'					)
    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model()
     __UpperCAmelCase					=   self.get_sd_image(__A     ,			shape=(3, 4, 64, 64)					)

     with torch.no_grad():
      __UpperCAmelCase					=   model.decode(__A					).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
      __UpperCAmelCase					=   model.decode(__A					).sample

     assert list(sample.shape					) == [3, 3, 512, 512]

     assert torch_all_close(__A     ,			__A     ,			atol=1E-2					)




    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
            [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
            # fmt: on
        ]					)
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.get_sd_vae_model()
     __UpperCAmelCase					=   self.get_sd_image(__A					)
     __UpperCAmelCase					=   self.get_generator(__A					)

     with torch.no_grad():
      __UpperCAmelCase					=   model.encode(__A					).latent_dist
      __UpperCAmelCase					=   dist.sample(generator=__A					)

     assert list(sample.shape					) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

     __UpperCAmelCase					=   sample[0, -1, -3:, -3:].flatten().cpu()
     __UpperCAmelCase					=   torch.tensor(__A					)

     __UpperCAmelCase					=   3E-3 if torch_device != 'mps' else 1E-2
     assert torch_all_close(__A     ,			__A     ,			atol=__A					)












'''simple docstring'''




# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_A:					str							=				"""Create a default config file for Accelerate with only a few flags set."""







def _lowerCAmelCase     (      _lowerCAmelCase="no"       ,    _lowerCAmelCase = default_json_config_file       ,    _lowerCAmelCase = False    )->     List[Any]:
 __UpperCAmelCase					=   Path(_lowerCAmelCase    )
 path.parent.mkdir(parents=_lowerCAmelCase       ,    exist_ok=_lowerCAmelCase    )
 if path.exists():
  print(
      F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'    )
  return False
 __UpperCAmelCase					=   mixed_precision.lower()
 if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
  raise ValueError(
      F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'    )
 __UpperCAmelCase					=   {
     'compute_environment': 'LOCAL_MACHINE',
     'mixed_precision': mixed_precision,
 }
 if torch.cuda.is_available():
  __UpperCAmelCase					=   torch.cuda.device_count()
  __UpperCAmelCase					=   num_gpus
  __UpperCAmelCase					=   False
  if num_gpus > 1:
   __UpperCAmelCase					=   'MULTI_GPU'
  else:
   __UpperCAmelCase					=   'NO'
 elif is_xpu_available() and use_xpu:
  __UpperCAmelCase					=   torch.xpu.device_count()
  __UpperCAmelCase					=   num_xpus
  __UpperCAmelCase					=   False
  if num_xpus > 1:
   __UpperCAmelCase					=   'MULTI_XPU'
  else:
   __UpperCAmelCase					=   'NO'
 elif is_npu_available():
  __UpperCAmelCase					=   torch.npu.device_count()
  __UpperCAmelCase					=   num_npus
  __UpperCAmelCase					=   False
  if num_npus > 1:
   __UpperCAmelCase					=   'MULTI_NPU'
  else:
   __UpperCAmelCase					=   'NO'
 else:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   True
  __UpperCAmelCase					=   1
  __UpperCAmelCase					=   'NO'
 __UpperCAmelCase					=   ClusterConfig(**_lowerCAmelCase    )
 config.to_json_file(_lowerCAmelCase    )
 return path







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 __UpperCAmelCase					=   parser.add_parser('default'       ,    parents=_lowerCAmelCase       ,    help=_lowerCAmelCase       ,    formatter_class=_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\'.'
     )       ,    dest='save_location'       ,    )

 parser.add_argument(
     '--mixed_precision'       ,    choices=['no', 'fp16', 'bf16']       ,    type=_lowerCAmelCase       ,    help='Whether or not to use mixed precision training. '
     'Choose between FP16 and BF16 (bfloat16) training. '
     'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,    default='no'       ,    )
 parser.set_defaults(func=_lowerCAmelCase    )
 return parser







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Union[str, Any]:
 __UpperCAmelCase					=   write_basic_config(args.mixed_precision       ,    args.save_location    )
 if config_file:
  print(F'accelerate configuration saved at {config_file}'    )





