


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     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Optional[int]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-canny'					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'					,   controlnet=A_					,   from_pt=A_					,   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(A_					,   jax.device_count()    )

         __UpperCamelCase    							=replicate(A_    )
         __UpperCamelCase    							=shard(A_    )
         __UpperCamelCase    							=shard(A_    )

         __UpperCamelCase    							=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)

         __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(
             [0.16_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 _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-openpose'					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'					,   controlnet=A_					,   from_pt=A_					,   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(A_					,   jax.device_count()    )

         __UpperCamelCase    							=replicate(A_    )
         __UpperCamelCase    							=shard(A_    )
         __UpperCamelCase    							=shard(A_    )

         __UpperCamelCase    							=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)

         __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(
             [[0.27_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









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.'    )





