
'''simple docstring'''
import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp






@slow
@require_flax
class    UpperCAmelCase_				(	unittest.TestCase					):





	def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
					return F'gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_) for s in shape])}.npy'





	def        snake_case__		(    self):
					super().tearDown()
					gc.collect()





	def        snake_case__		(    self ,       lowercase_=0 ,       lowercase_=(4, 4, 64, 64) ,       lowercase_=False):
					snake_case_ :    List[str]     			=						jnp.bfloataa if fpaa else jnp.floataa
					snake_case_ :    int     			=						jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_ ,       lowerCamelCase_)) ,       dtype=lowerCamelCase_)
					return image





	def        snake_case__		(    self ,       lowercase_=False ,       lowercase_="CompVis/stable-diffusion-v1-4"):
					snake_case_ :    List[Any]     			=						jnp.bfloataa if fpaa else jnp.floataa
					snake_case_ :    Tuple     			=						"""bf16""" if fpaa else None

					snake_case_ :    Optional[Any]     			=						FlaxUNetaDConditionModel.from_pretrained(
					    lowerCamelCase_ ,       subfolder="unet" ,       dtype=lowerCamelCase_ ,       revision=lowerCamelCase_)
					return model, params





	def        snake_case__		(    self ,       lowercase_=0 ,       lowercase_=(4, 77, 7_68) ,       lowercase_=False):
					snake_case_ :    str     			=						jnp.bfloataa if fpaa else jnp.floataa
					snake_case_ :    Dict     			=						jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_ ,       lowerCamelCase_)) ,       dtype=lowerCamelCase_)
					return hidden_states





	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
	        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
	        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
	        [3, 10_00, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
	        # fmt: on
	    ])
	def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
					snake_case_ :    Any     			=						self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4" ,       fpaa=lowerCamelCase_)
					snake_case_ :    Dict     			=						self.get_latents(lowerCamelCase_ ,       fpaa=lowerCamelCase_)
					snake_case_ :    str     			=						self.get_encoder_hidden_states(lowerCamelCase_ ,       fpaa=lowerCamelCase_)

					snake_case_ :    str     			=						model.apply(
					    {"params": params} ,       lowerCamelCase_ ,       jnp.array(lowerCamelCase_ ,       dtype=jnp.intaa) ,       encoder_hidden_states=lowerCamelCase_ ,       ).sample

					assert sample.shape == latents.shape

					snake_case_ :    Dict     			=						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())) ,       dtype=jnp.floataa)
					snake_case_ :    str     			=						jnp.array(lowerCamelCase_ ,       dtype=jnp.floataa)

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
					assert jnp.allclose(lowerCamelCase_ ,       lowerCamelCase_ ,       atol=1E-2)





	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
	        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
	        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
	        [3, 10_00, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
	        # fmt: on
	    ])
	def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_):
					snake_case_ :    List[str]     			=						self.get_unet_model(model_id="stabilityai/stable-diffusion-2" ,       fpaa=lowerCamelCase_)
					snake_case_ :    Tuple     			=						self.get_latents(lowerCamelCase_ ,       shape=(4, 4, 96, 96) ,       fpaa=lowerCamelCase_)
					snake_case_ :    Union[str, Any]     			=						self.get_encoder_hidden_states(lowerCamelCase_ ,       shape=(4, 77, 10_24) ,       fpaa=lowerCamelCase_)

					snake_case_ :    Tuple     			=						model.apply(
					    {"params": params} ,       lowerCamelCase_ ,       jnp.array(lowerCamelCase_ ,       dtype=jnp.intaa) ,       encoder_hidden_states=lowerCamelCase_ ,       ).sample

					assert sample.shape == latents.shape

					snake_case_ :    Dict     			=						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())) ,       dtype=jnp.floataa)
					snake_case_ :    Union[str, Any]     			=						jnp.array(lowerCamelCase_ ,       dtype=jnp.floataa)

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
					assert jnp.allclose(lowerCamelCase_ ,       lowerCamelCase_ ,       atol=1E-2)






'''simple docstring'''
from __future__ import annotations

from statistics import mean

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						[0] * no_of_processes
    snake_case_ :    Optional[int]     			=						[0] * no_of_processes

    # Initialize remaining_time to waiting_time.

    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    str     			=						burst_time[i]
    snake_case_ :    list[int]     			=						[]

    snake_case_ :    Tuple     			=						0
    snake_case_ :    Optional[int]     			=						0

    # When processes are not completed,
    # A process whose arrival time has passed \
    # and has remaining execution time is put into the ready_process.
    # The shortest process in the ready_process, target_process is executed.

    while completed != no_of_processes:
        snake_case_ :    Any     			=						[]
        snake_case_ :    Dict     			=						-1

        for i in range(__SCREAMING_SNAKE_CASE       ):
            if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                ready_process.append(__SCREAMING_SNAKE_CASE       )

        if len(__SCREAMING_SNAKE_CASE       ) > 0:
            snake_case_ :    Dict     			=						ready_process[0]
            for i in ready_process:
                if remaining_time[i] < remaining_time[target_process]:
                    snake_case_ :    Optional[int]     			=						i
            total_time += burst_time[target_process]
            completed += 1
            snake_case_ :    Tuple     			=						0
            snake_case_ :    int     			=						(
                total_time - arrival_time[target_process] - burst_time[target_process]
            )
        else:
            total_time += 1

    return waiting_time

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						[0] * no_of_processes
    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Dict     			=						burst_time[i] + waiting_time[i]
    return turn_around_time


if __name__ == "__main__":
  print("[TEST CASE 01]")

  a_											=						4
  a_											=						[2, 5, 3, 7]
  a_											=						[0, 0, 0, 0]
  a_											=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  a_											=						calculate_turnaroundtime(
      burst_time, no_of_processes, waiting_time
  )

  # Printing the Result
  print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
  for i, process_id in enumerate(list(range(1, 5))):
    print(
        f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
        f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
    )
  print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
  print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")




