





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class    UpperCAmelCase	(       __lowercase			,       unittest.TestCase				):
 __lowercase   		=				KandinskyImgaImgPipeline
 __lowercase   		=				["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
 __lowercase   		=				[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 __lowercase   		=				[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase   		=				False
 @property
 def      UpperCAmelCase_							(				self      :List[Any]							)->							int:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :Tuple							)->							Optional[int]:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :List[Any]							)->							List[Any]:
     return self.time_input_dim
 @property
 def      UpperCAmelCase_							(				self      :List[str]							)->							List[Any]:
     return self.time_input_dim * 4
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							str:
     return 1_00
 @property
 def      UpperCAmelCase_							(				self      :int							)->							List[str]:
     A__				=			XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"							)
     return tokenizer
 @property
 def      UpperCAmelCase_							(				self      :List[Any]							)->							List[str]:
     torch.manual_seed(0							)
     A__				=			MCLIPConfig(
         numDims=self.cross_attention_dim						,       transformerDimensions=self.text_embedder_hidden_size						,       hidden_size=self.text_embedder_hidden_size						,       intermediate_size=37						,       num_attention_heads=4						,       num_hidden_layers=5						,       vocab_size=10_05						,       )

     A__				=			MultilingualCLIP(SCREAMING_SNAKE_CASE__							)
     A__				=			text_encoder.eval()

     return text_encoder
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Union[str, Any]:
     torch.manual_seed(0							)

     A__				=			{
         "in_channels": 4,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "text_image",
         "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
         "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
         "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "layers_per_block": 1,
         "encoder_hid_dim": self.text_embedder_hidden_size,
         "encoder_hid_dim_type": "text_image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     A__				=			UNetaDConditionModel(**SCREAMING_SNAKE_CASE__							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Tuple:
     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     torch.manual_seed(0							)
     A__				=			VQModel(**self.dummy_movq_kwargs							)
     return model
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     A__				=			self.dummy_text_encoder
     A__				=			self.dummy_tokenizer
     A__				=			self.dummy_unet
     A__				=			self.dummy_movq

     A__				=			{
         "num_train_timesteps": 10_00,
         "beta_schedule": "linear",
         "beta_start": 0.0_0_0_8_5,
         "beta_end": 0.0_1_2,
         "clip_sample": False,
         "set_alpha_to_one": False,
         "steps_offset": 0,
         "prediction_type": "epsilon",
         "thresholding": False,
     }

     A__				=			DDIMScheduler(**SCREAMING_SNAKE_CASE__							)

     A__				=			{
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

     return components
 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :List[str]						,       lowercase_      :int=0							)->							Dict:
     A__				=			floats_tensor((1, self.cross_attention_dim)						,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
     A__				=			floats_tensor((1, self.cross_attention_dim)						,       rng=random.Random(seed + 1							)							).to(SCREAMING_SNAKE_CASE__							)
     # create init_image
     A__				=			floats_tensor((1, 3, 64, 64)						,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
     A__				=			image.cpu().permute(0						,       2						,       3						,       1							)[0]
     A__				=			Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("RGB"							).resize((2_56, 2_56)							)

     if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
         A__				=			torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     else:
         A__				=			torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
     A__				=			{
         "prompt": "horse",
         "image": init_image,
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "num_inference_steps": 10,
         "guidance_scale": 7.0,
         "strength": 0.2,
         "output_type": "np",
     }
     return inputs



 def      UpperCAmelCase_							(				self      :Tuple							)->							str:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

     A__				=			self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
     A__				=			pipe.to(SCREAMING_SNAKE_CASE__							)

     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     A__				=			pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)							)
     A__				=			output.images

     A__				=			pipe(
         **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)						,       return_dict=SCREAMING_SNAKE_CASE__						,       )[0]

     A__				=			image[0, -3:, -3:, -1]
     A__				=			image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     A__				=			np.array(
         [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]							)
     assert (
         np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Tuple:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def      UpperCAmelCase_							(				self      :str							)->							Union[str, Any]:
     A__				=			load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinsky/kandinsky_img2img_frog.npy"							)

     A__				=			load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"							)
     A__				=			"A red cartoon frog, 4k"

     A__				=			KandinskyPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1-prior"						,       torch_dtype=torch.floataa							)
     pipe_prior.to(SCREAMING_SNAKE_CASE__							)

     A__				=			KandinskyImgaImgPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1"						,       torch_dtype=torch.floataa							)
     A__				=			pipeline.to(SCREAMING_SNAKE_CASE__							)

     pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     A__				=			torch.Generator(device="cpu"							).manual_seed(0							)
     A__,	A__				=			pipe_prior(
         SCREAMING_SNAKE_CASE__						,       generator=SCREAMING_SNAKE_CASE__						,       num_inference_steps=5						,       negative_prompt=""						,       ).to_tuple()

     A__				=			pipeline(
         SCREAMING_SNAKE_CASE__						,       image=SCREAMING_SNAKE_CASE__						,       image_embeds=SCREAMING_SNAKE_CASE__						,       negative_image_embeds=SCREAMING_SNAKE_CASE__						,       generator=SCREAMING_SNAKE_CASE__						,       num_inference_steps=1_00						,       height=7_68						,       width=7_68						,       strength=0.2						,       output_type="np"						,       )

     A__				=			output.images[0]

     assert image.shape == (7_68, 7_68, 3)

     assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__							)





from __future__ import annotations

from statistics import mean




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int							)			->   list[int]:
				__lowerCamelCase    					=	[0] * no_of_processes
				__lowerCamelCase    					=	[0] * no_of_processes

				# Initialize remaining_time to waiting_time.

				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	burst_time[i]
				__lowerCamelCase    					=	[]

				__lowerCamelCase    					=	0
				__lowerCamelCase    					=	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:
								__lowerCamelCase    					=	[]
								__lowerCamelCase    					=	-1

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

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

				return waiting_time




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						list[int]							)			->   list[int]:
				__lowerCamelCase    					=	[0] * no_of_processes
				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	burst_time[i] + waiting_time[i]
				return turn_around_time


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

					SCREAMING_SNAKE_CASE__   :			Tuple												=    4
					SCREAMING_SNAKE_CASE__   :			Optional[int]												=    [2, 5, 3, 7]
					SCREAMING_SNAKE_CASE__   :			List[str]												=    [0, 0, 0, 0]
					SCREAMING_SNAKE_CASE__   :			str												=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    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}')



