"""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   lowerCAmelCase_	(       self						:	Dict       ):
      with tempfile.TemporaryDirectory() as tmpdirname:
        # pipeline has Flax weights
        _UpperCAmelCase    			=   FlaxDiffusionPipeline.from_pretrained(
            """hf-internal-testing/tiny-stable-diffusion-pipe""" ,					safety_checker=__lowerCAmelCase ,					cache_dir=__lowerCAmelCase       )

        _UpperCAmelCase    			=   [t[-1] for t in os.walk(os.path.join(__lowerCAmelCase ,					os.listdir(__lowerCAmelCase       )[0] ,					"""snapshots"""       )       )]
        _UpperCAmelCase    			=   [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   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   FlaxStableDiffusionPipeline.from_pretrained(
          """hf-internal-testing/tiny-stable-diffusion-pipe""" ,					safety_checker=__lowerCAmelCase       )

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

      _UpperCAmelCase    			=   jax.random.PRNGKey(0       )
      _UpperCAmelCase    			=   4

      _UpperCAmelCase    			=   jax.device_count()
      _UpperCAmelCase    			=   num_samples * [prompt]
      _UpperCAmelCase    			=   pipeline.prepare_inputs(__lowerCAmelCase       )

      # shard inputs and rng
      _UpperCAmelCase    			=   replicate(__lowerCAmelCase       )
      _UpperCAmelCase    			=   jax.random.split(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   shard(__lowerCAmelCase       )

      _UpperCAmelCase    			=   pipeline(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					jit=__lowerCAmelCase       ).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_514_745       ) < 1e-3
        assert np.abs(np.abs(__lowerCAmelCase ,					dtype=np.floataa       ).sum() - 49_947.875       ) < 5e-1

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


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   FlaxStableDiffusionPipeline.from_pretrained(
          """CompVis/stable-diffusion-v1-4""" ,					revision="""flax""" ,					safety_checker=__lowerCAmelCase       )

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

      _UpperCAmelCase    			=   jax.random.PRNGKey(0       )
      _UpperCAmelCase    			=   50

      _UpperCAmelCase    			=   jax.device_count()
      _UpperCAmelCase    			=   num_samples * [prompt]
      _UpperCAmelCase    			=   pipeline.prepare_inputs(__lowerCAmelCase       )

      # shard inputs and rng
      _UpperCAmelCase    			=   replicate(__lowerCAmelCase       )
      _UpperCAmelCase    			=   jax.random.split(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   shard(__lowerCAmelCase       )

      _UpperCAmelCase    			=   pipeline(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					jit=__lowerCAmelCase       ).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() - 0.05_652_401)       ) < 1e-3
        assert np.abs((np.abs(__lowerCAmelCase ,					dtype=np.floataa       ).sum() - 2_383_808.2)       ) < 5e-1


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

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

      _UpperCAmelCase    			=   jax.random.PRNGKey(0       )
      _UpperCAmelCase    			=   50

      _UpperCAmelCase    			=   jax.device_count()
      _UpperCAmelCase    			=   num_samples * [prompt]
      _UpperCAmelCase    			=   pipeline.prepare_inputs(__lowerCAmelCase       )

      # shard inputs and rng
      _UpperCAmelCase    			=   replicate(__lowerCAmelCase       )
      _UpperCAmelCase    			=   jax.random.split(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   shard(__lowerCAmelCase       )

      _UpperCAmelCase    			=   pipeline(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					jit=__lowerCAmelCase       ).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() - 0.04_003_906)       ) < 1e-3
        assert np.abs((np.abs(__lowerCAmelCase ,					dtype=np.floataa       ).sum() - 2_373_516.75)       ) < 5e-1


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   FlaxStableDiffusionPipeline.from_pretrained(
          """CompVis/stable-diffusion-v1-4""" ,					revision="""bf16""" ,					dtype=jnp.bfloataa       )

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

      _UpperCAmelCase    			=   jax.random.PRNGKey(0       )
      _UpperCAmelCase    			=   50

      _UpperCAmelCase    			=   jax.device_count()
      _UpperCAmelCase    			=   num_samples * [prompt]
      _UpperCAmelCase    			=   pipeline.prepare_inputs(__lowerCAmelCase       )

      # shard inputs and rng
      _UpperCAmelCase    			=   replicate(__lowerCAmelCase       )
      _UpperCAmelCase    			=   jax.random.split(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   shard(__lowerCAmelCase       )

