
'''simple docstring'''
from __future__ import annotations

from collections import Counter
from random import random


class       _lowerCAmelCase   :







       '''simple docstring'''

       def __init__(self  )					->       Dict:
              _snake_case       =  {}

       def 	lowercase							(self  ,  UpperCAmelCase  )					->       None:
              _snake_case       =  {}

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       None:
              if nodea not in self.connections:
                     self.add_node(_lowerCamelCase  )
              if nodea not in self.connections:
                     self.add_node(_lowerCamelCase  )
              _snake_case       =  probability

       def 	lowercase							(self  )					->       list[str]:
              return list(self.connections  )





       def 	lowercase							(self  ,  UpperCAmelCase  )					->       str:
              _snake_case       =  0
              _snake_case       =  random()

              for dest in self.connections[node]:
                     current_probability += self.connections[node][dest]
                     if current_probability > random_value:
                            return dest
              return ""






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       _snake_case       =  MarkovChainGraphUndirectedUnweighted()

       for nodea, nodea, probability in transitions:
              graph.add_transition_probability(a__							, a__							, a__				)

       _snake_case       =  Counter(graph.get_nodes()				)
       _snake_case       =  start

       for _ in range(a__				):
              _snake_case       =  graph.transition(a__				)
              visited[node] += 1

       return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()

'''simple docstring'''
import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


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


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([canny_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """Chef in the kitchen"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([pose_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2