

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			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

     SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






"""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 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





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

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

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

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			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, 768, 512, 3)

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

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

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

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

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			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, 768, 512, 3)

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

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




