
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A     :	List[Any]    = logging.get_logger(__name__)

A     :	Tuple    = {
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}







class 			__A( a  ,   a			):
				snake_case_						   =    '''resnet'''
				snake_case_						   =    ['''basic''', '''bottleneck''']




				def __init__(     self		,     _snake_case=3		,     _snake_case=64		,     _snake_case=[256, 512, 1_024, 2_048]		,     _snake_case=[3, 4, 6, 3]		,     _snake_case="bottleneck"		,     _snake_case="relu"		,     _snake_case=False		,     _snake_case=None		,     _snake_case=None		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								if layer_type not in self.layer_types:
												raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types				)}"""				)
								__a										=    num_channels
								__a										=    embedding_size
								__a										=    hidden_sizes
								__a										=    depths
								__a										=    layer_type
								__a										=    hidden_act
								__a										=    downsample_in_first_stage
								__a										=    ['''stem'''] + [F"""stage{idx}""" for idx in range(1		,     len(_snake_case				) + 1				)]
								__a							,     __a										=    get_aligned_output_features_output_indices(
								    out_features=_snake_case		,     out_indices=_snake_case		,     stage_names=self.stage_names				)







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-3



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    )





