





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class     __snake_case       :





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Dict		):
         torch.manual_seed(0		)
         __snake_case:						Tuple   =						TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

         torch.manual_seed(0		)
         __snake_case:						Tuple   =						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

         torch.manual_seed(0		)
         __snake_case:						Dict   =						UNetaDConditionModel(
             sample_size=32							,				layers_per_block=1							,				block_out_channels=[32, 64]							,				down_block_types=[
                 """ResnetDownsampleBlock2D""",
                 """SimpleCrossAttnDownBlock2D""",
             ]							,				mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""							,				up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]							,				in_channels=3							,				out_channels=6							,				cross_attention_dim=32							,				encoder_hid_dim=32							,				attention_head_dim=8							,				addition_embed_type="""text"""							,				addition_embed_type_num_heads=2							,				cross_attention_norm="""group_norm"""							,				resnet_time_scale_shift="""scale_shift"""							,				act_fn="""gelu"""							,				)
         unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

         torch.manual_seed(0		)
         __snake_case:						List[Any]   =						DDPMScheduler(
             num_train_timesteps=1_000							,				beta_schedule="""squaredcos_cap_v2"""							,				beta_start=0.0001							,				beta_end=0.02							,				thresholding=A							,				dynamic_thresholding_ratio=0.95							,				sample_max_value=1.0							,				prediction_type="""epsilon"""							,				variance_type="""learned_range"""							,				)

         torch.manual_seed(0		)
         __snake_case:						Tuple   =						IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         torch.manual_seed(0		)
         __snake_case:						List[str]   =						TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

         torch.manual_seed(0		)
         __snake_case:						Union[str, Any]   =						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

         torch.manual_seed(0		)
         __snake_case:						List[str]   =						UNetaDConditionModel(
             sample_size=32							,				layers_per_block=[1, 2]							,				block_out_channels=[32, 64]							,				down_block_types=[
                 """ResnetDownsampleBlock2D""",
                 """SimpleCrossAttnDownBlock2D""",
             ]							,				mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""							,				up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]							,				in_channels=6							,				out_channels=6							,				cross_attention_dim=32							,				encoder_hid_dim=32							,				attention_head_dim=8							,				addition_embed_type="""text"""							,				addition_embed_type_num_heads=2							,				cross_attention_norm="""group_norm"""							,				resnet_time_scale_shift="""scale_shift"""							,				act_fn="""gelu"""							,				class_embed_type="""timestep"""							,				mid_block_scale_factor=1.414							,				time_embedding_act_fn="""gelu"""							,				time_embedding_dim=32							,				)
         unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

         torch.manual_seed(0		)
         __snake_case:						Union[str, Any]   =						DDPMScheduler(
             num_train_timesteps=1_000							,				beta_schedule="""squaredcos_cap_v2"""							,				beta_start=0.0001							,				beta_end=0.02							,				thresholding=A							,				dynamic_thresholding_ratio=0.95							,				sample_max_value=1.0							,				prediction_type="""epsilon"""							,				variance_type="""learned_range"""							,				)

         torch.manual_seed(0		)
         __snake_case:						int   =						DDPMScheduler(
             num_train_timesteps=1_000							,				beta_schedule="""squaredcos_cap_v2"""							,				beta_start=0.0001							,				beta_end=0.02							,				)

         torch.manual_seed(0		)
         __snake_case:						List[str]   =						IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "image_noising_scheduler": image_noising_scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						str   =						self.get_dummy_components()
         __snake_case:						str   =						self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Union[str, Any]   =						self.get_dummy_inputs(A		)

         __snake_case:						List[str]   =						inputs["""prompt"""]
         __snake_case:						Optional[Any]   =						inputs["""generator"""]
         __snake_case:						List[str]   =						inputs["""num_inference_steps"""]
         __snake_case:						Dict   =						inputs["""output_type"""]

         if "image" in inputs:
           __snake_case:						List[Any]   =						inputs["""image"""]
         else:
           __snake_case:						Optional[int]   =						None

         if "mask_image" in inputs:
           __snake_case:						str   =						inputs["""mask_image"""]
         else:
           __snake_case:						List[str]   =						None

         if "original_image" in inputs:
           __snake_case:						Any   =						inputs["""original_image"""]
         else:
           __snake_case:						Dict   =						None

