





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase			       =      logging.get_logger(__name__)

_lowerCamelCase			       =      {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      """pegasus"""
      UpperCAmelCase__						=      ["""past_key_values"""]
      UpperCAmelCase__						=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


      def __init__(		self      ,							a__=5_0265      ,							a__=1024      ,							a__=12      ,							a__=4096      ,							a__=16      ,							a__=12      ,							a__=4096      ,							a__=16      ,							a__=0.0      ,							a__=0.0      ,							a__=True      ,							a__=True      ,							a__="gelu"      ,							a__=1024      ,							a__=0.1      ,							a__=0.0      ,							a__=0.0      ,							a__=0.02      ,							a__=0      ,							a__=False      ,							a__=0      ,							a__=1      ,							a__=1      ,							**a__      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		vocab_size
             _lowerCamelCase							:     Dict	=		max_position_embeddings
             _lowerCamelCase							:     Union[str, Any]	=		d_model
             _lowerCamelCase							:     List[Any]	=		encoder_ffn_dim
             _lowerCamelCase							:     int	=		encoder_layers
             _lowerCamelCase							:     str	=		encoder_attention_heads
             _lowerCamelCase							:     List[Any]	=		decoder_ffn_dim
             _lowerCamelCase							:     Optional[int]	=		decoder_layers
             _lowerCamelCase							:     int	=		decoder_attention_heads
             _lowerCamelCase							:     Tuple	=		dropout
             _lowerCamelCase							:     Optional[Any]	=		attention_dropout
             _lowerCamelCase							:     int	=		activation_dropout
             _lowerCamelCase							:     Dict	=		activation_function
             _lowerCamelCase							:     Union[str, Any]	=		init_std
             _lowerCamelCase							:     List[str]	=		encoder_layerdrop
             _lowerCamelCase							:     Optional[int]	=		decoder_layerdrop
             _lowerCamelCase							:     Union[str, Any]	=		use_cache
             _lowerCamelCase							:     Tuple	=		encoder_layers
             _lowerCamelCase							:     Tuple	=		scale_embedding  # scale factor will be sqrt(d_model) if True
             super().__init__(
                 pad_token_id=a__      ,							eos_token_id=a__      ,							is_encoder_decoder=a__      ,							decoder_start_token_id=a__      ,							forced_eos_token_id=a__      ,							**a__      ,							)


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return self.encoder_attention_heads



      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return self.d_model








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  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-canny'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Union[str, Any]	=		controlnet_params

             _lowerCamelCase							:     str	=		'''bird'''
             _lowerCamelCase							:     Union[str, Any]	=		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							:     Tuple	=		pipe.prepare_image_inputs([canny_image] * num_samples)

             _lowerCamelCase							:     Union[str, Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     Tuple	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     Dict	=		replicate(a__)
             _lowerCamelCase							:     Tuple	=		shard(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)

             _lowerCamelCase							:     Optional[Any]	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

             _lowerCamelCase							:     Optional[int]	=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
             _lowerCamelCase							:     List[Any]	=		images[0, 253:256, 253:256, -1]

             _lowerCamelCase							:     Union[str, Any]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-openpose'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Optional[Any]	=		controlnet_params

             _lowerCamelCase							:     Optional[Any]	=		'''Chef in the kitchen'''
             _lowerCamelCase							:     int	=		jax.device_count()
             _lowerCamelCase							:     Dict	=		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							:     str	=		pipe.prepare_image_inputs([pose_image] * num_samples)

             _lowerCamelCase							:     Optional[Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     List[str]	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     str	=		replicate(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)
             _lowerCamelCase							:     List[Any]	=		shard(a__)

             _lowerCamelCase							:     Any	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

             _lowerCamelCase							:     Optional[int]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		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

