

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCamelCase__		 =     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       if isinstance(__A	,torch.Tensor		):
              return image
       elif isinstance(__A	,PIL.Image.Image		):
              __snake_case       	=							[image]

       __snake_case       	=							[trans(img.convert("""RGB"""		)		) for img in image]
       __snake_case       	=							torch.stack(__A		)
       return image




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              super().__init__()

              # make sure scheduler can always be converted to DDIM
              __snake_case       	=							DDIMScheduler.from_config(scheduler.config   )

              self.register_modules(unet=_UpperCamelCase      , scheduler=_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              if strength < 0 or strength > 1:
                     raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							min(int(num_inference_steps * strength   )      , _UpperCamelCase   )

              __snake_case       	=							max(num_inference_steps - init_timestep      , 0   )
              __snake_case       	=							self.scheduler.timesteps[t_start:]

              return timesteps, num_inference_steps - t_start


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase=None   )      ->							Tuple:






              """simple docstring"""


              if not isinstance(_UpperCamelCase      , (torch.Tensor, PIL.Image.Image, list)   ):
                     raise ValueError(
                         F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase   )}'   )

              __snake_case       	=							image.to(device=_UpperCamelCase      , dtype=_UpperCamelCase   )

              if isinstance(_UpperCamelCase      , _UpperCamelCase   ) and len(_UpperCamelCase   ) != batch_size:
                     raise ValueError(
                         F'You have passed a list of generators of length {len(_UpperCamelCase   )}, but requested an effective batch'
                         F' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

              __snake_case       	=							init_latents.shape
              __snake_case       	=							randn_tensor(_UpperCamelCase      , generator=_UpperCamelCase      , device=_UpperCamelCase      , dtype=_UpperCamelCase   )

              # get latents
              print("""add noise to latents at timestep"""      , _UpperCamelCase   )
              __snake_case       	=							self.scheduler.add_noise(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )
              __snake_case       	=							init_latents

              return latents






       @torch.no_grad()
       def __call__(		self      , _UpperCamelCase = None      , _UpperCamelCase = 0.8      , _UpperCamelCase = 1      , _UpperCamelCase = None      , _UpperCamelCase = 0.0      , _UpperCamelCase = 50      , _UpperCamelCase = None      , _UpperCamelCase = "pil"      , _UpperCamelCase = True      , )      ->							Union[ImagePipelineOutput, Tuple]:






              """simple docstring"""


              self.check_inputs(_UpperCamelCase   )

              # 2. Preprocess image
              __snake_case       	=							preprocess(_UpperCamelCase   )

              # 3. set timesteps
              self.scheduler.set_timesteps(_UpperCamelCase      , device=self.device   )
              __snake_case	,    __snake_case       	=							self.get_timesteps(_UpperCamelCase      , _UpperCamelCase      , self.device   )
              __snake_case       	=							timesteps[:1].repeat(_UpperCamelCase   )

              # 4. Prepare latent variables
              __snake_case       	=							self.prepare_latents(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , self.unet.dtype      , self.device      , _UpperCamelCase   )
              __snake_case       	=							latents

              # 5. Denoising loop
              for t in self.progress_bar(_UpperCamelCase   ):
                     # 1. predict noise model_output
                     __snake_case       	=							self.unet(_UpperCamelCase      , _UpperCamelCase   ).sample

                     # 2. predict previous mean of image x_t-1 and add variance depending on eta
                     # eta corresponds to η in paper and should be between [0, 1]
                     # do x_t -> x_t-1
                     __snake_case       	=							self.scheduler.step(
                         _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , eta=_UpperCamelCase      , use_clipped_model_output=_UpperCamelCase      , generator=_UpperCamelCase      , ).prev_sample

              __snake_case       	=							(image / 2 + 0.5).clamp(0      , 1   )
              __snake_case       	=							image.cpu().permute(0      , 2      , 3      , 1   ).numpy()
              if output_type == "pil":
                     __snake_case       	=							self.numpy_to_pil(_UpperCamelCase   )

              if not return_dict:
                     return (image, latent_timestep.item())

              return ImagePipelineOutput(images=_UpperCamelCase   )




# 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


UpperCamelCase__		 =     '''Create a default config file for Accelerate with only a few flags set.'''







def 						lowerCamelCase__      ( __A    :List[Any]="no"	,__A    :str = default_json_config_file	,__A    :bool = False		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Dict	,__A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							parser.add_parser("""default"""	,parents=__A	,help=__A	,formatter_class=__A		)
       parser.add_argument(
           """--config_file"""	,default=__A	,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=__A	,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=__A		)
       return parser







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




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

