




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   ):






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = FlaxControlNetModel.from_pretrained(
											    "lllyasviel/sd-controlnet-canny"	,    from_pt=__a	,    dtype=jnp.bfloataa    )
											__lowerCAmelCase            = FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    "runwayml/stable-diffusion-v1-5"	,    controlnet=__a	,    from_pt=__a	,    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(__a	,    jax.device_count()    )

											__lowerCAmelCase            = replicate(__a    )
											__lowerCAmelCase            = shard(__a    )
											__lowerCAmelCase            = shard(__a    )

											__lowerCAmelCase            = 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)

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

											__lowerCAmelCase            = jnp.asarray(jax.device_get(image_slice.flatten()    )    )
											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = FlaxControlNetModel.from_pretrained(
											    "lllyasviel/sd-controlnet-openpose"	,    from_pt=__a	,    dtype=jnp.bfloataa    )
											__lowerCAmelCase            = FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    "runwayml/stable-diffusion-v1-5"	,    controlnet=__a	,    from_pt=__a	,    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(__a	,    jax.device_count()    )

											__lowerCAmelCase            = replicate(__a    )
											__lowerCAmelCase            = shard(__a    )
											__lowerCAmelCase            = shard(__a    )

											__lowerCAmelCase            = 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)

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

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










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


UpperCamelCase__					    =    logging.get_logger(__name__)

UpperCamelCase__					    =    {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class     a__					(      snake_case__   ):
						_a							:					Tuple      =					"""nllb-moe"""
						_a							:					Dict      =					["""past_key_values"""]
						_a							:					Optional[int]      =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(     self	,    _A=1_2_8_1_1_2	,    _A=1_0_2_4	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=0.05	,    _A=0.05	,    _A=True	,    _A=True	,    _A="relu"	,    _A=1_0_2_4	,    _A=0.1	,    _A=0.1	,    _A=0.0	,    _A=0.02	,    _A=2	,    _A=True	,    _A=False	,    _A="float32"	,    _A=False	,    _A=1_2_8	,    _A=6_4	,    _A=4	,    _A=4	,    _A=0.0_01	,    _A=0.0_01	,    _A="all"	,    _A=False	,    _A=False	,    _A=1.0	,    _A=0.2	,    _A=1	,    _A=0	,    _A=2	,    _A=False	,    **_A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = d_model
											__lowerCAmelCase            = encoder_ffn_dim
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = encoder_attention_heads
											__lowerCAmelCase            = decoder_ffn_dim
											__lowerCAmelCase            = decoder_layers
											__lowerCAmelCase            = decoder_attention_heads
											__lowerCAmelCase            = dropout
											__lowerCAmelCase            = attention_dropout
											__lowerCAmelCase            = activation_dropout
											__lowerCAmelCase            = activation_function
											__lowerCAmelCase            = init_std
											__lowerCAmelCase            = encoder_layerdrop
											__lowerCAmelCase            = decoder_layerdrop
											__lowerCAmelCase            = use_cache
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCAmelCase            = router_z_loss_coef
											__lowerCAmelCase            = router_aux_loss_coef
											__lowerCAmelCase            = decoder_sparse_step
											__lowerCAmelCase            = encoder_sparse_step
											__lowerCAmelCase            = num_experts
											__lowerCAmelCase            = expert_capacity
											__lowerCAmelCase            = router_bias
											if router_dtype not in ["float32", "float16", "bfloat16"]:
																raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""    )
											__lowerCAmelCase            = router_dtype

											__lowerCAmelCase            = router_ignore_padding_tokens
											__lowerCAmelCase            = batch_prioritized_routing
											__lowerCAmelCase            = second_expert_policy
											__lowerCAmelCase            = normalize_router_prob_before_dropping
											__lowerCAmelCase            = moe_eval_capacity_token_fraction
											__lowerCAmelCase            = moe_token_dropout
											__lowerCAmelCase            = output_router_logits
											super().__init__(
											    pad_token_id=_A	,    bos_token_id=_A	,    eos_token_id=_A	,    is_encoder_decoder=_A	,    decoder_start_token_id=_A	,    **_A	,    )




