"""simple docstring"""




import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard

     from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       with tempfile.TemporaryDirectory() as tmpdirname:
         # pipeline has Flax weights
         _lowerCAmelCase						:       str  			=     FlaxDiffusionPipeline.from_pretrained(
             """hf-internal-testing/tiny-stable-diffusion-pipe"""				,     safety_checker=lowercase_				,     cache_dir=lowercase_  )

         _lowerCAmelCase						:       str  			=     [t[-1] for t in os.walk(os.path.join(lowercase_				,     os.listdir(lowercase_  )[0]				,     """snapshots"""  )  )]
         _lowerCAmelCase						:       Any  			=     [item for sublist in all_root_files for item in sublist]

         # None of the downloaded files should be a PyTorch file even if we have some here:
         # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
         assert not any(f.endswith(""".bin"""  ) for f in files  )







@slow
@require_flax
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     FlaxStableDiffusionPipeline.from_pretrained(
           """hf-internal-testing/tiny-stable-diffusion-pipe"""				,     safety_checker=lowercase_  )

       _lowerCAmelCase						:       Tuple  			=     (
           """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
           """ field, close up, split lighting, cinematic"""
       )

       _lowerCAmelCase						:       List[Any]  			=     jax.random.PRNGKey(0  )
       _lowerCAmelCase						:       List[Any]  			=     4

       _lowerCAmelCase						:       Union[str, Any]  			=     jax.device_count()
       _lowerCAmelCase						:       List[Any]  			=     num_samples * [prompt]
       _lowerCAmelCase						:       Dict  			=     pipeline.prepare_inputs(lowercase_  )

       # shard inputs and rng
       _lowerCAmelCase						:       int  			=     replicate(lowercase_  )
       _lowerCAmelCase						:       Tuple  			=     jax.random.split(lowercase_				,     lowercase_  )
       _lowerCAmelCase						:       List[Any]  			=     shard(lowercase_  )

       _lowerCAmelCase						:       str  			=     pipeline(lowercase_				,     lowercase_				,     lowercase_				,     lowercase_				,     jit=lowercase_  ).images

       assert images.shape == (num_samples, 1, 64, 64, 3)
       if jax.device_count() == 8:
         assert np.abs(np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa  ).sum() - 4.1_5_1_4_7_4_5  ) < 1e-3
         assert np.abs(np.abs(lowercase_				,     dtype=np.floataa  ).sum() - 49947.875  ) < 5e-1

       _lowerCAmelCase						:       Dict  			=     pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]  )  )  )
       assert len(lowercase_  ) == num_samples






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,     revision="""flax"""				,     safety_checker=lowercase_  )

       _lowerCAmelCase						:       Any  			=     (
           """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
           """ field, close up, split lighting, cinematic"""
       )

       _lowerCAmelCase						:       Union[str, Any]  			=     jax.random.PRNGKey(0  )
       _lowerCAmelCase						:       Tuple  			=     50

       _lowerCAmelCase						:       Optional[int]  			=     jax.device_count()
       _lowerCAmelCase						:       Optional[int]  			=     num_samples * [prompt]
       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline.prepare_inputs(lowercase_  )

       # shard inputs and rng
       _lowerCAmelCase						:       str  			=     replicate(lowercase_  )
       _lowerCAmelCase						:       Optional[int]  			=     jax.random.split(lowercase_				,     lowercase_  )
       _lowerCAmelCase						:       Dict  			=     shard(lowercase_  )

       _lowerCAmelCase						:       List[Any]  			=     pipeline(lowercase_				,     lowercase_				,     lowercase_				,     lowercase_				,     jit=lowercase_  ).images

       assert images.shape == (num_samples, 1, 512, 512, 3)
       if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa  ).sum() - 0.0_5_6_5_2_4_0_1)  ) < 1e-3
         assert np.abs((np.abs(lowercase_				,     dtype=np.floataa  ).sum() - 2383808.2)  ) < 5e-1






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,     revision="""bf16"""				,     dtype=jnp.bfloataa				,     safety_checker=lowercase_  )

       _lowerCAmelCase						:       List[Any]  			=     (
           """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
           """ field, close up, split lighting, cinematic"""
       )

       _lowerCAmelCase						:       Any  			=     jax.random.PRNGKey(0  )
       _lowerCAmelCase						:       Optional[Any]  			=     50

       _lowerCAmelCase						:       Union[str, Any]  			=     jax.device_count()
       _lowerCAmelCase						:       Union[str, Any]  			=     num_samples * [prompt]
       _lowerCAmelCase						:       Optional[int]  			=     pipeline.prepare_inputs(lowercase_  )

       # shard inputs and rng
       _lowerCAmelCase						:       Tuple  			=     replicate(lowercase_  )
       _lowerCAmelCase						:       Tuple  			=     jax.random.split(lowercase_				,     lowercase_  )
       _lowerCAmelCase						:       Optional[Any]  			=     shard(lowercase_  )

       _lowerCAmelCase						:       int  			=     pipeline(lowercase_				,     lowercase_				,     lowercase_				,     lowercase_				,     jit=lowercase_  ).images

       assert images.shape == (num_samples, 1, 512, 512, 3)
       if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa  ).sum() - 0.0_4_0_0_3_9_0_6)  ) < 1e-3
         assert np.abs((np.abs(lowercase_				,     dtype=np.floataa  ).sum() - 2373516.75)  ) < 5e-1






