
'''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, KandinskyInpaintPipeline, 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  A  (						SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :List[Any]   =  KandinskyInpaintPipeline
	snake_case__   :str   =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	snake_case__   :List[str]   =  [
	    'prompt',
	    'negative_prompt',
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	snake_case__   :List[Any]   =  [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'negative_prompt',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	snake_case__   :Tuple   =  False





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				return 32





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return 32





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				return self.time_input_dim





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self.time_input_dim * 4





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return 100





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"				)
				return tokenizer





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				torch.manual_seed(0				)
				lowerCAmelCase__										=				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=1005   ,				)

				lowerCAmelCase__										=				MultilingualCLIP(__magic_name__				)
				lowerCAmelCase__										=				text_encoder.eval()

				return text_encoder





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				torch.manual_seed(0				)

				lowerCAmelCase__										=				{
				    "in_channels": 9,
				    # 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,
				}

				lowerCAmelCase__										=				UNetaDConditionModel(**__magic_name__				)
				return model





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				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 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				torch.manual_seed(0				)
				lowerCAmelCase__										=				VQModel(**self.dummy_movq_kwargs				)
				return model





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.dummy_text_encoder
				lowerCAmelCase__										=				self.dummy_tokenizer
				lowerCAmelCase__										=				self.dummy_unet
				lowerCAmelCase__										=				self.dummy_movq

				lowerCAmelCase__										=				DDIMScheduler(
				    num_train_timesteps=1000   ,				beta_schedule="linear"   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				clip_sample=__magic_name__   ,				set_alpha_to_one=__magic_name__   ,				steps_offset=1   ,				prediction_type="epsilon"   ,				thresholding=__magic_name__   ,				)

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

				return components





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Union[str, Any]=0				):


				"""simple docstring"""

				lowerCAmelCase__										=				floats_tensor((1, self.cross_attention_dim)   ,				rng=random.Random(__magic_name__				)				).to(__magic_name__				)
				lowerCAmelCase__										=				floats_tensor((1, self.cross_attention_dim)   ,				rng=random.Random(seed + 1				)				).to(__magic_name__				)
				# create init_image
				lowerCAmelCase__										=				floats_tensor((1, 3, 64, 64)   ,				rng=random.Random(__magic_name__				)				).to(__magic_name__				)
				lowerCAmelCase__										=				image.cpu().permute(0   ,				2   ,				3   ,				1				)[0]
				lowerCAmelCase__										=				Image.fromarray(np.uinta(__magic_name__				)				).convert("RGB"				).resize((256, 256)				)
				# create mask
				lowerCAmelCase__										=				np.ones((64, 64)   ,				dtype=np.floataa				)
				lowerCAmelCase__										=				0

				if str(__magic_name__				).startswith("mps"				):
							lowerCAmelCase__										=				torch.manual_seed(__magic_name__				)
				else:
							lowerCAmelCase__										=				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
				lowerCAmelCase__										=				{
				    "prompt": "horse",
				    "image": init_image,
				    "mask_image": mask,
				    "image_embeds": image_embeds,
				    "negative_image_embeds": negative_image_embeds,
				    "generator": generator,
				    "height": 64,
				    "width": 64,
				    "num_inference_steps": 2,
				    "guidance_scale": 4.0,
				    "output_type": "np",
				}
				return inputs





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				"cpu"

				lowerCAmelCase__										=				self.get_dummy_components()

				lowerCAmelCase__										=				self.pipeline_class(**__magic_name__				)
				lowerCAmelCase__										=				pipe.to(__magic_name__				)

				pipe.set_progress_bar_config(disable=__magic_name__				)

				lowerCAmelCase__										=				pipe(**self.get_dummy_inputs(__magic_name__				)				)
				lowerCAmelCase__										=				output.images

				lowerCAmelCase__										=				pipe(
				    **self.get_dummy_inputs(__magic_name__				)   ,				return_dict=__magic_name__   ,				)[0]

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

				print(f"""image.shape {image.shape}"""				)

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

				lowerCAmelCase__										=				np.array(
				    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]				)

				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()}"""







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				super().test_inference_batch_single_identical(expected_max_diff=3E-3				)






@slow
@require_torch_gpu
class  A  (						unittest.TestCase     ):





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"				)

				lowerCAmelCase__										=				load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"				)
				lowerCAmelCase__										=				np.ones((768, 768)   ,				dtype=np.floataa				)
				lowerCAmelCase__										=				0

				lowerCAmelCase__										=				"a hat"

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

				lowerCAmelCase__										=				KandinskyInpaintPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-1-inpaint"   ,				torch_dtype=torch.floataa				)
				lowerCAmelCase__										=				pipeline.to(__magic_name__				)
				pipeline.set_progress_bar_config(disable=__magic_name__				)

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

				lowerCAmelCase__										=				pipeline(
				    __magic_name__   ,				image=__magic_name__   ,				mask_image=__magic_name__   ,				image_embeds=__magic_name__   ,				negative_image_embeds=__magic_name__   ,				generator=__magic_name__   ,				num_inference_steps=100   ,				height=768   ,				width=768   ,				output_type="np"   ,				)

				lowerCAmelCase__										=				output.images[0]

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(__magic_name__   ,				__magic_name__				)










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}')