         __snake_case  ,   __snake_case:						List[str]   =						pipe.encode_prompt(A		)

         # inputs with prompt converted to embeddings
         __snake_case:						Union[str, Any]   =						{
             """prompt_embeds""": prompt_embeds,
             """negative_prompt_embeds""": negative_prompt_embeds,
             """generator""": generator,
             """num_inference_steps""": num_inference_steps,
             """output_type""": output_type,
         }

         if image is not None:
           __snake_case:						Union[str, Any]   =						image

         if mask_image is not None:
           __snake_case:						List[Any]   =						mask_image

         if original_image is not None:
           __snake_case:						List[Any]   =						original_image

         # set all optional components to None
         for optional_component in pipe._optional_components:
           setattr(A							,				A							,				A		)

         __snake_case:						str   =						pipe(**A		)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(A		)
           __snake_case:						Any   =						self.pipeline_class.from_pretrained(A		)
           pipe_loaded.to(A		)
           pipe_loaded.set_progress_bar_config(disable=A		)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

         for optional_component in pipe._optional_components:
           self.assertTrue(
               getattr(A							,				A		) is None							,				f'''`{optional_component}` did not stay set to None after loading.'''							,				)

         __snake_case:						Optional[int]   =						self.get_dummy_inputs(A		)

         __snake_case:						str   =						inputs["""generator"""]
         __snake_case:						Union[str, Any]   =						inputs["""num_inference_steps"""]
         __snake_case:						str   =						inputs["""output_type"""]

         # inputs with prompt converted to embeddings
         __snake_case:						Tuple   =						{
             """prompt_embeds""": prompt_embeds,
             """negative_prompt_embeds""": negative_prompt_embeds,
             """generator""": generator,
             """num_inference_steps""": num_inference_steps,
             """output_type""": output_type,
         }

         if image is not None:
           __snake_case:						Union[str, Any]   =						image

         if mask_image is not None:
           __snake_case:						Any   =						mask_image

         if original_image is not None:
           __snake_case:						Any   =						original_image

         __snake_case:						str   =						pipe_loaded(**A		)[0]

         __snake_case:						Optional[Any]   =						np.abs(to_np(A		) - to_np(A		)		).max()
         self.assertLess(A							,				1E-4		)






       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Union[str, Any]   =						self.get_dummy_components()
         __snake_case:						int   =						self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[Any]   =						self.get_dummy_inputs(A		)
         __snake_case:						int   =						pipe(**A		)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(A		)
           __snake_case:						Optional[int]   =						self.pipeline_class.from_pretrained(A		)
           pipe_loaded.to(A		)
           pipe_loaded.set_progress_bar_config(disable=A		)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

         __snake_case:						Any   =						self.get_dummy_inputs(A		)
         __snake_case:						Union[str, Any]   =						pipe_loaded(**A		)[0]

         __snake_case:						List[str]   =						np.abs(to_np(A		) - to_np(A		)		).max()
         self.assertLess(A							,				1E-4		)













from statistics import mean

import numpy as np







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						List[Any]   =						0
  # Number of processes finished
  __snake_case:						Union[str, Any]   =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						Dict   =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						Tuple   =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						int   =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						Any   =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

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

    __snake_case:						List[Any]   =						0
    # Index showing the location of the process being performed
    __snake_case:						Union[str, Any]   =						0
    # Saves the current response ratio.
    __snake_case:						Optional[Any]   =						0
    for i in range(0					,					SCREAMING_SNAKE_CASE__):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        __snake_case:						Tuple   =						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        __snake_case:						Union[str, Any]   =						temp
        __snake_case:						Optional[int]   =						i

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

  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						Union[str, Any]   =						[0] * no_of_process
  for i in range(0					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							5
      __UpperCAmelCase       :   Tuple				       =							["A", "B", "C", "D", "E"]
      __UpperCAmelCase       :   str				       =							[1, 2, 3, 4, 5]
      __UpperCAmelCase       :   Dict				       =							[1, 2, 3, 4, 5]

      __UpperCAmelCase       :   List[str]				       =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase       :   List[str]				       =							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}')






