


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


snake_case				:    Any       				=     logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case				:    Union[str, Any]       				=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		List[str]=8						):




      """simple docstring"""





      a						:List[str] 							=       height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      a						:int 							=       width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class 		_snake_case  (      _snake_case							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       super().__init__()

       self.register_modules(
           unet=_lowerCamelCase					,  scheduler=_lowerCamelCase					,  movq=_lowerCamelCase					,  )
       a						:Any 							=       2 ** (len(self.movq.config.block_out_channels    ) - 1)




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if latents is None:
             a						:str 							=       randn_tensor(_lowerCamelCase					,  generator=_lowerCamelCase					,  device=_lowerCamelCase					,  dtype=_lowerCamelCase    )
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
             a						:Any 							=       latents.to(_lowerCamelCase    )

       a						:Dict 							=       latents * scheduler.init_noise_sigma
       return latents




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase=0    ):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

       a						:int 							=       torch.device(F'''cuda:{gpu_id}'''    )

       a						:int 							=       [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                   cpu_offload(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase=0    ):
       if is_accelerate_available() and is_accelerate_version('''>='''					,  '''0.17.0.dev0'''    ):
             from accelerate import cpu_offload_with_hook
       else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''    )

       a						:Any 							=       torch.device(F'''cuda:{gpu_id}'''    )

       if self.device.type != "cpu":
             self.to('''cpu'''					,  silence_dtype_warnings=_lowerCamelCase    )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       a						:Tuple 							=       None
       for cpu_offloaded_model in [self.unet, self.movq]:
             a							,				a						:List[str] 							=       cpu_offload_with_hook(_lowerCamelCase					,  _lowerCamelCase					,  prev_module_hook=_lowerCamelCase    )

       # We'll offload the last model manually.
       a						:str 							=       hook




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if not hasattr(self.unet					,  '''_hf_hook'''    ):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_lowerCamelCase					,  '''_hf_hook'''    )
                 and hasattr(module._hf_hook					,  '''execution_device'''    )
                 and module._hf_hook.execution_device is not None
             ):
                   return torch.device(module._hf_hook.execution_device    )
       return self.device




 @torch.no_grad()
 @replace_example_docstring(_lowerCamelCase    )
 def __call__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = 512					,  _lowerCamelCase = 512					,  _lowerCamelCase = 100					,  _lowerCamelCase = 4.0					,  _lowerCamelCase = 1					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = "pil"					,  _lowerCamelCase = True					,  ):
       a						:int 							=       self._execution_device

       a						:Optional[Any] 							=       guidance_scale > 1.0

       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Union[str, Any] 							=       torch.cat(_lowerCamelCase					,  dim=0    )
       a						:Any 							=       image_embeds.shape[0] * num_images_per_prompt
       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:List[str] 							=       torch.cat(_lowerCamelCase					,  dim=0    )

       if do_classifier_free_guidance:
             a						:Union[str, Any] 							=       image_embeds.repeat_interleave(_lowerCamelCase					,  dim=0    )
             a						:Optional[int] 							=       negative_image_embeds.repeat_interleave(_lowerCamelCase					,  dim=0    )

       a						:Optional[int] 							=       torch.cat([negative_image_embeds, image_embeds]					,  dim=0    ).to(dtype=self.unet.dtype					,  device=_lowerCamelCase    )

       self.scheduler.set_timesteps(_lowerCamelCase					,  device=_lowerCamelCase    )
       a						:Optional[Any] 							=       self.scheduler.timesteps

       a						:List[str] 							=       self.unet.config.in_channels

       a							,				a						:str 							=       downscale_height_and_width(_lowerCamelCase					,  _lowerCamelCase					,  self.movq_scale_factor    )

       # create initial latent
       a						:int 							=       self.prepare_latents(
           (batch_size, num_channels_latents, height, width)					,  image_embeds.dtype					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  self.scheduler					,  )

       for i, t in enumerate(self.progress_bar(_lowerCamelCase    )    ):
             # expand the latents if we are doing classifier free guidance
             a						:Tuple 							=       torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

             a						:Union[str, Any] 							=       {'''image_embeds''': image_embeds}
             a						:Optional[Any] 							=       self.unet(
                 sample=_lowerCamelCase					,  timestep=_lowerCamelCase					,  encoder_hidden_states=_lowerCamelCase					,  added_cond_kwargs=_lowerCamelCase					,  return_dict=_lowerCamelCase					,  )[0]

             if do_classifier_free_guidance:
                   a							,				a						:Any 							=       noise_pred.split(latents.shape[1]					,  dim=1    )
                   a							,				a						:List[str] 							=       noise_pred.chunk(2    )
                   a							,				a						:int 							=       variance_pred.chunk(2    )
                   a						:List[Any] 							=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   a						:Optional[int] 							=       torch.cat([noise_pred, variance_pred_text]					,  dim=1    )

             if not (
                 hasattr(self.scheduler.config					,  '''variance_type'''    )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                   a							,				a						:Tuple 							=       noise_pred.split(latents.shape[1]					,  dim=1    )

             # compute the previous noisy sample x_t -> x_t-1
             a						:int 							=       self.scheduler.step(
                 _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  generator=_lowerCamelCase					,  )[0]
       # post-processing
       a						:int 							=       self.movq.decode(_lowerCamelCase					,  force_not_quantize=_lowerCamelCase    )['''sample''']

       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''    )

       if output_type in ["np", "pil"]:
             a						:str 							=       image * 0.5 + 0.5
             a						:List[Any] 							=       image.clamp(0					,  1    )
             a						:str 							=       image.cpu().permute(0					,  2					,  3					,  1    ).float().numpy()

       if output_type == "pil":
             a						:str 							=       self.numpy_to_pil(_lowerCamelCase    )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase    )









# 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


snake_case				:    int       				=     '''Create a default config file for Accelerate with only a few flags set.'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any]="no" ,  UpperCAmelCase_						:		str = default_json_config_file ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[str] 							=       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						:Optional[Any] 							=       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						:List[Any] 							=       {
          '''compute_environment''': '''LOCAL_MACHINE''',
          '''mixed_precision''': mixed_precision,
      }
      if torch.cuda.is_available():
            a						:Dict 							=       torch.cuda.device_count()
            a						:Tuple 							=       num_gpus
            a						:int 							=       False
            if num_gpus > 1:
                  a						:str 							=       '''MULTI_GPU'''
            else:
                  a						:List[Any] 							=       '''NO'''
      elif is_xpu_available() and use_xpu:
            a						:List[Any] 							=       torch.xpu.device_count()
            a						:Optional[int] 							=       num_xpus
            a						:List[Any] 							=       False
            if num_xpus > 1:
                  a						:int 							=       '''MULTI_XPU'''
            else:
                  a						:str 							=       '''NO'''
      elif is_npu_available():
            a						:List[str] 							=       torch.npu.device_count()
            a						:Any 							=       num_npus
            a						:Optional[int] 							=       False
            if num_npus > 1:
                  a						:List[str] 							=       '''MULTI_NPU'''
            else:
                  a						:Dict 							=       '''NO'''
      else:
            a						:str 							=       0
            a						:Optional[Any] 							=       True
            a						:Optional[Any] 							=       1
            a						:str 							=       '''NO'''
      a						:List[str] 							=       ClusterConfig(**UpperCAmelCase_						)
      config.to_json_file(UpperCAmelCase_						)
      return path
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:List[Any] 							=       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_						:		int						):




      """simple docstring"""





      a						:Optional[Any] 							=       write_basic_config(args.mixed_precision ,  args.save_location						)
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''						)





