






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 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      super().tearDown()
      gc.collect()







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  FlaxControlNetModel.from_pretrained(
          "lllyasviel/sd-controlnet-canny"							,   from_pt=SCREAMING_SNAKE_CASE							,   dtype=jnp.bfloataa						)
      UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
          "runwayml/stable-diffusion-v1-5"							,   controlnet=SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE							,   dtype=jnp.bfloataa						)
      UpperCamelCase__			:   List[Any]       				=  controlnet_params

      UpperCamelCase__			:   Optional[int]       				=  "bird"
      UpperCamelCase__			:   Tuple       				=  jax.device_count()
      UpperCamelCase__			:   Optional[Any]       				=  pipe.prepare_text_inputs([prompts] * num_samples						)

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

      UpperCamelCase__			:   Any       				=  jax.random.PRNGKey(0						)
      UpperCamelCase__			:   int       				=  jax.random.split(SCREAMING_SNAKE_CASE							,   jax.device_count()						)

      UpperCamelCase__			:   str       				=  replicate(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  shard(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  shard(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  pipe(
          prompt_ids=SCREAMING_SNAKE_CASE							,   image=SCREAMING_SNAKE_CASE							,   params=SCREAMING_SNAKE_CASE							,   prng_seed=SCREAMING_SNAKE_CASE							,   num_inference_steps=50							,   jit=SCREAMING_SNAKE_CASE							,   ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      UpperCamelCase__			:   str       				=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
      UpperCamelCase__			:   Any       				=  images[0, 2_53:2_56, 2_53:2_56, -1]

      UpperCamelCase__			:   Optional[int]       				=  jnp.asarray(jax.device_get(image_slice.flatten()						)						)
      UpperCamelCase__			:   Dict       				=  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 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  FlaxControlNetModel.from_pretrained(
          "lllyasviel/sd-controlnet-openpose"							,   from_pt=SCREAMING_SNAKE_CASE							,   dtype=jnp.bfloataa						)
      UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
          "runwayml/stable-diffusion-v1-5"							,   controlnet=SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE							,   dtype=jnp.bfloataa						)
      UpperCamelCase__			:   Optional[Any]       				=  controlnet_params

      UpperCamelCase__			:   Tuple       				=  "Chef in the kitchen"
      UpperCamelCase__			:   int       				=  jax.device_count()
      UpperCamelCase__			:   Any       				=  pipe.prepare_text_inputs([prompts] * num_samples						)

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

      UpperCamelCase__			:   List[Any]       				=  jax.random.PRNGKey(0						)
      UpperCamelCase__			:   Any       				=  jax.random.split(SCREAMING_SNAKE_CASE							,   jax.device_count()						)

      UpperCamelCase__			:   Tuple       				=  replicate(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  shard(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  shard(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  pipe(
          prompt_ids=SCREAMING_SNAKE_CASE							,   image=SCREAMING_SNAKE_CASE							,   params=SCREAMING_SNAKE_CASE							,   prng_seed=SCREAMING_SNAKE_CASE							,   num_inference_steps=50							,   jit=SCREAMING_SNAKE_CASE							,   ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      UpperCamelCase__			:   Optional[Any]       				=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
      UpperCamelCase__			:   List[str]       				=  images[0, 2_53:2_56, 2_53:2_56, -1]

      UpperCamelCase__			:   int       				=  jnp.asarray(jax.device_get(image_slice.flatten()						)						)
      UpperCamelCase__			:   Dict       				=  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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)