





import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()






		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/sd2-inpaint/init_image.png"""  )
									lowercase_	:   str  				=     load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""  )

									lowercase_	:   List[str]  				=     """xvjiarui/stable-diffusion-2-inpainting"""
									lowercase_				,		lowercase_	:   int  				=     FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__,   safety_checker=snake_case__  )

									lowercase_	:   Any  				=     """Face of a yellow cat, high resolution, sitting on a park bench"""

									lowercase_	:   Union[str, Any]  				=     jax.random.PRNGKey(0  )
									lowercase_	:   int  				=     50

									lowercase_	:   List[str]  				=     jax.device_count()
									lowercase_	:   int  				=     num_samples * [prompt]
									lowercase_	:   int  				=     num_samples * [init_image]
									lowercase_	:   str  				=     num_samples * [mask_image]
									lowercase_				,		lowercase_				,		lowercase_	:   Dict  				=     pipeline.prepare_inputs(snake_case__,   snake_case__,   snake_case__  )

									# shard inputs and rng
									lowercase_	:   List[Any]  				=     replicate(snake_case__  )
									lowercase_	:   List[Any]  				=     jax.random.split(snake_case__,   jax.device_count()  )
									lowercase_	:   Union[str, Any]  				=     shard(snake_case__  )
									lowercase_	:   Optional[int]  				=     shard(snake_case__  )
									lowercase_	:   List[Any]  				=     shard(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     pipeline(
									    snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   jit=snake_case__  )

									lowercase_	:   Optional[int]  				=     output.images.reshape(snake_case__,   5_12,   5_12,   3  )

									lowercase_	:   Optional[Any]  				=     images[0, 2_53:2_56, 2_53:2_56, -1]

									lowercase_	:   Dict  				=     jnp.asarray(jax.device_get(image_slice.flatten()  )  )
									lowercase_	:   Union[str, Any]  				=     jnp.array(
									    [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]  )
									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_							=				{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Optional[int]       =	"""canine"""






		def __init__(							self,   snake_case__=7_68,   snake_case__=12,   snake_case__=12,   snake_case__=30_72,   snake_case__="gelu",   snake_case__=0.1,   snake_case__=0.1,   snake_case__=1_63_84,   snake_case__=16,   snake_case__=0.02,   snake_case__=1E-12,   snake_case__=0,   snake_case__=0XE_0_0_0,   snake_case__=0XE_0_0_1,   snake_case__=4,   snake_case__=4,   snake_case__=8,   snake_case__=1_63_84,   snake_case__=1_28,   **snake_case__,   )       -> Optional[int]:






									"""simple docstring"""
									super().__init__(pad_token_id=snake_case__,   bos_token_id=snake_case__,   eos_token_id=snake_case__,   **snake_case__  )

									lowercase_	:   int  				=     max_position_embeddings
									lowercase_	:   Union[str, Any]  				=     hidden_size
									lowercase_	:   Optional[Any]  				=     num_hidden_layers
									lowercase_	:   str  				=     num_attention_heads
									lowercase_	:   List[Any]  				=     intermediate_size
									lowercase_	:   Optional[Any]  				=     hidden_act
									lowercase_	:   List[str]  				=     hidden_dropout_prob
									lowercase_	:   Any  				=     attention_probs_dropout_prob
									lowercase_	:   Union[str, Any]  				=     initializer_range
									lowercase_	:   Optional[Any]  				=     type_vocab_size
									lowercase_	:   Dict  				=     layer_norm_eps

									# Character config:
									lowercase_	:   Optional[int]  				=     downsampling_rate
									lowercase_	:   List[str]  				=     upsampling_kernel_size
									lowercase_	:   int  				=     num_hash_functions
									lowercase_	:   List[str]  				=     num_hash_buckets
									lowercase_	:   List[str]  				=     local_transformer_stride