





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):







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__					,							a__	:					int										=FlaxControlNetModel.from_pretrained(
            "lllyasviel/sd-controlnet-canny"		,      from_pt=A_		,      dtype=jnp.bfloataa							)
        a__					,							a__	:					Any										=FlaxStableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5"		,      controlnet=A_		,      from_pt=A_		,      dtype=jnp.bfloataa							)
        a__	:					List[str]										=controlnet_params

        a__	:					Any										="bird"
        a__	:					Any										=jax.device_count()
        a__	:					int										=pipe.prepare_text_inputs([prompts] * num_samples							)

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

        a__	:					str										=jax.random.PRNGKey(0							)
        a__	:					Optional[int]										=jax.random.split(A_		,      jax.device_count()							)

        a__	:					Union[str, Any]										=replicate(A_							)
        a__	:					Optional[int]										=shard(A_							)
        a__	:					Union[str, Any]										=shard(A_							)

        a__	:					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)

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

        a__	:					Tuple										=jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        a__	:					List[Any]										=jnp.array(
            [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]							)
        print(F'''output_slice: {output_slice}'''							)
        assert jnp.abs(output_slice - expected_slice							).max() < 1E-2







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




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

        a__	:					Any										="Chef in the kitchen"
        a__	:					List[Any]										=jax.device_count()
        a__	:					Tuple										=pipe.prepare_text_inputs([prompts] * num_samples							)

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

        a__	:					Optional[int]										=jax.random.PRNGKey(0							)
        a__	:					Union[str, Any]										=jax.random.split(A_		,      jax.device_count()							)

        a__	:					Optional[int]										=replicate(A_							)
        a__	:					Dict										=shard(A_							)
        a__	:					Dict										=shard(A_							)

        a__	:					Optional[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)

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

        a__	:					Optional[Any]										=jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        a__	:					Optional[Any]										=jnp.array(
            [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]							)
        print(F'''output_slice: {output_slice}'''							)
        assert jnp.abs(output_slice - expected_slice							).max() < 1E-2










import warnings

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __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    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





