






"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

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


enable_full_determinism()






class        UpperCamelCase			(	snake_case_		,     snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       Dict			=					CycleDiffusionPipeline
 UpperCamelCase					:       Union[str, Any]			=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     '''negative_prompt''',
     '''height''',
     '''width''',
     '''negative_prompt_embeds''',
 }
 UpperCamelCase					:       str			=					PipelineTesterMixin.required_optional_params - {'''latents'''}
 UpperCamelCase					:       int			=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}      )
 UpperCamelCase					:       str			=					IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCamelCase					:       Any			=					IMAGE_TO_IMAGE_IMAGE_PARAMS


 def _lowercase	(     self		:						int							)					->       Dict:
   torch.manual_seed(0							)
   _a  :       Optional[int]             =	UNetaDConditionModel(
       block_out_channels=(32, 64)							,						layers_per_block=2							,						sample_size=32							,						in_channels=4							,						out_channels=4							,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")							,						cross_attention_dim=32							,						)
   _a  :       Any             =	DDIMScheduler(
       beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule="""scaled_linear"""							,						num_train_timesteps=1000							,						clip_sample=UpperCAmelCase__							,						set_alpha_to_one=UpperCAmelCase__							,						)
   torch.manual_seed(0							)
   _a  :       Optional[Any]             =	AutoencoderKL(
       block_out_channels=[32, 64]							,						in_channels=3							,						out_channels=3							,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,						latent_channels=4							,						)
   torch.manual_seed(0							)
   _a  :       Dict             =	CLIPTextConfig(
       bos_token_id=0							,						eos_token_id=2							,						hidden_size=32							,						intermediate_size=37							,						layer_norm_eps=1E-05							,						num_attention_heads=4							,						num_hidden_layers=5							,						pad_token_id=1							,						vocab_size=1000							,						)
   _a  :       List[Any]             =	CLIPTextModel(UpperCAmelCase__							)
   _a  :       int             =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

   _a  :       Tuple             =	{
       """unet""": unet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }
   return components


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Dict=0							)					->       Optional[int]:
   _a  :       List[str]             =	floats_tensor((1, 3, 32, 32)							,						rng=random.Random(UpperCAmelCase__							)							).to(UpperCAmelCase__							)
   _a  :       Optional[Any]             =	image / 2 + 0.5
   if str(UpperCAmelCase__							).startswith("""mps"""							):
     _a  :       Union[str, Any]             =	torch.manual_seed(UpperCAmelCase__							)
   else:
     _a  :       Union[str, Any]             =	torch.Generator(device=UpperCAmelCase__							).manual_seed(UpperCAmelCase__							)
   _a  :       List[str]             =	{
       """prompt""": """An astronaut riding an elephant""",
       """source_prompt""": """An astronaut riding a horse""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """eta""": 0.1,
       """strength""": 0.8,
       """guidance_scale""": 3,
       """source_guidance_scale""": 1,
       """output_type""": """numpy""",
   }
   return inputs


 def _lowercase	(     self		:						int							)					->       Tuple:
   _a  :       Any             =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

   _a  :       Optional[Any]             =	self.get_dummy_components()
   _a  :       Any             =	CycleDiffusionPipeline(**UpperCAmelCase__							)
   _a  :       List[Any]             =	pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[Any]             =	self.get_dummy_inputs(UpperCAmelCase__							)
   _a  :       Tuple             =	pipe(**UpperCAmelCase__							)
   _a  :       Any             =	output.images

   _a  :       List[str]             =	images[0, -3:, -3:, -1]

   assert images.shape == (1, 32, 32, 3)
   _a  :       int             =	np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]							)

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


 @unittest.skipIf(torch_device != """cuda"""							,						"""This test requires a GPU"""							)
 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       List[str]             =	self.get_dummy_components()
   for name, module in components.items():
     if hasattr(UpperCAmelCase__							,						"""half"""							):
       _a  :       List[str]             =	module.half()
   _a  :       Union[str, Any]             =	CycleDiffusionPipeline(**UpperCAmelCase__							)
   _a  :       List[str]             =	pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[int]             =	self.get_dummy_inputs(UpperCAmelCase__							)
   _a  :       Tuple             =	pipe(**UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	output.images

