




'''simple docstring'''





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  _snake_case		(	unittest.TestCase							):




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

										'''simple docstring'''




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




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

										'''simple docstring'''




										snake_case_						=      load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-inpaint/init_image.png"							)
										snake_case_						=      load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

										snake_case_						=      "xvjiarui/stable-diffusion-2-inpainting"
										snake_case_					,      snake_case_						=      FlaxStableDiffusionInpaintPipeline.from_pretrained(a__				,							safety_checker=a__							)

										snake_case_						=      "Face of a yellow cat, high resolution, sitting on a park bench"

										snake_case_						=      jax.random.PRNGKey(0							)
										snake_case_						=      50

										snake_case_						=      jax.device_count()
										snake_case_						=      num_samples * [prompt]
										snake_case_						=      num_samples * [init_image]
										snake_case_						=      num_samples * [mask_image]
										snake_case_					,      snake_case_					,      snake_case_						=      pipeline.prepare_inputs(a__				,							a__				,							a__							)

										# shard inputs and rng
										snake_case_						=      replicate(a__							)
										snake_case_						=      jax.random.split(a__				,							jax.device_count()							)
										snake_case_						=      shard(a__							)
										snake_case_						=      shard(a__							)
										snake_case_						=      shard(a__							)

										snake_case_						=      pipeline(
										    a__				,							a__				,							a__				,							a__				,							a__				,							a__				,							jit=a__							)

										snake_case_						=      output.images.reshape(a__				,							512				,							512				,							3							)

										snake_case_						=      images[0, 253:256, 253:256, -1]

										snake_case_						=      jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										snake_case_						=      jnp.array(
										    [0.3_6_1_1_3_0_7, 0.3_7_6_4_9_7_3_6, 0.3_7_5_7_4_0_8, 0.3_8_2_1_3_9_5_3, 0.3_9_2_9_5_1_6_7, 0.3_8_4_1_6_3_1, 0.4_1_5_5_4_9_7_8, 0.4_1_3_7_4_7_5, 0.4_2_1_7_0_8_4]							)
										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


__lowerCAmelCase			:     List[Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     int           =   {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""biogpt"""
  def __init__( self			: List[str]			,							UpperCamelCase__			: Optional[Any]=4_2384			,							UpperCamelCase__			: Union[str, Any]=1024			,							UpperCamelCase__			: Any=24			,							UpperCamelCase__			: Union[str, Any]=16			,							UpperCamelCase__			: Tuple=4096			,							UpperCamelCase__			: Tuple="gelu"			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: str=1024			,							UpperCamelCase__			: Any=0.02			,							UpperCamelCase__			: List[str]=1E-12			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: Optional[int]=True			,							UpperCamelCase__			: Union[str, Any]=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Dict=0			,							UpperCamelCase__			: List[str]=2			,							**UpperCamelCase__			: Optional[int]			,							)			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       initializer_range
    __magic_name__				     =       layer_norm_eps
    __magic_name__				     =       scale_embedding
    __magic_name__				     =       use_cache
    __magic_name__				     =       layerdrop
    __magic_name__				     =       activation_dropout
    super().__init__(pad_token_id=UpperCamelCase__			,							bos_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							**UpperCamelCase__    )

