
'''simple docstring'''



from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

UpperCamelCase__   :		Union[str, Any]											=	'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def  lowerCAmelCase_						(					SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_=8    )  ->  List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE  			=					h // scale_factor**2
   if h % scale_factor**2 != 0:
      new_h += 1
   _SCREAMING_SNAKE_CASE  			=					w // scale_factor**2
   if w % scale_factor**2 != 0:
      new_w += 1
   return new_h * scale_factor, new_w * scale_factor







class  _a					(_lowercase):





       """simple docstring"""




       def __init__(				self							,				A__							,				A__							,				A__							,				A__							,				A__							,				)     ->      Union[str, Any]:
          super().__init__()

          self.register_modules(
              text_encoder=A__							,				tokenizer=A__							,				unet=A__							,				scheduler=A__							,				movq=A__							,				)
          _SCREAMING_SNAKE_CASE  			=					2 ** (len(self.movq.config.block_out_channels   ) - 1)






       def 							UpperCamelCase					(				self							,				A__							,				A__							,				A__							,				A__							,				A__							,				A__   )     ->      int:
          if latents is None:
             _SCREAMING_SNAKE_CASE  			=					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}"   )
             _SCREAMING_SNAKE_CASE  			=					latents.to(A__   )

          _SCREAMING_SNAKE_CASE  			=					latents * scheduler.init_noise_sigma
          return latents






       def 							UpperCamelCase					(				self							,				A__							,				A__							,				A__							,				A__							,				A__=None							,				)     ->      Any:
          _SCREAMING_SNAKE_CASE  			=					len(A__   ) if isinstance(A__							,				A__   ) else 1
          # get prompt text embeddings
          _SCREAMING_SNAKE_CASE  			=					self.tokenizer(
              A__							,				padding="""max_length"""							,				truncation=A__							,				max_length=77							,				return_attention_mask=A__							,				add_special_tokens=A__							,				return_tensors="""pt"""							,				)

          _SCREAMING_SNAKE_CASE  			=					text_inputs.input_ids
          _SCREAMING_SNAKE_CASE  			=					self.tokenizer(A__							,				padding="""longest"""							,				return_tensors="""pt"""   ).input_ids

          if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A__							,				A__   ):
             _SCREAMING_SNAKE_CASE  			=					self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
             logger.warning(
                 """The following part of your input was truncated because CLIP can only handle sequences up to"""
                 F" {self.tokenizer.model_max_length} tokens: {removed_text}"   )

          _SCREAMING_SNAKE_CASE  			=					text_input_ids.to(A__   )
          _SCREAMING_SNAKE_CASE  			=					text_inputs.attention_mask.to(A__   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self.text_encoder(
              input_ids=A__							,				attention_mask=A__   )

          _SCREAMING_SNAKE_CASE  			=					prompt_embeds.repeat_interleave(A__							,				dim=0   )
          _SCREAMING_SNAKE_CASE  			=					text_encoder_hidden_states.repeat_interleave(A__							,				dim=0   )
          _SCREAMING_SNAKE_CASE  			=					text_mask.repeat_interleave(A__							,				dim=0   )

          if do_classifier_free_guidance:
             _SCREAMING_SNAKE_CASE  			=					42
             if negative_prompt is None:
                _SCREAMING_SNAKE_CASE  			=					[""""""] * batch_size
             elif type(A__   ) is not type(A__   ):
                raise TypeError(
                    F"`negative_prompt` should be the same type to `prompt`, but got {type(A__   )} !="
                    F" {type(A__   )}."   )
             elif isinstance(A__							,				A__   ):
                _SCREAMING_SNAKE_CASE  			=					[negative_prompt]
             elif batch_size != len(A__   ):
                raise ValueError(
                    F"`negative_prompt`: {negative_prompt} has batch size {len(A__   )}, but `prompt`:"
                    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    """ the batch size of `prompt`."""   )
             else:
                _SCREAMING_SNAKE_CASE  			=					negative_prompt

             _SCREAMING_SNAKE_CASE  			=					self.tokenizer(
                 A__							,				padding="""max_length"""							,				max_length=77							,				truncation=A__							,				return_attention_mask=A__							,				add_special_tokens=A__							,				return_tensors="""pt"""							,				)
             _SCREAMING_SNAKE_CASE  			=					uncond_input.input_ids.to(A__   )
             _SCREAMING_SNAKE_CASE  			=					uncond_input.attention_mask.to(A__   )

             _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self.text_encoder(
                 input_ids=A__							,				attention_mask=A__   )

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

             _SCREAMING_SNAKE_CASE  			=					negative_prompt_embeds.shape[1]
             _SCREAMING_SNAKE_CASE  			=					negative_prompt_embeds.repeat(1							,				A__   )
             _SCREAMING_SNAKE_CASE  			=					negative_prompt_embeds.view(batch_size * num_images_per_prompt							,				A__   )

             _SCREAMING_SNAKE_CASE  			=					uncond_text_encoder_hidden_states.shape[1]
             _SCREAMING_SNAKE_CASE  			=					uncond_text_encoder_hidden_states.repeat(1							,				A__							,				1   )
             _SCREAMING_SNAKE_CASE  			=					uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt							,				A__							,				-1   )
             _SCREAMING_SNAKE_CASE  			=					uncond_text_mask.repeat_interleave(A__							,				dim=0   )

