




"""simple docstring"""

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,
)


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

a         =      '''
    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 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: List[Any]    ,  _snake_case							: List[Any]=8							)   -> Optional[int]:






      '''simple docstring'''


      _A        =       height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _A        =       width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	UNetaDConditionModel   ,							_UpperCAmelCase	:	DDPMScheduler   ,							_UpperCAmelCase	:	VQModel   ,							):
       super().__init__()

       self.register_modules(
           unet=_UpperCAmelCase   ,							scheduler=_UpperCAmelCase   ,							movq=_UpperCAmelCase   ,							)
       _A        =       2 ** (len(self.movq.config.block_out_channels		) - 1)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if latents is None:
             _A        =       randn_tensor(_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		)
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
             _A        =       latents.to(_UpperCAmelCase		)

       _A        =       latents * scheduler.init_noise_sigma
       return latents

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

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

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

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[str, Any]=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        =       torch.device(F'''cuda:{gpu_id}'''		)

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

       _A        =       None
       for cpu_offloaded_model in [self.unet, self.movq]:
             _A       ,   _A        =       cpu_offload_with_hook(_UpperCAmelCase   ,							_UpperCAmelCase   ,							prev_module_hook=_UpperCAmelCase		)

       # We'll offload the last model manually.
       _A        =       hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       lowerCAmelCase_							(			self	:	int		):
       if not hasattr(self.unet   ,							'_hf_hook'		):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase   ,							'_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(_UpperCAmelCase		)
 def __call__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 100   ,							_UpperCAmelCase	:	float = 4.0   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	Optional[torch.FloatTensor] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							):
       _A        =       self._execution_device

       _A        =       guidance_scale > 1.0

       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
             _A        =       torch.cat(_UpperCAmelCase   ,							dim=0		)
       _A        =       image_embeds.shape[0] * num_images_per_prompt
       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
             _A        =       torch.cat(_UpperCAmelCase   ,							dim=0		)

       if do_classifier_free_guidance:
             _A        =       image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)
             _A        =       negative_image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)

       _A        =       torch.cat([negative_image_embeds, image_embeds]   ,							dim=0		).to(dtype=self.unet.dtype   ,							device=_UpperCAmelCase		)

       self.scheduler.set_timesteps(_UpperCAmelCase   ,							device=_UpperCAmelCase		)
       _A        =       self.scheduler.timesteps

       _A        =       self.unet.config.in_channels

       _A       ,   _A        =       downscale_height_and_width(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.movq_scale_factor		)

       # create initial latent
       _A        =       self.prepare_latents(
           (batch_size, num_channels_latents, height, width)   ,							image_embeds.dtype   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.scheduler   ,							)

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

             _A        =       {'image_embeds': image_embeds}
             _A        =       self.unet(
                 sample=_UpperCAmelCase   ,							timestep=_UpperCAmelCase   ,							encoder_hidden_states=_UpperCAmelCase   ,							added_cond_kwargs=_UpperCAmelCase   ,							return_dict=_UpperCAmelCase   ,							)[0]

             if do_classifier_free_guidance:
                   _A       ,   _A        =       noise_pred.split(latents.shape[1]   ,							dim=1		)
                   _A       ,   _A        =       noise_pred.chunk(2		)
                   _A       ,   _A        =       variance_pred.chunk(2		)
                   _A        =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   _A        =       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        =       noise_pred.split(latents.shape[1]   ,							dim=1		)

             # compute the previous noisy sample x_t -> x_t-1
             _A        =       self.scheduler.step(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							)[0]
       # post-processing
       _A        =       self.movq.decode(_UpperCAmelCase   ,							force_not_quantize=_UpperCAmelCase		)['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        =       image * 0.5 + 0.5
             _A        =       image.clamp(0   ,							1		)
             _A        =       image.cpu().permute(0   ,							2   ,							3   ,							1		).float().numpy()

       if output_type == "pil":
             _A        =       self.numpy_to_pil(_UpperCAmelCase		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase		)







"""simple docstring"""

from collections import deque





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	int   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       process_name  # process name
       _A        =       arrival_time  # arrival time of the process
       # completion time of finished process or last interrupted time
       _A        =       arrival_time
       _A        =       burst_time  # remaining burst time
       _A        =       0  # total time of the process wait in ready queue
       _A        =       0  # time from arrival time to completion time





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	deque[Process]   ,							_UpperCAmelCase	:	int   ,							):
       # total number of mlfq's queues
       _A        =       number_of_queues
       # time slice of queues that round robin algorithm applied
       _A        =       time_slices
       # unfinished process is in this ready_queue
       _A        =       queue
       # current time
       _A        =       current_time
       # finished process is in this sequence queue
       _A        =       deque()

