





'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__						:      Tuple	=    logging.get_logger(__name__)

lowercase__						:      Dict	=    {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   'conditional_detr'
       _snake_case     :					Tuple						    =   ['past_key_values']
       _snake_case     :					List[Any]						    =   {
           'hidden_size': 'd_model',
           'num_attention_heads': 'encoder_attention_heads',
       }





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Optional[int]=True	,			lowerCAmelCase__       :		List[str]=None	,			lowerCAmelCase__       :		List[str]=3	,			lowerCAmelCase__       :		Optional[Any]=300	,			lowerCAmelCase__       :		int=6	,			lowerCAmelCase__       :		Optional[Any]=2048	,			lowerCAmelCase__       :		int=8	,			lowerCAmelCase__       :		Union[str, Any]=6	,			lowerCAmelCase__       :		int=2048	,			lowerCAmelCase__       :		int=8	,			lowerCAmelCase__       :		Dict=0.0	,			lowerCAmelCase__       :		Optional[int]=0.0	,			lowerCAmelCase__       :		str=True	,			lowerCAmelCase__       :		str="relu"	,			lowerCAmelCase__       :		Any=256	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		str=0.0	,			lowerCAmelCase__       :		List[str]=0.0	,			lowerCAmelCase__       :		Tuple=0.02	,			lowerCAmelCase__       :		Any=1.0	,			lowerCAmelCase__       :		Dict=False	,			lowerCAmelCase__       :		Optional[Any]="sine"	,			lowerCAmelCase__       :		List[Any]="resnet50"	,			lowerCAmelCase__       :		Union[str, Any]=True	,			lowerCAmelCase__       :		Dict=False	,			lowerCAmelCase__       :		str=2	,			lowerCAmelCase__       :		List[str]=5	,			lowerCAmelCase__       :		Dict=2	,			lowerCAmelCase__       :		int=1	,			lowerCAmelCase__       :		Dict=1	,			lowerCAmelCase__       :		Any=2	,			lowerCAmelCase__       :		str=5	,			lowerCAmelCase__       :		Union[str, Any]=2	,			lowerCAmelCase__       :		int=0.25	,			**lowerCAmelCase__       :		Any	,			)   ->					int:

            '''simple docstring'''




            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       =	CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
                 elif isinstance(lowerCAmelCase__	,			lowerCAmelCase__					):
                      _UpperCamelCase       =	backbone_config.get('''model_type'''					)
                      _UpperCamelCase       =	CONFIG_MAPPING[backbone_model_type]
                      _UpperCamelCase       =	config_class.from_dict(lowerCAmelCase__					)

            _UpperCamelCase       =	use_timm_backbone
            _UpperCamelCase       =	backbone_config
            _UpperCamelCase       =	num_channels
            _UpperCamelCase       =	num_queries
            _UpperCamelCase       =	d_model
            _UpperCamelCase       =	encoder_ffn_dim
            _UpperCamelCase       =	encoder_layers
            _UpperCamelCase       =	encoder_attention_heads
            _UpperCamelCase       =	decoder_ffn_dim
            _UpperCamelCase       =	decoder_layers
            _UpperCamelCase       =	decoder_attention_heads
            _UpperCamelCase       =	dropout
            _UpperCamelCase       =	attention_dropout
            _UpperCamelCase       =	activation_dropout
            _UpperCamelCase       =	activation_function
            _UpperCamelCase       =	init_std
            _UpperCamelCase       =	init_xavier_std
            _UpperCamelCase       =	encoder_layerdrop
            _UpperCamelCase       =	decoder_layerdrop
            _UpperCamelCase       =	encoder_layers
            _UpperCamelCase       =	auxiliary_loss
            _UpperCamelCase       =	position_embedding_type
            _UpperCamelCase       =	backbone
            _UpperCamelCase       =	use_pretrained_backbone
            _UpperCamelCase       =	dilation
            # Hungarian matcher
            _UpperCamelCase       =	class_cost
            _UpperCamelCase       =	bbox_cost
            _UpperCamelCase       =	giou_cost
            # Loss coefficients
            _UpperCamelCase       =	mask_loss_coefficient
            _UpperCamelCase       =	dice_loss_coefficient
            _UpperCamelCase       =	cls_loss_coefficient
            _UpperCamelCase       =	bbox_loss_coefficient
            _UpperCamelCase       =	giou_loss_coefficient
            _UpperCamelCase       =	focal_alpha
            super().__init__(is_encoder_decoder=lowerCAmelCase__	,			**lowerCAmelCase__					)





       @property
       def snake_case__						(							self       :		Optional[Any]					)   ->					int:

            '''simple docstring'''




            return self.encoder_attention_heads





       @property
       def snake_case__						(							self       :		List[Any]					)   ->					int:

            '''simple docstring'''




            return self.d_model







       def snake_case__						(							self       :		Any					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	copy.deepcopy(self.__dict__					)
            if self.backbone_config is not None:
                 _UpperCamelCase       =	self.backbone_config.to_dict()
            _UpperCamelCase       =	self.__class__.model_type
            return output

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   version.parse('1.11'			)





       @property
       def snake_case__						(							self       :		List[str]					)   ->					Mapping[str, Mapping[int, str]]:

            '''simple docstring'''




            return OrderedDict(
                [
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                    ('''pixel_mask''', {0: '''batch'''}),
                ]					)





       @property
       def snake_case__						(							self       :		Optional[Any]					)   ->					float:

            '''simple docstring'''




            return 1e-5







       @property
       def snake_case__						(							self       :		Optional[int]					)   ->					int:

            '''simple docstring'''




            return 12










'''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     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            super().tearDown()
            gc.collect()





       def snake_case__						(							self       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-canny'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

            _UpperCamelCase       =	'''bird'''
            _UpperCamelCase       =	jax.device_count()
            _UpperCamelCase       =	pipe.prepare_text_inputs([prompts] * num_samples					)

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).images
            assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2







       def snake_case__						(							self       :		Union[str, Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-openpose'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

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

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).images
            assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2