             # done duplicates

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             _SCREAMING_SNAKE_CASE  			=					torch.cat([negative_prompt_embeds, prompt_embeds]   )
             _SCREAMING_SNAKE_CASE  			=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

             _SCREAMING_SNAKE_CASE  			=					torch.cat([uncond_text_mask, text_mask]   )

          return prompt_embeds, text_encoder_hidden_states, text_mask






       def 							UpperCamelCase					(				self							,				A__=0   )     ->      str:
          if is_accelerate_available():
             from accelerate import cpu_offload
          else:
             raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

          _SCREAMING_SNAKE_CASE  			=					torch.device(F"cuda:{gpu_id}"   )

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






       def 							UpperCamelCase					(				self							,				A__=0   )     ->      str:
          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."""   )

          _SCREAMING_SNAKE_CASE  			=					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)

          _SCREAMING_SNAKE_CASE  			=					None
          for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
             _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					cpu_offload_with_hook(A__							,				A__							,				prev_module_hook=A__   )

          if self.safety_checker is not None:
             _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					cpu_offload_with_hook(self.safety_checker							,				A__							,				prev_module_hook=A__   )

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






       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 							UpperCamelCase					(				self   )     ->      List[str]:
          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__							,				A__ = None							,				A__ = 5_12							,				A__ = 5_12							,				A__ = 1_00							,				A__ = 4.0							,				A__ = 1							,				A__ = None							,				A__ = None							,				A__ = "pil"							,				A__ = True							,				)     ->      Any:

          if isinstance(A__							,				A__   ):
             _SCREAMING_SNAKE_CASE  			=					1
          elif isinstance(A__							,				A__   ):
             _SCREAMING_SNAKE_CASE  			=					len(A__   )
          else:
             raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(A__   )}"   )

          _SCREAMING_SNAKE_CASE  			=					self._execution_device

          _SCREAMING_SNAKE_CASE  			=					batch_size * num_images_per_prompt
          _SCREAMING_SNAKE_CASE  			=					guidance_scale > 1.0

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._encode_prompt(
              A__							,				A__							,				A__							,				A__							,				A__   )

          if isinstance(A__							,				A__   ):
             _SCREAMING_SNAKE_CASE  			=					torch.cat(A__							,				dim=0   )
          if isinstance(A__							,				A__   ):
             _SCREAMING_SNAKE_CASE  			=					torch.cat(A__							,				dim=0   )

          if do_classifier_free_guidance:
             _SCREAMING_SNAKE_CASE  			=					image_embeds.repeat_interleave(A__							,				dim=0   )
             _SCREAMING_SNAKE_CASE  			=					negative_image_embeds.repeat_interleave(A__							,				dim=0   )

          _SCREAMING_SNAKE_CASE  			=					torch.cat([negative_image_embeds, image_embeds]							,				dim=0   ).to(
              dtype=prompt_embeds.dtype							,				device=A__   )

          self.scheduler.set_timesteps(A__							,				device=A__   )
          _SCREAMING_SNAKE_CASE  			=					self.scheduler.timesteps

          _SCREAMING_SNAKE_CASE  			=					self.unet.config.in_channels

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					get_new_h_w(A__							,				A__							,				self.movq_scale_factor   )

          # create initial latent
          _SCREAMING_SNAKE_CASE  			=					self.prepare_latents(
              (batch_size, num_channels_latents, height, width)							,				text_encoder_hidden_states.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
             _SCREAMING_SNAKE_CASE  			=					torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

             _SCREAMING_SNAKE_CASE  			=					{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
             _SCREAMING_SNAKE_CASE  			=					self.unet(
                 sample=A__							,				timestep=A__							,				encoder_hidden_states=A__							,				added_cond_kwargs=A__							,				return_dict=A__							,				)[0]

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

             # compute the previous noisy sample x_t -> x_t-1
             _SCREAMING_SNAKE_CASE  			=					self.scheduler.step(
                 A__							,				A__							,				A__							,				generator=A__							,				).prev_sample
          # post-processing
          _SCREAMING_SNAKE_CASE  			=					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"]:
             _SCREAMING_SNAKE_CASE  			=					image * 0.5 + 0.5
             _SCREAMING_SNAKE_CASE  			=					image.clamp(0							,				1   )
             _SCREAMING_SNAKE_CASE  			=					image.cpu().permute(0							,				2							,				3							,				1   ).float().numpy()

          if output_type == "pil":
             _SCREAMING_SNAKE_CASE  			=					self.numpy_to_pil(A__   )

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__   )











from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

 # Sort by arrival time.
 a__												= [burst_time[i] for i in np.argsort(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  a__												= 0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   a__												= arrival_time[i]

  a__												= 0
  # Index showing the location of the process being performed
  a__												= 0
  # Saves the current response ratio.
  a__												= 0
  for i in range(0							,  __UpperCAmelCase       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    a__												= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    a__												= temp
    a__												= i

        # Calculate the turn around time
  a__												= current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  a__												= 1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     a_      :      List[Any]    =     5
     a_      :      Optional[int]    =     ['A', 'B', 'C', 'D', 'E']
     a_      :      Union[str, Any]    =     [1, 2, 3, 4, 5]
     a_      :      Dict    =     [1, 2, 3, 4, 5]

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
              f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
          )

     print(f'average waiting time : {mean(waiting_time):.5f}')
     print(f'average turn around time : {mean(turn_around_time):.5f}')