      _UpperCAmelCase    			=   pipeline(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					jit=__lowerCAmelCase       ).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() - 0.04_003_906)       ) < 1e-3
        assert np.abs((np.abs(__lowerCAmelCase ,					dtype=np.floataa       ).sum() - 2_373_516.75)       ) < 5e-1


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   FlaxDDIMScheduler(
          beta_start=0.00_085 ,					beta_end=0.012 ,					beta_schedule="""scaled_linear""" ,					set_alpha_to_one=__lowerCAmelCase ,					steps_offset=1 ,					)

      _UpperCAmelCase			,					_UpperCAmelCase    			=   FlaxStableDiffusionPipeline.from_pretrained(
          """CompVis/stable-diffusion-v1-4""" ,					revision="""bf16""" ,					dtype=jnp.bfloataa ,					scheduler=__lowerCAmelCase ,					safety_checker=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   scheduler.create_state()

      _UpperCAmelCase    			=   scheduler_state

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

      _UpperCAmelCase    			=   jax.random.PRNGKey(0       )
      _UpperCAmelCase    			=   50

      _UpperCAmelCase    			=   jax.device_count()
      _UpperCAmelCase    			=   num_samples * [prompt]
      _UpperCAmelCase    			=   pipeline.prepare_inputs(__lowerCAmelCase       )

      # shard inputs and rng
      _UpperCAmelCase    			=   replicate(__lowerCAmelCase       )
      _UpperCAmelCase    			=   jax.random.split(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   shard(__lowerCAmelCase       )

      _UpperCAmelCase    			=   pipeline(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					jit=__lowerCAmelCase       ).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() - 0.045_043_945)       ) < 1e-3
        assert np.abs((np.abs(__lowerCAmelCase ,					dtype=np.floataa       ).sum() - 2_347_693.5)       ) < 5e-1


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

      _UpperCAmelCase    			=   jax.device_count()
      _UpperCAmelCase    			=   num_samples * [prompt]
      _UpperCAmelCase    			=   jax.random.split(jax.random.PRNGKey(0       ) ,					__lowerCAmelCase       )

      _UpperCAmelCase			,					_UpperCAmelCase    			=   FlaxStableDiffusionPipeline.from_pretrained(
          """CompVis/stable-diffusion-v1-4""" ,					revision="""bf16""" ,					dtype=jnp.bfloataa ,					safety_checker=__lowerCAmelCase ,					)

      _UpperCAmelCase    			=   replicate(__lowerCAmelCase       )
      _UpperCAmelCase    			=   pipeline.prepare_inputs(__lowerCAmelCase       )
      _UpperCAmelCase    			=   shard(__lowerCAmelCase       )
      _UpperCAmelCase    			=   pipeline(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					jit=__lowerCAmelCase       ).images
      assert images.shape == (num_samples, 1, 512, 512, 3)
      _UpperCAmelCase    			=   images[2, 0, 256, 10:17, 1]

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

      _UpperCAmelCase    			=   replicate(__lowerCAmelCase       )
      _UpperCAmelCase    			=   pipeline.prepare_inputs(__lowerCAmelCase       )
      _UpperCAmelCase    			=   shard(__lowerCAmelCase       )
      _UpperCAmelCase    			=   pipeline(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					jit=__lowerCAmelCase       ).images
      assert images_eff.shape == (num_samples, 1, 512, 512, 3)
      _UpperCAmelCase    			=   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"""


import heapq






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   []

  # for each node and his adjacency list add them and the rank of the node to queue
  # using heapq module the queue will be filled like a Priority Queue
  # heapq works with a min priority queue, so I used -1*len(v) to build it
  for key, value in graph.items():
    # O(log(n))
    heapq.heappush(lowercase ,[-1 * len(lowercase		), (key, value)]		)

  # chosen_vertices = set of chosen vertices
  _UpperCAmelCase    			=   set()

  # while queue isn't empty and there are still edges
  #   (queue[0][0] is the rank of the node with max rank)
  while queue and queue[0][0] != 0:
    # extract vertex with max rank from queue and add it to chosen_vertices
    _UpperCAmelCase    			=   heapq.heappop(lowercase		)[1][0]
    chosen_vertices.add(lowercase		)

    # Remove all arcs adjacent to argmax
    for elem in queue:
      # if v haven't adjacent node, skip
      if elem[0] == 0:
        continue
      # if argmax is reachable from elem
      # remove argmax from elem's adjacent list and update his rank
      if argmax in elem[1][1]:
        _UpperCAmelCase    			=   elem[1][1].index(lowercase		)
        del elem[1][1][index]
        elem[0] += 1
        # re-order the queue
    heapq.heapify(lowercase		)
  return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCAmelCase__			=    {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(F'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')
