

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,
)


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

SCREAMING_SNAKE_CASE_        =      '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Optional[Any]=8 )   ->       List[Any]:
				_UpperCAmelCase       :		Optional[Any] 		=    height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_UpperCAmelCase       :		Optional[int] 		=    width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					A_						,					A_						,					A_						,					):







										'''simple docstring'''


										super().__init__()

										self.register_modules(
										    unet=A_						,					scheduler=A_						,					movq=A_						,					)
										_UpperCAmelCase       :		Optional[Any] 		=    2 ** (len(self.movq.config.block_out_channels	) - 1)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase       :		int 		=    randn_tensor(A_						,					generator=A_						,					device=A_						,					dtype=A_	)
										else:
														if latents.shape != shape:
																		raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'	)
														_UpperCAmelCase       :		Any 		=    latents.to(A_	)

										_UpperCAmelCase       :		Dict 		=    latents * scheduler.init_noise_sigma
										return latents


						def        _UpperCAmelCase				(       self						,					A_=0	):







										'''simple docstring'''


										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError("Please install accelerate via `pip install accelerate`"	)

										_UpperCAmelCase       :		Any 		=    torch.device(f'cuda:{gpu_id}'	)

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


						def        _UpperCAmelCase				(       self						,					A_=0	):







										'''simple docstring'''


										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."	)

										_UpperCAmelCase       :		Optional[Any] 		=    torch.device(f'cuda:{gpu_id}'	)

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

										_UpperCAmelCase       :		Dict 		=    None
										for cpu_offloaded_model in [self.unet, self.movq]:
														_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    cpu_offload_with_hook(A_						,					A_						,					prev_module_hook=A_	)

										# We'll offload the last model manually.
										_UpperCAmelCase       :		Any 		=    hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if not hasattr(self.unet						,					"_hf_hook"	):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(A_						,					"_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(A_	)
						def __call__(       self						,					A_						,					A_						,					A_ = 512						,					A_ = 512						,					A_ = 100						,					A_ = 4.0						,					A_ = 1						,					A_ = None						,					A_ = None						,					A_ = "pil"						,					A_ = True						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self._execution_device

										_UpperCAmelCase       :		Optional[int] 		=    guidance_scale > 1.0

										if isinstance(A_						,					A_	):
														_UpperCAmelCase       :		str 		=    torch.cat(A_						,					dim=0	)
										_UpperCAmelCase       :		Dict 		=    image_embeds.shape[0] * num_images_per_prompt
										if isinstance(A_						,					A_	):
														_UpperCAmelCase       :		List[str] 		=    torch.cat(A_						,					dim=0	)

										if do_classifier_free_guidance:
														_UpperCAmelCase       :		Tuple 		=    image_embeds.repeat_interleave(A_						,					dim=0	)
														_UpperCAmelCase       :		Optional[int] 		=    negative_image_embeds.repeat_interleave(A_						,					dim=0	)

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

										self.scheduler.set_timesteps(A_						,					device=A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.scheduler.timesteps

										_UpperCAmelCase       :		Any 		=    self.unet.config.in_channels

										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    downscale_height_and_width(A_						,					A_						,					self.movq_scale_factor	)

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

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

														_UpperCAmelCase       :		List[Any] 		=    {"image_embeds": image_embeds}
														_UpperCAmelCase       :		Dict 		=    self.unet(
														    sample=A_						,					timestep=A_						,					encoder_hidden_states=A_						,					added_cond_kwargs=A_						,					return_dict=A_						,					)[0]

														if do_classifier_free_guidance:
																		_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    noise_pred.split(latents.shape[1]						,					dim=1	)
																		_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    noise_pred.chunk(2	)
																		_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    variance_pred.chunk(2	)
																		_UpperCAmelCase       :		int 		=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		_UpperCAmelCase       :		Dict 		=    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"]
														):
																		_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    noise_pred.split(latents.shape[1]						,					dim=1	)

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

										if output_type == "pil":
														_UpperCAmelCase       :		Union[str, Any] 		=    self.numpy_to_pil(A_	)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=A_	)





from __future__ import annotations

import pandas as pd

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       list[int]:
				_UpperCAmelCase       :		List[Any] 		=    [0] * no_of_processes
				_UpperCAmelCase       :		Dict 		=    [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    burst_time[i]

				_UpperCAmelCase       :		Any 		=    0
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		str 		=    9_9999_9999
				_UpperCAmelCase       :		List[Any] 		=    0
				_UpperCAmelCase       :		Any 		=    False

				# Process until all processes are completed
				while complete != no_of_processes:
								for j in range(lowerCAmelCase ):
												if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																if remaining_time[j] < minm:
																				_UpperCAmelCase       :		Optional[int] 		=    remaining_time[j]
																				_UpperCAmelCase       :		int 		=    j
																				_UpperCAmelCase       :		Tuple 		=    True

								if not check:
												increment_time += 1
												continue
								remaining_time[short] -= 1

								_UpperCAmelCase       :		List[Any] 		=    remaining_time[short]
								if minm == 0:
												_UpperCAmelCase       :		List[Any] 		=    9_9999_9999

								if remaining_time[short] == 0:
												complete += 1
												_UpperCAmelCase       :		int 		=    False

												# Find finish time of current process
												_UpperCAmelCase       :		Optional[int] 		=    increment_time + 1

												# Calculate waiting time
												_UpperCAmelCase       :		str 		=    finish_time - arrival_time[short]
												_UpperCAmelCase       :		Union[str, Any] 		=    finar - burst_time[short]

												if waiting_time[short] < 0:
																_UpperCAmelCase       :		Optional[int] 		=    0

        # Increment time
								increment_time += 1
				return waiting_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  list[int] )   ->       list[int]:
				_UpperCAmelCase       :		str 		=    [0] * no_of_processes
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    burst_time[i] + waiting_time[i]
				return turn_around_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       None:
				_UpperCAmelCase       :		Dict 		=    0
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    total_waiting_time + waiting_time[i]
								_UpperCAmelCase       :		int 		=    total_turn_around_time + turn_around_time[i]
				print(F'Average waiting time = {total_waiting_time / no_of_processes:.5f}' )
				print("Average turn around time ="  ,      total_turn_around_time / no_of_processes )


if __name__ == "__main__":
					print('Enter how many process you want to analyze')
					SCREAMING_SNAKE_CASE_        =      int(input())
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print('Enter the arrival time and burst time for process:--' + str(i + 1))
										SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      map(int, input().split())

					SCREAMING_SNAKE_CASE_        =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      burst_time
					SCREAMING_SNAKE_CASE_        =      no_of_processes
					SCREAMING_SNAKE_CASE_        =      waiting_time
					SCREAMING_SNAKE_CASE_        =      calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        'Process',
					        'BurstTime',
					        'ArrivalTime',
					        'WaitingTime',
					        'TurnAroundTime',
					    ],
					)

					# Printing the dataFrame
					pd.set_option('display.max_rows', fcfs.shape[0] + 1)
					print(fcfs)