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,     revision="""bf16"""				,     dtype=jnp.bfloataa  )

       _lowerCAmelCase						:       Optional[int]  			=     (
           """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
           """ field, close up, split lighting, cinematic"""
       )

       _lowerCAmelCase						:       Tuple  			=     jax.random.PRNGKey(0  )
       _lowerCAmelCase						:       Optional[int]  			=     50

       _lowerCAmelCase						:       str  			=     jax.device_count()
       _lowerCAmelCase						:       str  			=     num_samples * [prompt]
       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline.prepare_inputs(lowercase_  )

       # shard inputs and rng
       _lowerCAmelCase						:       Optional[int]  			=     replicate(lowercase_  )
       _lowerCAmelCase						:       Any  			=     jax.random.split(lowercase_				,     lowercase_  )
       _lowerCAmelCase						:       Dict  			=     shard(lowercase_  )

       _lowerCAmelCase						:       List[str]  			=     pipeline(lowercase_				,     lowercase_				,     lowercase_				,     lowercase_				,     jit=lowercase_  ).images

       assert images.shape == (num_samples, 1, 512, 512, 3)
       if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa  ).sum() - 0.0_4_0_0_3_9_0_6)  ) < 1e-3
         assert np.abs((np.abs(lowercase_				,     dtype=np.floataa  ).sum() - 2373516.75)  ) < 5e-1






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     FlaxDDIMScheduler(
           beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="""scaled_linear"""				,     set_alpha_to_one=lowercase_				,     steps_offset=1				,     )

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,     revision="""bf16"""				,     dtype=jnp.bfloataa				,     scheduler=lowercase_				,     safety_checker=lowercase_				,     )
       _lowerCAmelCase						:       int  			=     scheduler.create_state()

       _lowerCAmelCase						:       Any  			=     scheduler_state

       _lowerCAmelCase						:       Dict  			=     (
           """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
           """ field, close up, split lighting, cinematic"""
       )

       _lowerCAmelCase						:       List[str]  			=     jax.random.PRNGKey(0  )
       _lowerCAmelCase						:       List[str]  			=     50

       _lowerCAmelCase						:       str  			=     jax.device_count()
       _lowerCAmelCase						:       List[str]  			=     num_samples * [prompt]
       _lowerCAmelCase						:       Tuple  			=     pipeline.prepare_inputs(lowercase_  )

       # shard inputs and rng
       _lowerCAmelCase						:       Dict  			=     replicate(lowercase_  )
       _lowerCAmelCase						:       Optional[Any]  			=     jax.random.split(lowercase_				,     lowercase_  )
       _lowerCAmelCase						:       str  			=     shard(lowercase_  )

       _lowerCAmelCase						:       Dict  			=     pipeline(lowercase_				,     lowercase_				,     lowercase_				,     lowercase_				,     jit=lowercase_  ).images

       assert images.shape == (num_samples, 1, 512, 512, 3)
       if jax.device_count() == 8:
         assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa  ).sum() - 0.0_4_5_0_4_3_9_4_5)  ) < 1e-3
         assert np.abs((np.abs(lowercase_				,     dtype=np.floataa  ).sum() - 2347693.5)  ) < 5e-1






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     (
           """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
           """ field, close up, split lighting, cinematic"""
       )

       _lowerCAmelCase						:       str  			=     jax.device_count()
       _lowerCAmelCase						:       Any  			=     num_samples * [prompt]
       _lowerCAmelCase						:       Union[str, Any]  			=     jax.random.split(jax.random.PRNGKey(0  )				,     lowercase_  )

       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,     revision="""bf16"""				,     dtype=jnp.bfloataa				,     safety_checker=lowercase_				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     replicate(lowercase_  )
       _lowerCAmelCase						:       Any  			=     pipeline.prepare_inputs(lowercase_  )
       _lowerCAmelCase						:       Any  			=     shard(lowercase_  )
       _lowerCAmelCase						:       Optional[Any]  			=     pipeline(lowercase_				,     lowercase_				,     lowercase_				,     jit=lowercase_  ).images
       assert images.shape == (num_samples, 1, 512, 512, 3)
       _lowerCAmelCase						:       Dict  			=     images[2, 0, 256, 10:17, 1]

       # With memory efficient attention
       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     FlaxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""				,     revision="""bf16"""				,     dtype=jnp.bfloataa				,     safety_checker=lowercase_				,     use_memory_efficient_attention=lowercase_				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     replicate(lowercase_  )
       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline.prepare_inputs(lowercase_  )
       _lowerCAmelCase						:       Tuple  			=     shard(lowercase_  )
       _lowerCAmelCase						:       Optional[int]  			=     pipeline(lowercase_				,     lowercase_				,     lowercase_				,     jit=lowercase_  ).images
       assert images_eff.shape == (num_samples, 1, 512, 512, 3)
       _lowerCAmelCase						:       int  			=     images[2, 0, 256, 10:17, 1]

       # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
       # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
       assert abs(slice_eff - slice  ).max() < 1e-2











'''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()



