
import math

import flax.linen as nn
import jax.numpy as jnp
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     jnp.ndarray			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     float = 1			,							UpperCAmelCase_	:     float = 1			,							UpperCAmelCase_	:     float = 1.0E4			,							UpperCAmelCase_	:     bool = False			,							UpperCAmelCase_	:     float = 1.0			,							)			->						Optional[int]:
       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, f'Embedding dimension {embedding_dim} should be even'
       SCREAMING_SNAKE_CASE_							:				Any		=float(embedding_dim // 2				)
       SCREAMING_SNAKE_CASE_							:				int		=math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=min_timescale * jnp.exp(jnp.arange(_snake_case			,							dtype=jnp.floataa				) * -log_timescale_increment				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=jnp.expand_dims(_snake_case			,							1				) * jnp.expand_dims(_snake_case			,							0				)

       # scale embeddings
       SCREAMING_SNAKE_CASE_							:				str		=scale * emb

       if flip_sin_to_cos:
              SCREAMING_SNAKE_CASE_							:				List[str]		=jnp.concatenate([jnp.cos(_snake_case				), jnp.sin(_snake_case				)]			,							axis=1				)
       else:
              SCREAMING_SNAKE_CASE_							:				List[str]		=jnp.concatenate([jnp.sin(_snake_case				), jnp.cos(_snake_case				)]			,							axis=1				)
       SCREAMING_SNAKE_CASE_							:				List[str]		=jnp.reshape(_snake_case			,							[jnp.shape(_snake_case				)[0], embedding_dim]				)
       return signal


class 			lowercase_		(      nn.Module       ):
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						jnp.floataa


       @nn.compact
       def __call__(    self						,       __A				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				int		=nn.Dense(self.time_embed_dim						,       dtype=self.dtype						,       name='''linear_1'''				)(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Dict		=nn.silu(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Dict		=nn.Dense(self.time_embed_dim						,       dtype=self.dtype						,       name='''linear_2'''				)(UpperCAmelCase_				)
              return temb






class 			lowercase_		(      nn.Module       ):
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						1


       @nn.compact
       def __call__(    self						,       __A				)						->    Tuple:
              return get_sinusoidal_embeddings(
                  UpperCAmelCase_						,       embedding_dim=self.dim						,       flip_sin_to_cos=self.flip_sin_to_cos						,       freq_shift=self.freq_shift				)





'''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 					snake_case			(unittest.TestCase ):



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



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

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

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    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)

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

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

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

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    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)

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

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    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