   _a  :       int             =	images[0, -3:, -3:, -1]

   assert images.shape == (1, 32, 32, 3)
   _a  :       int             =	np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]							)

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


 @skip_mps
 def _lowercase	(     self		:						int							)					->       str:
   return super().test_save_load_local()


 @unittest.skip("""non-deterministic pipeline"""							)
 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   return super().test_inference_batch_single_identical()


 @skip_mps
 def _lowercase	(     self		:						Union[str, Any]							)					->       List[Any]:
   return super().test_dict_tuple_outputs_equivalent()


 @skip_mps
 def _lowercase	(     self		:						Dict							)					->       Dict:
   return super().test_save_load_optional_components()







 @skip_mps
 def _lowercase	(     self		:						Union[str, Any]							)					->       Dict:
   return super().test_attention_slicing_forward_pass()







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


 def _lowercase	(     self		:						List[str]							)					->       Dict:
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def _lowercase	(     self		:						Optional[int]							)					->       Optional[int]:
   _a  :       str             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/cycle-diffusion/black_colored_car.png"""							)
   _a  :       Any             =	load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""							)
   _a  :       Dict             =	init_image.resize((512, 512)							)

   _a  :       List[str]             =	"""CompVis/stable-diffusion-v1-4"""
   _a  :       int             =	DDIMScheduler.from_pretrained(UpperCAmelCase__							,						subfolder="""scheduler"""							)
   _a  :       List[Any]             =	CycleDiffusionPipeline.from_pretrained(
       UpperCAmelCase__							,						scheduler=UpperCAmelCase__							,						safety_checker=UpperCAmelCase__							,						torch_dtype=torch.floataa							,						revision="""fp16"""							)

   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)
   pipe.enable_attention_slicing()

   _a  :       Tuple             =	"""A black colored car"""
   _a  :       List[str]             =	"""A blue colored car"""

   _a  :       Union[str, Any]             =	torch.manual_seed(0							)
   _a  :       Any             =	pipe(
       prompt=UpperCAmelCase__							,						source_prompt=UpperCAmelCase__							,						image=UpperCAmelCase__							,						num_inference_steps=100							,						eta=0.1							,						strength=0.8_5							,						guidance_scale=3							,						source_guidance_scale=1							,						generator=UpperCAmelCase__							,						output_type="""np"""							,						)
   _a  :       List[Any]             =	output.images

   # the values aren't exactly equal, but the images look the same visually
   assert np.abs(image - expected_image							).max() < 5E-1







 def _lowercase	(     self		:						Any							)					->       Any:
   _a  :       int             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/cycle-diffusion/black_colored_car.png"""							)
   _a  :       Any             =	load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""							)
   _a  :       Union[str, Any]             =	init_image.resize((512, 512)							)

   _a  :       Union[str, Any]             =	"""CompVis/stable-diffusion-v1-4"""
   _a  :       List[Any]             =	DDIMScheduler.from_pretrained(UpperCAmelCase__							,						subfolder="""scheduler"""							)
   _a  :       Dict             =	CycleDiffusionPipeline.from_pretrained(UpperCAmelCase__							,						scheduler=UpperCAmelCase__							,						safety_checker=UpperCAmelCase__							)

   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)
   pipe.enable_attention_slicing()

   _a  :       Optional[int]             =	"""A black colored car"""
   _a  :       List[Any]             =	"""A blue colored car"""

