"""simple docstring"""





import copy

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


_UpperCamelCase     :		Optional[int]											=					logging.get_logger(__name__)

_UpperCamelCase     :		Dict											=					{
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					str										=						"deformable_detr"
      lowerCamelCase__	:					List[Any]										=						{
          "hidden_size": "d_model",
          "num_attention_heads": "encoder_attention_heads",
      }


      def __init__(     self	,    a=True	,    a=None	,    a=3	,    a=3_0_0	,    a=1_0_2_4	,    a=6	,    a=1_0_2_4	,    a=8	,    a=6	,    a=1_0_2_4	,    a=8	,    a=0.0	,    a=True	,    a="relu"	,    a=2_5_6	,    a=0.1	,    a=0.0	,    a=0.0	,    a=0.02	,    a=1.0	,    a=True	,    a=False	,    a="sine"	,    a="resnet50"	,    a=True	,    a=False	,    a=4	,    a=4	,    a=4	,    a=False	,    a=3_0_0	,    a=False	,    a=1	,    a=5	,    a=2	,    a=1	,    a=1	,    a=5	,    a=2	,    a=0.1	,    a=0.25	,    a=False	,    **a	,    ) ->      Any:
             if backbone_config is not None and use_timm_backbone:
                    raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'							)

             if not use_timm_backbone:
                    if backbone_config is None:
                           logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'							)
                           lowercase__    :	List[Any]     					=			CONFIG_MAPPING['resnet'](out_features=['stage4']							)
                    elif isinstance(a	,    a							):
                           lowercase__    :	Union[str, Any]     					=			backbone_config.get('model_type'							)
                           lowercase__    :	List[Any]     					=			CONFIG_MAPPING[backbone_model_type]
                           lowercase__    :	Union[str, Any]     					=			config_class.from_dict(a							)
             lowercase__    :	List[Any]     					=			use_timm_backbone
             lowercase__    :	int     					=			backbone_config
             lowercase__    :	Optional[int]     					=			num_channels
             lowercase__    :	Optional[Any]     					=			num_queries
             lowercase__    :	List[Any]     					=			max_position_embeddings
             lowercase__    :	List[str]     					=			d_model
             lowercase__    :	List[str]     					=			encoder_ffn_dim
             lowercase__    :	List[str]     					=			encoder_layers
             lowercase__    :	List[Any]     					=			encoder_attention_heads
             lowercase__    :	str     					=			decoder_ffn_dim
             lowercase__    :	Optional[Any]     					=			decoder_layers
             lowercase__    :	List[Any]     					=			decoder_attention_heads
             lowercase__    :	Optional[Any]     					=			dropout
             lowercase__    :	int     					=			attention_dropout
             lowercase__    :	Union[str, Any]     					=			activation_dropout
             lowercase__    :	Tuple     					=			activation_function
             lowercase__    :	Optional[int]     					=			init_std
             lowercase__    :	List[str]     					=			init_xavier_std
             lowercase__    :	Tuple     					=			encoder_layerdrop
             lowercase__    :	List[str]     					=			auxiliary_loss
             lowercase__    :	Tuple     					=			position_embedding_type
             lowercase__    :	Tuple     					=			backbone
             lowercase__    :	List[Any]     					=			use_pretrained_backbone
             lowercase__    :	List[Any]     					=			dilation
             # deformable attributes
             lowercase__    :	Dict     					=			num_feature_levels
             lowercase__    :	Optional[Any]     					=			encoder_n_points
             lowercase__    :	Union[str, Any]     					=			decoder_n_points
             lowercase__    :	List[Any]     					=			two_stage
             lowercase__    :	str     					=			two_stage_num_proposals
             lowercase__    :	Optional[int]     					=			with_box_refine
             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
             lowercase__    :	int     					=			class_cost
             lowercase__    :	List[Any]     					=			bbox_cost
             lowercase__    :	Optional[Any]     					=			giou_cost
             # Loss coefficients
             lowercase__    :	List[Any]     					=			mask_loss_coefficient
             lowercase__    :	List[Any]     					=			dice_loss_coefficient
             lowercase__    :	Tuple     					=			bbox_loss_coefficient
             lowercase__    :	int     					=			giou_loss_coefficient
             lowercase__    :	Any     					=			eos_coefficient
             lowercase__    :	str     					=			focal_alpha
             lowercase__    :	Any     					=			disable_custom_kernels
             super().__init__(is_encoder_decoder=a	,    **a							)


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


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


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Dict     					=			copy.deepcopy(self.__dict__							)
             if self.backbone_config is not None:
                    lowercase__    :	Optional[Any]     					=			self.backbone_config.to_dict()
             lowercase__    :	Optional[Any]     					=			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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


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

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

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

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

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

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

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

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

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

