






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       _a   ( self							):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: Any   				=    FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-canny'  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
         UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'  ,      controlnet=_lowerCamelCase  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
         UpperCamelCase_: str   				=    controlnet_params

         UpperCamelCase_: int   				=    'bird'
         UpperCamelCase_: Union[str, Any]   				=    jax.device_count()
         UpperCamelCase_: Union[str, Any]   				=    pipe.prepare_text_inputs([prompts] * num_samples							)

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

         UpperCamelCase_: List[Any]   				=    jax.random.PRNGKey(0							)
         UpperCamelCase_: Optional[Any]   				=    jax.random.split(_lowerCamelCase  ,      jax.device_count()							)

         UpperCamelCase_: Any   				=    replicate(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    shard(_lowerCamelCase							)
         UpperCamelCase_: int   				=    shard(_lowerCamelCase							)

         UpperCamelCase_: str   				=    pipe(
             prompt_ids=_lowerCamelCase  ,      image=_lowerCamelCase  ,      params=_lowerCamelCase  ,      prng_seed=_lowerCamelCase  ,      num_inference_steps=5_0  ,      jit=_lowerCamelCase  ,      ).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         UpperCamelCase_: Tuple   				=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
         UpperCamelCase_: List[str]   				=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         UpperCamelCase_: Any   				=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
         UpperCamelCase_: List[str]   				=    jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]							)
         print(f'''output_slice: {output_slice}'''							)
         assert jnp.abs(output_slice - expected_slice							).max() < 1e-2





       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: List[str]   				=    FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-openpose'  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
         UpperCamelCase_				,UpperCamelCase_: List[str]   				=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'  ,      controlnet=_lowerCamelCase  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
         UpperCamelCase_: int   				=    controlnet_params

         UpperCamelCase_: Union[str, Any]   				=    'Chef in the kitchen'
         UpperCamelCase_: List[Any]   				=    jax.device_count()
         UpperCamelCase_: Tuple   				=    pipe.prepare_text_inputs([prompts] * num_samples							)

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

         UpperCamelCase_: Any   				=    jax.random.PRNGKey(0							)
         UpperCamelCase_: str   				=    jax.random.split(_lowerCamelCase  ,      jax.device_count()							)

         UpperCamelCase_: Optional[int]   				=    replicate(_lowerCamelCase							)
         UpperCamelCase_: int   				=    shard(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    shard(_lowerCamelCase							)

         UpperCamelCase_: int   				=    pipe(
             prompt_ids=_lowerCamelCase  ,      image=_lowerCamelCase  ,      params=_lowerCamelCase  ,      prng_seed=_lowerCamelCase  ,      num_inference_steps=5_0  ,      jit=_lowerCamelCase  ,      ).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         UpperCamelCase_: int   				=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
         UpperCamelCase_: Optional[int]   				=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         UpperCamelCase_: Dict   				=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
         UpperCamelCase_: int   				=    jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]							)
         print(f'''output_slice: {output_slice}'''							)
         assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.

def   snake_case (UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: int   				=    [False] * len(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    [-1] * len(UpperCAmelCase__  )

  def dfs(UpperCAmelCase__     ,   UpperCAmelCase__  ):
    UpperCamelCase_: Tuple   				=    True
    UpperCamelCase_: Optional[int]   				=    c
    for u in graph[v]:
      if not visited[u]:
        dfs(UpperCAmelCase__     ,   1 - c  )

  for i in range(len(UpperCAmelCase__  )  ):
    if not visited[i]:
      dfs(UpperCAmelCase__     ,   0  )

  for i in range(len(UpperCAmelCase__  )  ):
    for j in graph[i]:
      if color[i] == color[j]:
        return False

  return True


# Adjacency list of graph
A_		:		Dict		     =			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))