
'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      a_     (a_			):




       def __init__(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_ = None      ,      ):
         super().__init__()
         self.register_modules(transformer=lowercase_      ,      vae=lowercase_      ,      scheduler=lowercase_     )

         # create a imagenet -> id dictionary for easier use
         _lowerCAmelCase				:     int       =    {}
         if idalabel is not None:
           for key, value in idalabel.items():
             for label in value.split(""","""     ):
               _lowerCAmelCase				:     Union[str, Any]       =    int(lowercase_     )
           _lowerCAmelCase				:     int       =    dict(sorted(self.labels.items()     )     )




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):

         if not isinstance(lowercase_      ,      lowercase_     ):
           _lowerCAmelCase				:     Any       =    list(lowercase_     )

         for l in label:
           if l not in self.labels:
             raise ValueError(
                 f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'     )

         return [self.labels[l] for l in label]







       @torch.no_grad()
       def __call__(   self      ,      snake_case_      ,      snake_case_ = 4.0      ,      snake_case_ = None      ,      snake_case_ = 5_0      ,      snake_case_ = "pil"      ,      snake_case_ = True      ,      ):
         _lowerCAmelCase				:     Optional[Any]       =    len(lowercase_     )
         _lowerCAmelCase				:     Any       =    self.transformer.config.sample_size
         _lowerCAmelCase				:     int       =    self.transformer.config.in_channels

         _lowerCAmelCase				:     List[str]       =    randn_tensor(
             shape=(batch_size, latent_channels, latent_size, latent_size)      ,      generator=lowercase_      ,      device=self.device      ,      dtype=self.transformer.dtype      ,      )
         _lowerCAmelCase				:     Any       =    torch.cat([latents] * 2     ) if guidance_scale > 1 else latents

         _lowerCAmelCase				:     List[Any]       =    torch.tensor(lowercase_      ,      device=self.device     ).reshape(-1     )
         _lowerCAmelCase				:     Dict       =    torch.tensor([1_0_0_0] * batch_size      ,      device=self.device     )
         _lowerCAmelCase				:     List[Any]       =    torch.cat([class_labels, class_null]      ,      0     ) if guidance_scale > 1 else class_labels

         # set step values
         self.scheduler.set_timesteps(lowercase_     )

         for t in self.progress_bar(self.scheduler.timesteps     ):
           if guidance_scale > 1:
             _lowerCAmelCase				:     List[Any]       =    latent_model_input[: len(lowercase_     ) // 2]
             _lowerCAmelCase				:     Optional[Any]       =    torch.cat([half, half]      ,      dim=0     )
           _lowerCAmelCase				:     List[str]       =    self.scheduler.scale_model_input(lowercase_      ,      lowercase_     )

           _lowerCAmelCase				:     Optional[int]       =    t
           if not torch.is_tensor(lowercase_     ):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             _lowerCAmelCase				:     Any       =    latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_      ,      lowercase_     ):
               _lowerCAmelCase				:     Dict       =    torch.floataa if is_mps else torch.floataa
             else:
               _lowerCAmelCase				:     List[str]       =    torch.intaa if is_mps else torch.intaa
             _lowerCAmelCase				:     List[Any]       =    torch.tensor([timesteps]      ,      dtype=lowercase_      ,      device=latent_model_input.device     )
           elif len(timesteps.shape     ) == 0:
             _lowerCAmelCase				:     List[Any]       =    timesteps[None].to(latent_model_input.device     )
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           _lowerCAmelCase				:     List[str]       =    timesteps.expand(latent_model_input.shape[0]     )
           # predict noise model_output
           _lowerCAmelCase				:     Dict       =    self.transformer(
               lowercase_      ,      timestep=lowercase_      ,      class_labels=lowercase_     ).sample

           # perform guidance
           if guidance_scale > 1:
             _lowerCAmelCase				:     Tuple       =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             _lowerCAmelCase				:     Any       =    torch.split(lowercase_      ,      len(lowercase_     ) // 2      ,      dim=0     )

             _lowerCAmelCase				:     Optional[Any]       =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             _lowerCAmelCase				:     Optional[int]       =    torch.cat([half_eps, half_eps]      ,      dim=0     )

             _lowerCAmelCase				:     Union[str, Any]       =    torch.cat([eps, rest]      ,      dim=1     )

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
             _lowerCAmelCase				:     Any       =    torch.split(lowercase_      ,      lowercase_      ,      dim=1     )
           else:
             _lowerCAmelCase				:     int       =    noise_pred

           # compute previous image: x_t -> x_t-1
           _lowerCAmelCase				:     int       =    self.scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_     ).prev_sample

         if guidance_scale > 1:
           _lowerCAmelCase				:     int       =    latent_model_input.chunk(2      ,      dim=0     )
         else:
           _lowerCAmelCase				:     Optional[int]       =    latent_model_input

         _lowerCAmelCase				:     List[Any]       =    1 / self.vae.config.scaling_factor * latents
         _lowerCAmelCase				:     List[Any]       =    self.vae.decode(lowercase_     ).sample

         _lowerCAmelCase				:     Tuple       =    (samples / 2 + 0.5).clamp(0      ,      1     )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         _lowerCAmelCase				:     Tuple       =    samples.cpu().permute(0      ,      2      ,      3      ,      1     ).float().numpy()

         if output_type == "pil":
           _lowerCAmelCase				:     List[str]       =    self.numpy_to_pil(lowercase_     )

         if not return_dict:
           return (samples,)

         return ImagePipelineOutput(images=lowercase_     )





"""simple docstring"""





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCamelCase  :      Tuple							      =		TypeVar('''T''')







class 				SCREAMING_SNAKE_CASE					(		Generic[T]       ):






					"""simple docstring"""



					lowercase__				 =       42  # Cache store of keys
					lowercase__				 =       42  # References of the keys in cache
					lowercase__				 =       10  # Maximum capacity of cache







					def __init__(       self			:				Dict   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	deque()
						lowerCAmelCase__	:						Any  				=	set()
						if not n:
							lowerCAmelCase__	:						Optional[Any]  				=	sys.maxsize
						elif n < 0:
							raise ValueError('''n should be an integer greater than 0.'''		)
						else:
							lowerCAmelCase__	:						int  				=	n







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				T		):
						if x not in self.key_reference:
							if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
								lowerCAmelCase__	:						Any  				=	self.dq_store.pop()
								self.key_reference.remove(lowercase_		)
						else:
							self.dq_store.remove(lowercase_		)

						self.dq_store.appendleft(lowercase_		)
						self.key_reference.add(lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						for k in self.dq_store:
							print(lowercase_		)







					def __repr__(       self			:				Tuple		):
						return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}'


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__UpperCamelCase  :      LRUCache[str | int]							      =		LRUCache(4)
							lru_cache.refer('''A''')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('''A''')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




