import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      StableUnCLIPImgaImgPipeline
    __a  =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    __a  =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __a  =      frozenset(
        []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __a  =      frozenset([]    )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      32
     SCREAMING_SNAKE_CASE__:   Optional[int]=      embedder_hidden_size

     # image encoding components

     SCREAMING_SNAKE_CASE__:   Any=      CLIPImageProcessor(crop_size=32 , size=32   )

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=lowerCAmelCase , projection_dim=lowerCAmelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )   )

     # regular denoising components

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Any=      StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''   )

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Any=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCAmelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )   )

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      UNetaDConditionModel(
         sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCAmelCase , layers_per_block=1 , upcast_attention=lowerCAmelCase , use_linear_projection=lowerCAmelCase , )

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Tuple=      DDIMScheduler(
         beta_schedule='''scaled_linear''' , beta_start=0.00085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=lowerCAmelCase , steps_offset=1 , )

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   int=      AutoencoderKL()

     SCREAMING_SNAKE_CASE__:   List[str]=      {
         # image encoding components
         '''feature_extractor''': feature_extractor,
         '''image_encoder''': image_encoder.eval(),
         # image noising components
         '''image_normalizer''': image_normalizer.eval(),
         '''image_noising_scheduler''': image_noising_scheduler,
         # regular denoising components
         '''tokenizer''': tokenizer,
         '''text_encoder''': text_encoder.eval(),
         '''unet''': unet.eval(),
         '''scheduler''': scheduler,
         '''vae''': vae.eval(),
     }

     return components
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=0 , lowerCAmelCase=True   )       ->					Tuple:
     if str(lowerCAmelCase   ).startswith('''mps'''   ):
      SCREAMING_SNAKE_CASE__:   Tuple=      torch.manual_seed(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   int=      torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )

     if pil_image:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      input_image * 0.5 + 0.5
      SCREAMING_SNAKE_CASE__:   Tuple=      input_image.clamp(0 , 1   )
      SCREAMING_SNAKE_CASE__:   Optional[int]=      input_image.cpu().permute(0 , 2 , 3 , 1   ).float().numpy()
      SCREAMING_SNAKE_CASE__:   Optional[int]=      DiffusionPipeline.numpy_to_pil(lowerCAmelCase   )[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }
    @skip_mps
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   str=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.get_dummy_components()
     SCREAMING_SNAKE_CASE__:   Tuple=      StableUnCLIPImgaImgPipeline(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      sd_pipe.to(lowerCAmelCase   )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      self.get_dummy_inputs(lowerCAmelCase   )
     inputs.update({'''image_embeds''': None}   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      sd_pipe(**lowerCAmelCase   ).images
     SCREAMING_SNAKE_CASE__:   Dict=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE__:   Optional[int]=      np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      torch_device in ['''cpu''', '''mps''']

     self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch_device in ['''cpu''', '''mps''']

     self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase   )



    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCAmelCase   )






@slow
@require_torch_gpu
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Tuple=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''   )

     SCREAMING_SNAKE_CASE__:   List[str]=      StableUnCLIPImgaImgPipeline.from_pretrained(
         '''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa   )
     pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__:   List[Any]=      torch.Generator(device='''cpu'''   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Dict=      pipe(lowerCAmelCase , '''anime turle''' , generator=lowerCAmelCase , output_type='''np'''   )

     SCREAMING_SNAKE_CASE__:   List[str]=      output.images[0]

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

     assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   Dict=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''   )

     SCREAMING_SNAKE_CASE__:   Any=      StableUnCLIPImgaImgPipeline.from_pretrained(
         '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa   )
     pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__:   List[str]=      torch.Generator(device='''cpu'''   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Any=      pipe(lowerCAmelCase , '''anime turle''' , generator=lowerCAmelCase , output_type='''np'''   )

     SCREAMING_SNAKE_CASE__:   str=      output.images[0]

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

     assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase   )



    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   str=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''   )

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__:   Tuple=      StableUnCLIPImgaImgPipeline.from_pretrained(
         '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa   )
     SCREAMING_SNAKE_CASE__:   Any=      pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__:   List[str]=      pipe(
         lowerCAmelCase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )

     SCREAMING_SNAKE_CASE__:   List[Any]=      torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9


from __future__ import annotations

from statistics import mean







def 			A__      (	snake_case_ :		list[int]       ,				snake_case_ :		list[int]       ,				snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Dict=      [0] * no_of_processes
 SCREAMING_SNAKE_CASE__:   Dict=      [0] * no_of_processes

 # Initialize remaining_time to waiting_time.

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

 SCREAMING_SNAKE_CASE__:   List[Any]=      0
 SCREAMING_SNAKE_CASE__:   List[Any]=      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:
  SCREAMING_SNAKE_CASE__:   Optional[int]=      []
  SCREAMING_SNAKE_CASE__:   List[str]=      -1

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

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

 return waiting_time







def 			A__      (	snake_case_ :		list[int]       ,				snake_case_ :		int       ,				snake_case_ :		list[int]      ):
 SCREAMING_SNAKE_CASE__:   List[str]=      [0] * no_of_processes
 for i in range(snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Dict=      burst_time[i] + waiting_time[i]
 return turn_around_time


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

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

