


'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=DDIMPipeline
       __UpperCAmelCase					=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       __UpperCAmelCase					=PipelineTesterMixin.required_optional_params - {
           'num_images_per_prompt',
           'latents',
           'callback',
           'callback_steps',
       }
       __UpperCAmelCase					=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       __UpperCAmelCase					=False






       def   UpperCamelCase						( self   )->      int:
            torch.manual_seed(0   )
            _A				=							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')			,     )
            _A				=							DDIMScheduler()
            _A				=							{'unet': unet, 'scheduler': scheduler}
            return components






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=0   )->      Optional[Any]:
            if str(_UpperCamelCase   ).startswith('mps'   ):
                 _A				=							torch.manual_seed(_UpperCamelCase   )
            else:
                 _A				=							torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
            _A				=							{
                'batch_size': 1,
                'generator': generator,
                'num_inference_steps': 2,
                'output_type': 'numpy',
            }
            return inputs






       def   UpperCamelCase						( self   )->      Dict:
            _A				=							'cpu'

            _A				=							self.get_dummy_components()
            _A				=							self.pipeline_class(**_UpperCamelCase   )
            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   ).images
            _A				=							image[0, -3:, -3:, -1]

            self.assertEqual(image.shape			,     (1, 32, 32, 3)   )
            _A				=							np.array(
                [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]   )
            _A				=							np.abs(image_slice.flatten() - expected_slice   ).max()
            self.assertLessEqual(_UpperCamelCase			,     1e-3   )






       def   UpperCamelCase						( self   )->      Any:
            super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )






       def   UpperCamelCase						( self   )->      List[Any]:
            super().test_save_load_local(expected_max_difference=3e-3   )






       def   UpperCamelCase						( self   )->      int:
            super().test_save_load_optional_components(expected_max_difference=3e-3   )






       def   UpperCamelCase						( self   )->      Dict:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3   )






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






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							'google/ddpm-cifar10-32'

            _A				=							UNetaDModel.from_pretrained(_UpperCamelCase   )
            _A				=							DDIMScheduler()

            _A				=							DDIMPipeline(unet=_UpperCamelCase			,     scheduler=_UpperCamelCase   )
            ddim.to(_UpperCamelCase   )
            ddim.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							torch.manual_seed(0   )
            _A				=							ddim(generator=_UpperCamelCase			,     eta=0.0			,     output_type='numpy'   ).images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            _A				=							np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]   )

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






       def   UpperCamelCase						( self   )->      Any:
            _A				=							'google/ddpm-ema-bedroom-256'

            _A				=							UNetaDModel.from_pretrained(_UpperCamelCase   )
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase   )

            _A				=							DDIMPipeline(unet=_UpperCamelCase			,     scheduler=_UpperCamelCase   )
            ddpm.to(_UpperCamelCase   )
            ddpm.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							torch.manual_seed(0   )
            _A				=							ddpm(generator=_UpperCamelCase			,     output_type='numpy'   ).images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)
            _A				=							np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]   )

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









'''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


lowerCAmelCase						=					"""Create a default config file for Accelerate with only a few flags set."""

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]="no"							,    __UpperCamelCase					:       str = default_json_config_file							,    __UpperCamelCase					:       bool = False		)					->	Tuple:







     """simple docstring"""
     _A				=							Path(__UpperCamelCase		)
     path.parent.mkdir(parents=__UpperCamelCase							,    exist_ok=__UpperCamelCase		)
     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
     _A				=							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}'		)
     _A				=							{
         'compute_environment': 'LOCAL_MACHINE',
         'mixed_precision': mixed_precision,
     }
     if torch.cuda.is_available():
          _A				=							torch.cuda.device_count()
          _A				=							num_gpus
          _A				=							False
          if num_gpus > 1:
               _A				=							'MULTI_GPU'
          else:
               _A				=							'NO'
     elif is_xpu_available() and use_xpu:
          _A				=							torch.xpu.device_count()
          _A				=							num_xpus
          _A				=							False
          if num_xpus > 1:
               _A				=							'MULTI_XPU'
          else:
               _A				=							'NO'
     elif is_npu_available():
          _A				=							torch.npu.device_count()
          _A				=							num_npus
          _A				=							False
          if num_npus > 1:
               _A				=							'MULTI_NPU'
          else:
               _A				=							'NO'
     else:
          _A				=							0
          _A				=							True
          _A				=							1
          _A				=							'NO'
     _A				=							ClusterConfig(**__UpperCamelCase		)
     config.to_json_file(__UpperCamelCase		)
     return path

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Any:







     """simple docstring"""
     _A				=							parser.add_parser('default'							,    parents=__UpperCamelCase							,    help=__UpperCamelCase							,    formatter_class=__UpperCamelCase		)
     parser.add_argument(
         '--config_file'							,    default=__UpperCamelCase							,    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=__UpperCamelCase							,    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=__UpperCamelCase		)
     return parser

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any		)					->	Union[str, Any]:







     """simple docstring"""
     _A				=							write_basic_config(args.mixed_precision							,    args.save_location		)
     if config_file:
          print(F'accelerate configuration saved at {config_file}'		)





