




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 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()

      def      UpperCAmelCase				(				self     )		->    Tuple:
         snake_case     , snake_case				:    List[Any]           =	FlaxControlNetModel.from_pretrained(
             """lllyasviel/sd-controlnet-canny"""    ,				from_pt=UpperCAmelCase__    ,				dtype=jnp.bfloataa     )
         snake_case     , snake_case				:    Optional[Any]           =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
             """runwayml/stable-diffusion-v1-5"""    ,				controlnet=UpperCAmelCase__    ,				from_pt=UpperCAmelCase__    ,				dtype=jnp.bfloataa     )
         snake_case				:    Dict           =	controlnet_params

         snake_case				:    Any           =	"""bird"""
         snake_case				:    str           =	jax.device_count()
         snake_case				:    Any           =	pipe.prepare_text_inputs([prompts] * num_samples     )

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

         snake_case				:    Optional[Any]           =	jax.random.PRNGKey(0     )
         snake_case				:    int           =	jax.random.split(UpperCAmelCase__    ,				jax.device_count()     )

         snake_case				:    List[str]           =	replicate(UpperCAmelCase__     )
         snake_case				:    Union[str, Any]           =	shard(UpperCAmelCase__     )
         snake_case				:    Any           =	shard(UpperCAmelCase__     )

         snake_case				:    Optional[Any]           =	pipe(
             prompt_ids=UpperCAmelCase__    ,				image=UpperCAmelCase__    ,				params=UpperCAmelCase__    ,				prng_seed=UpperCAmelCase__    ,				num_inference_steps=5_0    ,				jit=UpperCAmelCase__    ,				).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

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

         snake_case				:    Any           =	jnp.asarray(jax.device_get(image_slice.flatten()     )     )
         snake_case				:    Dict           =	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      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case     , snake_case				:    Optional[Any]           =	FlaxControlNetModel.from_pretrained(
             """lllyasviel/sd-controlnet-openpose"""    ,				from_pt=UpperCAmelCase__    ,				dtype=jnp.bfloataa     )
         snake_case     , snake_case				:    int           =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
             """runwayml/stable-diffusion-v1-5"""    ,				controlnet=UpperCAmelCase__    ,				from_pt=UpperCAmelCase__    ,				dtype=jnp.bfloataa     )
         snake_case				:    Union[str, Any]           =	controlnet_params

         snake_case				:    Union[str, Any]           =	"""Chef in the kitchen"""
         snake_case				:    str           =	jax.device_count()
         snake_case				:    Optional[Any]           =	pipe.prepare_text_inputs([prompts] * num_samples     )

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

         snake_case				:    Dict           =	jax.random.PRNGKey(0     )
         snake_case				:    List[str]           =	jax.random.split(UpperCAmelCase__    ,				jax.device_count()     )

         snake_case				:    str           =	replicate(UpperCAmelCase__     )
         snake_case				:    Tuple           =	shard(UpperCAmelCase__     )
         snake_case				:    Dict           =	shard(UpperCAmelCase__     )

         snake_case				:    List[str]           =	pipe(
             prompt_ids=UpperCAmelCase__    ,				image=UpperCAmelCase__    ,				params=UpperCAmelCase__    ,				prng_seed=UpperCAmelCase__    ,				num_inference_steps=5_0    ,				jit=UpperCAmelCase__    ,				).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

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

         snake_case				:    int           =	jnp.asarray(jax.device_get(image_slice.flatten()     )     )
         snake_case				:    Optional[Any]           =	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











from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





