

"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			int	  =			{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}


class 		_A       (			__a					):
      __a								=	'deta'
      __a								=	{
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=900    ,						SCREAMING_SNAKE_CASE__=2048    ,						SCREAMING_SNAKE_CASE__=6    ,						SCREAMING_SNAKE_CASE__=2048    ,						SCREAMING_SNAKE_CASE__=8    ,						SCREAMING_SNAKE_CASE__=6    ,						SCREAMING_SNAKE_CASE__=1024    ,						SCREAMING_SNAKE_CASE__=8    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__="relu"    ,						SCREAMING_SNAKE_CASE__=256    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=1.0    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__="sine"    ,						SCREAMING_SNAKE_CASE__=5    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=300    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=1    ,						SCREAMING_SNAKE_CASE__=5    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=1    ,						SCREAMING_SNAKE_CASE__=1    ,						SCREAMING_SNAKE_CASE__=5    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.25    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Tuple:
         if backbone_config is None:
            logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
            lowerCamelCase__						=			CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]		)
         else:
            if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
               lowerCamelCase__						=			backbone_config.pop("model_type"		)
               lowerCamelCase__						=			CONFIG_MAPPING[backbone_model_type]
               lowerCamelCase__						=			config_class.from_dict(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			backbone_config
         lowerCamelCase__						=			num_queries
         lowerCamelCase__						=			max_position_embeddings
         lowerCamelCase__						=			d_model
         lowerCamelCase__						=			encoder_ffn_dim
         lowerCamelCase__						=			encoder_layers
         lowerCamelCase__						=			encoder_attention_heads
         lowerCamelCase__						=			decoder_ffn_dim
         lowerCamelCase__						=			decoder_layers
         lowerCamelCase__						=			decoder_attention_heads
         lowerCamelCase__						=			dropout
         lowerCamelCase__						=			attention_dropout
         lowerCamelCase__						=			activation_dropout
         lowerCamelCase__						=			activation_function
         lowerCamelCase__						=			init_std
         lowerCamelCase__						=			init_xavier_std
         lowerCamelCase__						=			encoder_layerdrop
         lowerCamelCase__						=			auxiliary_loss
         lowerCamelCase__						=			position_embedding_type
         # deformable attributes
         lowerCamelCase__						=			num_feature_levels
         lowerCamelCase__						=			encoder_n_points
         lowerCamelCase__						=			decoder_n_points
         lowerCamelCase__						=			two_stage
         lowerCamelCase__						=			two_stage_num_proposals
         lowerCamelCase__						=			with_box_refine
         lowerCamelCase__						=			assign_first_stage
         if two_stage is True and with_box_refine is False:
            raise ValueError("If two_stage is True, with_box_refine must be True."		)
         # Hungarian matcher
         lowerCamelCase__						=			class_cost
         lowerCamelCase__						=			bbox_cost
         lowerCamelCase__						=			giou_cost
         # Loss coefficients
         lowerCamelCase__						=			mask_loss_coefficient
         lowerCamelCase__						=			dice_loss_coefficient
         lowerCamelCase__						=			bbox_loss_coefficient
         lowerCamelCase__						=			giou_loss_coefficient
         lowerCamelCase__						=			eos_coefficient
         lowerCamelCase__						=			focal_alpha
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					int:
         return self.encoder_attention_heads





      @property
      def 					_lowerCamelCase  (  self		)   ->					int:
         return self.d_model





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			copy.deepcopy(self.__dict__		)
         lowerCamelCase__						=			self.backbone_config.to_dict()
         lowerCamelCase__						=			self.__class__.model_type
         return output






"""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