   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       List[Any]             =	pipe(
       prompt=UpperCAmelCase__							,						source_prompt=UpperCAmelCase__							,						image=UpperCAmelCase__							,						num_inference_steps=100							,						eta=0.1							,						strength=0.8_5							,						guidance_scale=3							,						source_guidance_scale=1							,						generator=UpperCAmelCase__							,						output_type="""np"""							,						)
   _a  :       Any             =	output.images

   assert np.abs(image - expected_image							).max() < 2E-2









"""simple docstring"""




from __future__ import annotations

import time

import numpy as np

_snake_case          =   [8, 5, 9, 7]
_snake_case          =   [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
_snake_case          =   [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class        UpperCamelCase			:


 def __init__(     self		:						List[Any]							,						UpperCAmelCase__		:						list[int]							,						UpperCAmelCase__		:						list[list[int]]							,						UpperCAmelCase__		:						list[list[int]]							,						)					->       None:
   _a  :       List[str]             =	claim_vector
   _a  :       List[Any]             =	allocated_resources_table
   _a  :       Union[str, Any]             =	maximum_claim_table


 def _lowercase	(     self		:						Tuple							)					->       list[int]:
   return [
       sum(p_item[i] for p_item in self.__allocated_resources_table							)
       for i in range(len(self.__allocated_resources_table[0]							)							)
   ]


 def _lowercase	(     self		:						int							)					->       list[int]:
   return np.array(self.__claim_vector							) - np.array(
       self.__processes_resource_summation()							)


 def _lowercase	(     self		:						List[str]							)					->       list[list[int]]:
   return [
       list(np.array(self.__maximum_claim_table[i]							) - np.array(UpperCAmelCase__							)							)
       for i, allocated_resource in enumerate(self.__allocated_resources_table							)
   ]


 def _lowercase	(     self		:						Optional[Any]							)					->       dict[int, list[int]]:
   return {self.__need().index(UpperCAmelCase__							): i for i in self.__need()}


 def _lowercase	(     self		:						Dict							,						**UpperCAmelCase__		:						Optional[Any]							)					->       None:
   _a  :       List[Any]             =	self.__need()
   _a  :       Optional[int]             =	self.__allocated_resources_table
   _a  :       str             =	self.__available_resources()
   _a  :       Optional[Any]             =	self.__need_index_manager()
   for kw, val in kwargs.items():
     if kw and val is True:
       self.__pretty_data()
   print("""_""" * 50 + """\n"""							)
   while need_list:
     _a  :       int             =	False
     for each_need in need_list:
       _a  :       Optional[int]             =	True
       for index, need in enumerate(UpperCAmelCase__							):
         if need > available_resources[index]:
           _a  :       List[Any]             =	False
           break
       if execution:
         _a  :       str             =	True
         # get the original index of the process from ind_ctrl db
         for original_need_index, need_clone in need_index_manager.items():
           if each_need == need_clone:
             _a  :       Any             =	original_need_index
         print(f"""Process {process_number + 1} is executing."""							)
         # remove the process run from stack
         need_list.remove(UpperCAmelCase__							)
         # update available/freed resources stack
         _a  :       Union[str, Any]             =	np.array(UpperCAmelCase__							) + np.array(
             alloc_resources_table[process_number]							)
         print(
             """Updated available resource stack for processes: """
             + """ """.join([str(UpperCAmelCase__							) for x in available_resources]							)							)
         break
     if safe:
       print("""The process is in a safe state.\n"""							)
     else:
       print("""System in unsafe state. Aborting...\n"""							)
       break







 def _lowercase	(     self		:						Any							)					->       Optional[int]:
   print(""" """ * 9 + """Allocated Resource Table"""							)
   for item in self.__allocated_resources_table:
     print(
         f"""P{self.__allocated_resources_table.index(UpperCAmelCase__							) + 1}"""
         + """ """.join(f"""{it:>8}""" for it in item							)
         + """\n"""							)
   print(""" """ * 9 + """System Resource Table"""							)
   for item in self.__maximum_claim_table:
     print(
         f"""P{self.__maximum_claim_table.index(UpperCAmelCase__							) + 1}"""
         + """ """.join(f"""{it:>8}""" for it in item							)
         + """\n"""							)
   print(
       """Current Usage by Active Processes: """
       + """ """.join(str(UpperCAmelCase__							) for x in self.__claim_vector							)							)
   print(
       """Initial Available Resources:       """
       + """ """.join(str(UpperCAmelCase__							) for x in self.__available_resources()							)							)
   time.sleep(1							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

