"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_a      :		Dict           =   False
class 						__A							( unittest.TestCase		):
     pass







@nightly
@require_torch_gpu
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""				,     torch_dtype=torch.floataa  )
       pipe.to(lowercase_  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       _lowerCAmelCase						:       List[Any]  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""  )

       _lowerCAmelCase						:       Optional[Any]  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Optional[Any]  			=     pipe.dual_guided(
           prompt="""first prompt"""				,     image=lowercase_				,     text_to_image_strength=0.7_5				,     generator=lowercase_				,     guidance_scale=7.5				,     num_inference_steps=2				,     output_type="""numpy"""				,     ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(lowercase_  )
         _lowerCAmelCase						:       Dict  			=     VersatileDiffusionPipeline.from_pretrained(lowercase_				,     torch_dtype=torch.floataa  )
       pipe.to(lowercase_  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       _lowerCAmelCase						:       Dict  			=     generator.manual_seed(0  )
       _lowerCAmelCase						:       Any  			=     pipe.dual_guided(
           prompt="""first prompt"""				,     image=lowercase_				,     text_to_image_strength=0.7_5				,     generator=lowercase_				,     guidance_scale=7.5				,     num_inference_steps=2				,     output_type="""numpy"""				,     ).images

       assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""				,     torch_dtype=torch.floataa  )
       pipe.to(lowercase_  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       _lowerCAmelCase						:       str  			=     """cyberpunk 2077"""
       _lowerCAmelCase						:       Optional[int]  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""  )
       _lowerCAmelCase						:       Any  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       int  			=     pipe.dual_guided(
           prompt=lowercase_				,     image=lowercase_				,     text_to_image_strength=0.7_5				,     generator=lowercase_				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="""numpy"""				,     ).images

       _lowerCAmelCase						:       List[str]  			=     image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Union[str, Any]  			=     np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]  )

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

       _lowerCAmelCase						:       Dict  			=     """A painting of a squirrel eating a burger """
       _lowerCAmelCase						:       int  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     pipe.text_to_image(
           prompt=lowercase_				,     generator=lowercase_				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="""numpy"""  ).images

       _lowerCAmelCase						:       Any  			=     image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       List[str]  			=     np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]  )

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

       _lowerCAmelCase						:       Dict  			=     pipe.image_variation(lowercase_				,     generator=lowercase_				,     output_type="""numpy"""  ).images

       _lowerCAmelCase						:       List[str]  			=     image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Tuple  			=     np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]  )

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











'''simple docstring'''

from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase							:	Optional[Any]			=[8, 5, 9, 7]
__lowerCAmelCase							:	Optional[int]			=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase							:	List[Any]			=[
    [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      :Optional[int]						,       lowercase_      :list[int]						,       lowercase_      :list[list[int]]						,       lowercase_      :list[list[int]]						,       )->							None:
     A__				=			claim_vector
     A__				=			allocated_resources_table
     A__				=			maximum_claim_table
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							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      UpperCAmelCase_							(				self      :Tuple							)->							list[int]:
     return np.array(self.__claim_vector							) - np.array(
         self.__processes_resource_summation()							)
 def      UpperCAmelCase_							(				self      :Dict							)->							list[list[int]]:
     return [
         list(np.array(self.__maximum_claim_table[i]							) - np.array(lowercase_							)							)
         for i, allocated_resource in enumerate(self.__allocated_resources_table							)
     ]
 def      UpperCAmelCase_							(				self      :Any							)->							dict[int, list[int]]:
     return {self.__need().index(lowercase_							): i for i in self.__need()}
 def      UpperCAmelCase_							(				self      :Tuple						,       **lowercase_      :List[str]							)->							None:
     A__				=			self.__need()
     A__				=			self.__allocated_resources_table
     A__				=			self.__available_resources()
     A__				=			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__				=			False
         for each_need in need_list:
             A__				=			True
             for index, need in enumerate(lowercase_							):
                 if need > available_resources[index]:
                     A__				=			False
                     break
             if execution:
                 A__				=			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__				=			original_need_index
                 print(F"Process {process_number + 1} is executing."							)
                 # remove the process run from stack
                 need_list.remove(lowercase_							)
                 # update available/freed resources stack
                 A__				=			np.array(lowercase_							) + np.array(
                     alloc_resources_table[process_number]							)
                 print(
                     "Updated available resource stack for processes: "
                     + " ".join([str(lowercase_							) 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      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     print(" " * 9 + "Allocated Resource Table"							)
     for item in self.__allocated_resources_table:
         print(
             F"P{self.__allocated_resources_table.index(lowercase_							) + 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(lowercase_							) + 1}"
             + " ".join(F"{it:>8}" for it in item							)
             + "\n"							)
     print(
         "Current Usage by Active Processes: "
         + " ".join(str(lowercase_							) for x in self.__claim_vector							)							)
     print(
         "Initial Available Resources:       "
         + " ".join(str(lowercase_							) for x in self.__available_resources()							)							)
     time.sleep(1							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



