

'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

_UpperCamelCase						:  List[str]							       =				{
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}



class 				_snake_case					(   a_     , a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     '''dinat'''

  SCREAMING_SNAKE_CASE			:       Optional[int]				=     {
      '''num_attention_heads''': '''num_heads''',
      '''num_hidden_layers''': '''num_layers''',
  }

  def __init__(				self      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=3      , _SCREAMING_SNAKE_CASE=64      , _SCREAMING_SNAKE_CASE=[3, 4, 6, 5]      , _SCREAMING_SNAKE_CASE=[2, 4, 8, 16]      , _SCREAMING_SNAKE_CASE=7      , _SCREAMING_SNAKE_CASE=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]      , _SCREAMING_SNAKE_CASE=3.0      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=0.0      , _SCREAMING_SNAKE_CASE=0.0      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-5      , _SCREAMING_SNAKE_CASE=0.0      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=None      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							patch_size
      lowerCAmelCase 						=							num_channels
      lowerCAmelCase 						=							embed_dim
      lowerCAmelCase 						=							depths
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							num_heads
      lowerCAmelCase 						=							kernel_size
      lowerCAmelCase 						=							dilations
      lowerCAmelCase 						=							mlp_ratio
      lowerCAmelCase 						=							qkv_bias
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							drop_path_rate
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							layer_norm_eps
      lowerCAmelCase 						=							initializer_range
      # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      lowerCAmelCase 						=							int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE	) - 1)	)
      lowerCAmelCase 						=							layer_scale_init_value
      lowerCAmelCase 						=							['stem'] + [F'stage{idx}' for idx in range(1      , len(_SCREAMING_SNAKE_CASE	) + 1	)]
      lowerCAmelCase						,   lowerCAmelCase 						=							get_aligned_output_features_output_indices(
          out_features=_SCREAMING_SNAKE_CASE      , out_indices=_SCREAMING_SNAKE_CASE      , stage_names=self.stage_names	)








'''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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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, 7_68, 5_12, 3)

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

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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, 7_68, 5_12, 3)

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

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