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       []
       for i in range(len(self.finish_queue		)		):
             sequence.append(self.finish_queue[i].process_name		)
       return sequence

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	list[Process]		):
       _A        =       []
       for i in range(len(_UpperCAmelCase		)		):
             waiting_times.append(queue[i].waiting_time		)
       return waiting_times

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	list[Process]		):
       _A        =       []
       for i in range(len(_UpperCAmelCase		)		):
             turnaround_times.append(queue[i].turnaround_time		)
       return turnaround_times

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	list[Process]		):
       _A        =       []
       for i in range(len(_UpperCAmelCase		)		):
             completion_times.append(queue[i].stop_time		)
       return completion_times

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	deque[Process]		):
       return [q.burst_time for q in queue]

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Process		):
       process.waiting_time += self.current_time - process.stop_time
       return process.waiting_time

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	deque[Process]		):
       _A        =       deque()  # sequence deque of finished process
       while len(_UpperCAmelCase		) != 0:
             _A        =       ready_queue.popleft()  # current process

             # if process's arrival time is later than current time, update current time
             if self.current_time < cp.arrival_time:
                   self.current_time += cp.arrival_time

             # update waiting time of current process
             self.update_waiting_time(_UpperCAmelCase		)
             # update current time
             self.current_time += cp.burst_time
             # finish the process and set the process's burst-time 0
             _A        =       0
             # set the process's turnaround time because it is finished
             _A        =       self.current_time - cp.arrival_time
             # set the completion time
             _A        =       self.current_time
             # add the process to queue that has finished queue
             finished.append(_UpperCAmelCase		)

       self.finish_queue.extend(_UpperCAmelCase		)  # add finished process to finish queue
       # FCFS will finish all remaining processes
       return finished

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	deque[Process]   ,							_UpperCAmelCase	:	int		):
       _A        =       deque()  # sequence deque of terminated process
       # just for 1 cycle and unfinished processes will go back to queue
       for _ in range(len(_UpperCAmelCase		)		):
             _A        =       ready_queue.popleft()  # current process

             # if process's arrival time is later than current time, update current time
             if self.current_time < cp.arrival_time:
                   self.current_time += cp.arrival_time

             # update waiting time of unfinished processes
             self.update_waiting_time(_UpperCAmelCase		)
             # if the burst time of process is bigger than time-slice
             if cp.burst_time > time_slice:
                   # use CPU for only time-slice
                   self.current_time += time_slice
                   # update remaining burst time
                   cp.burst_time -= time_slice
                   # update end point time
                   _A        =       self.current_time
                   # locate the process behind the queue because it is not finished
                   ready_queue.append(_UpperCAmelCase		)
             else:
                   # use CPU for remaining burst time
                   self.current_time += cp.burst_time
                   # set burst time 0 because the process is finished
                   _A        =       0
                   # set the finish time
                   _A        =       self.current_time
                   # update the process' turnaround time because it is finished
                   _A        =       self.current_time - cp.arrival_time
                   # add the process to queue that has finished queue
                   finished.append(_UpperCAmelCase		)

       self.finish_queue.extend(_UpperCAmelCase		)  # add finished process to finish queue
       # return finished processes queue and remaining processes queue
       return finished, ready_queue



 def       lowerCAmelCase_							(			self	:	str		):

       #  all queues except last one have round_robin algorithm
       for i in range(self.number_of_queues - 1		):
             _A       ,   _A        =       self.round_robin(
                 self.ready_queue   ,							self.time_slices[i]		)
       #  the last queue has first_come_first_served algorithm
       self.first_come_first_served(self.ready_queue		)

       return self.finish_queue


if __name__ == "__main__":
       import doctest

       a         =      Process('''P1''', 0, 53)
       a         =      Process('''P2''', 0, 17)
       a         =      Process('''P3''', 0, 68)
       a         =      Process('''P4''', 0, 24)
       a         =      3
       a         =      [17, 25]
       a         =      deque([Pa, Pa, Pa, Pa])

       if len(time_slices) != number_of_queues - 1:
              raise SystemExit(0)

       doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

       a         =      Process('''P1''', 0, 53)
       a         =      Process('''P2''', 0, 17)
       a         =      Process('''P3''', 0, 68)
       a         =      Process('''P4''', 0, 24)
       a         =      3
       a         =      [17, 25]
       a         =      deque([Pa, Pa, Pa, Pa])
       a         =      MLFQ(number_of_queues, time_slices, queue, 0)
       a         =      mlfq.multi_level_feedback_queue()

       # print total waiting times of processes(P1, P2, P3, P4)
       print(
           F'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
       )
       # print completion times of processes(P1, P2, P3, P4)
       print(
           F'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
       )
       # print total turnaround times of processes(P1, P2, P3, P4)
       print(
           F'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
       )
       # print sequence of finished processes
       print(
           F'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
       )

