
"""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 ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()

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

			__lowerCAmelCase			:						Dict									=      'bird'
			__lowerCAmelCase			:						str									=      jax.device_count()
			__lowerCAmelCase			:						Dict									=      pipe.prepare_text_inputs([prompts] * num_samples )

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

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

			__lowerCAmelCase			:						Tuple									=      replicate(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[int]									=      shard(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      shard(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Union[str, Any]									=      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, 7_68, 5_12, 3)

			__lowerCAmelCase			:						Tuple									=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
			__lowerCAmelCase			:						Any									=      images[0, 2_53:2_56, 2_53:2_56, -1]

			__lowerCAmelCase			:						int									=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
			__lowerCAmelCase			:						Dict									=      jnp.array(
			    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078] )
			print(f"output_slice: {output_slice}" )
			assert jnp.abs(output_slice - expected_slice ).max() < 1E-2







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						str									=      FlaxControlNetModel.from_pretrained(
			    'lllyasviel/sd-controlnet-openpose'       ,		from_pt=_SCREAMING_SNAKE_CASE       ,		dtype=jnp.bfloataa )
			__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5'       ,		controlnet=_SCREAMING_SNAKE_CASE       ,		from_pt=_SCREAMING_SNAKE_CASE       ,		dtype=jnp.bfloataa )
			__lowerCAmelCase			:						List[Any]									=      controlnet_params

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

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

			__lowerCAmelCase			:						List[Any]									=      jax.random.PRNGKey(0 )
			__lowerCAmelCase			:						Optional[Any]									=      jax.random.split(_SCREAMING_SNAKE_CASE       ,		jax.device_count() )

			__lowerCAmelCase			:						Optional[Any]									=      replicate(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      shard(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      shard(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      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, 7_68, 5_12, 3)

			__lowerCAmelCase			:						Dict									=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
			__lowerCAmelCase			:						Any									=      images[0, 2_53:2_56, 2_53:2_56, -1]

			__lowerCAmelCase			:						Optional[Any]									=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
			__lowerCAmelCase			:						Tuple									=      jnp.array(
			    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]] )
			print(f"output_slice: {output_slice}" )
			assert jnp.abs(output_slice - expected_slice ).max() < 1E-2







"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''detr'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self					: Dict	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Union[str, Any]=None	,	lowerCAmelCase_					: Dict=3	,	lowerCAmelCase_					: List[Any]=100	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: int=8	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: Any=8	,	lowerCAmelCase_					: Dict=0.0	,	lowerCAmelCase_					: Union[str, Any]=0.0	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: List[Any]="relu"	,	lowerCAmelCase_					: List[Any]=256	,	lowerCAmelCase_					: Tuple=0.1	,	lowerCAmelCase_					: Optional[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0_2	,	lowerCAmelCase_					: Optional[int]=1.0	,	lowerCAmelCase_					: str=False	,	lowerCAmelCase_					: Tuple="sine"	,	lowerCAmelCase_					: str="resnet50"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: int=False	,	lowerCAmelCase_					: Optional[Any]=1	,	lowerCAmelCase_					: int=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: Dict=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: Union[str, Any]=0.1	,	**lowerCAmelCase_					: Dict	,	)						->			int:
				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."						)
										UpperCAmelCase_     : Tuple			     =					CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
							elif isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Dict			     =					backbone_config.get("model_type"						)
										UpperCAmelCase_     : Dict			     =					CONFIG_MAPPING[backbone_model_type]
										UpperCAmelCase_     : Tuple			     =					config_class.from_dict(lowerCAmelCase_						)
							# set timm attributes to None
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					None, None, None

				UpperCAmelCase_     : str			     =					use_timm_backbone
				UpperCAmelCase_     : Optional[Any]			     =					backbone_config
				UpperCAmelCase_     : Tuple			     =					num_channels
				UpperCAmelCase_     : Dict			     =					num_queries
				UpperCAmelCase_     : str			     =					d_model
				UpperCAmelCase_     : Any			     =					encoder_ffn_dim
				UpperCAmelCase_     : Union[str, Any]			     =					encoder_layers
				UpperCAmelCase_     : Optional[int]			     =					encoder_attention_heads
				UpperCAmelCase_     : List[str]			     =					decoder_ffn_dim
				UpperCAmelCase_     : Tuple			     =					decoder_layers
				UpperCAmelCase_     : Optional[int]			     =					decoder_attention_heads
				UpperCAmelCase_     : List[Any]			     =					dropout
				UpperCAmelCase_     : Union[str, Any]			     =					attention_dropout
				UpperCAmelCase_     : int			     =					activation_dropout
				UpperCAmelCase_     : List[str]			     =					activation_function
				UpperCAmelCase_     : Optional[int]			     =					init_std
				UpperCAmelCase_     : Union[str, Any]			     =					init_xavier_std
				UpperCAmelCase_     : List[str]			     =					encoder_layerdrop
				UpperCAmelCase_     : Tuple			     =					decoder_layerdrop
				UpperCAmelCase_     : str			     =					encoder_layers
				UpperCAmelCase_     : Any			     =					auxiliary_loss
				UpperCAmelCase_     : Optional[int]			     =					position_embedding_type
				UpperCAmelCase_     : List[str]			     =					backbone
				UpperCAmelCase_     : int			     =					use_pretrained_backbone
				UpperCAmelCase_     : Any			     =					dilation
				# Hungarian matcher
				UpperCAmelCase_     : str			     =					class_cost
				UpperCAmelCase_     : Any			     =					bbox_cost
				UpperCAmelCase_     : int			     =					giou_cost
				# Loss coefficients
				UpperCAmelCase_     : List[str]			     =					mask_loss_coefficient
				UpperCAmelCase_     : Dict			     =					dice_loss_coefficient
				UpperCAmelCase_     : Any			     =					bbox_loss_coefficient
				UpperCAmelCase_     : Union[str, Any]			     =					giou_loss_coefficient
				UpperCAmelCase_     : int			     =					eos_coefficient
				super().__init__(is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			int:
				return self.encoder_attention_heads





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





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: Union[str, Any]	,	lowerCAmelCase_					: PretrainedConfig	,	**lowerCAmelCase_					: Tuple						)						->			List[Any]:
				return cls(backbone_config=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Dict[str, any]:
				UpperCAmelCase_     : Tuple			     =					copy.deepcopy(self.__dict__						)
				if output["backbone_config"] is not None:
							UpperCAmelCase_     : Union[str, Any]			     =					self.backbone_config.to_dict()
				UpperCAmelCase_     : Any			     =					self.__class__.model_type
				return output







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				version.parse('''1.11'''     )





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				        ("pixel_mask", {0: "batch"}),
				    ]						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			float:
				return 1e-5





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				return 12



