'''simple docstring'''


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      KandinskyVaaPriorPipeline
							a_        =      ['''prompt''']
							a_        =      ['''prompt''', '''negative_prompt''']
							a_        =      [
							    '''num_images_per_prompt''',
							    '''generator''',
							    '''num_inference_steps''',
							    '''latents''',
							    '''negative_prompt''',
							    '''guidance_scale''',
							    '''output_type''',
							    '''return_dict''',
							]
							a_        =      False





							@property
							def       snake_case__						(      self				:							Union[str, Any]	)							->	int:
								return 3_2





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[int]:
								return 3_2





							@property
							def       snake_case__						(      self				:							Union[str, Any]	)							->	Union[str, Any]:
								return self.time_input_dim





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	List[str]:
								return self.time_input_dim * 4





							@property
							def       snake_case__						(      self				:							Any	)							->	Dict:
								return 1_0_0





							@property
							def       snake_case__						(      self				:							str	)							->	Any:
								__UpperCAmelCase							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)
								return tokenizer





							@property
							def       snake_case__						(      self				:							List[Any]	)							->	Union[str, Any]:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPTextConfig(
								    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=self.text_embedder_hidden_size     ,		projection_dim=self.text_embedder_hidden_size     ,		intermediate_size=3_7     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
								return CLIPTextModelWithProjection(__a	)





							@property
							def       snake_case__						(      self				:							Optional[int]	)							->	List[Any]:
								torch.manual_seed(0	)

								__UpperCAmelCase							=			{
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 1_2,
								    '''embedding_dim''': self.text_embedder_hidden_size,
								    '''num_layers''': 1,
								}

								__UpperCAmelCase							=			PriorTransformer(**__a	)
								# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
								__UpperCAmelCase							=			nn.Parameter(torch.ones(model.clip_std.shape	)	)
								return model





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	Dict:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size     ,		image_size=2_2_4     ,		projection_dim=self.text_embedder_hidden_size     ,		intermediate_size=3_7     ,		num_attention_heads=4     ,		num_channels=3     ,		num_hidden_layers=5     ,		patch_size=1_4     ,		)

								__UpperCAmelCase							=			CLIPVisionModelWithProjection(__a	)
								return model





							@property
							def       snake_case__						(      self				:							Dict	)							->	Tuple:
								__UpperCAmelCase							=			CLIPImageProcessor(
								    crop_size=2_2_4     ,		do_center_crop=__a     ,		do_normalize=__a     ,		do_resize=__a     ,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,		resample=3     ,		size=2_2_4     ,		)

								return image_processor





							def       snake_case__						(      self				:							Optional[int]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			self.dummy_prior
								__UpperCAmelCase							=			self.dummy_image_encoder
								__UpperCAmelCase							=			self.dummy_text_encoder
								__UpperCAmelCase							=			self.dummy_tokenizer
								__UpperCAmelCase							=			self.dummy_image_processor

								__UpperCAmelCase							=			UnCLIPScheduler(
								    variance_type='''fixed_small_log'''     ,		prediction_type='''sample'''     ,		num_train_timesteps=1_0_0_0     ,		clip_sample=__a     ,		clip_sample_range=1_0.0     ,		)

								__UpperCAmelCase							=			{
								    '''prior''': prior,
								    '''image_encoder''': image_encoder,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''scheduler''': scheduler,
								    '''image_processor''': image_processor,
								}

								return components





							def       snake_case__						(      self				:							Any     ,		__a				:							Any     ,		__a				:							str=0	)							->	Optional[int]:
								if str(__a	).startswith('''mps'''	):
									__UpperCAmelCase							=			torch.manual_seed(__a	)
								else:
									__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
								__UpperCAmelCase							=			{
								    '''prompt''': '''horse''',
								    '''generator''': generator,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Optional[int]:
								__UpperCAmelCase							=			'''cpu'''

								__UpperCAmelCase							=			self.get_dummy_components()

								__UpperCAmelCase							=			self.pipeline_class(**__a	)
								__UpperCAmelCase							=			pipe.to(__a	)

								pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			pipe(**self.get_dummy_inputs(__a	)	)
								__UpperCAmelCase							=			output.image_embeds

								__UpperCAmelCase							=			pipe(
								    **self.get_dummy_inputs(__a	)     ,		return_dict=__a     ,		)[0]

								__UpperCAmelCase							=			image[0, -1_0:]
								__UpperCAmelCase							=			image_from_tuple[0, -1_0:]

								assert image.shape == (1, 3_2)

								__UpperCAmelCase							=			np.array(
								    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]	)

								assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2





							@skip_mps
							def       snake_case__						(      self				:							List[Any]	)							->	Any:
								__UpperCAmelCase							=			torch_device == '''cpu'''
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			False

								self._test_inference_batch_single_identical(
								    test_max_difference=__a     ,		relax_max_difference=__a     ,		test_mean_pixel_difference=__a     ,		)






							@skip_mps
							def       snake_case__						(      self				:							int	)							->	List[str]:
								__UpperCAmelCase							=			torch_device == '''cpu'''
								__UpperCAmelCase							=			False

								self._test_attention_slicing_forward_pass(
								    test_max_difference=__a     ,		test_mean_pixel_difference=__a     ,		)

'''simple docstring'''


from __future__ import annotations

from statistics import mean





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	__UpperCAmelCase							=			[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i]
	__UpperCAmelCase							=			[]

	__UpperCAmelCase							=			0
	__UpperCAmelCase							=			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:
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			-1

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

		if len(UpperCamelCase__       ) > 0:
			__UpperCAmelCase							=			ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					__UpperCAmelCase							=			i
			total_time += burst_time[target_process]
			completed += 1
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   list[int]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i] + waiting_time[i]
	return turn_around_time


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

					__lowerCAmelCase	:       List[Any]						       =	4
					__lowerCAmelCase	:       List[Any]						       =	[2, 5, 3, 7]
					__lowerCAmelCase	:       Tuple						       =	[0, 0, 0, 0]
					__lowerCAmelCase	:       Optional[int]						       =	calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					__lowerCAmelCase	:       Dict						       =	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}""")
