




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        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       super().tearDown()
       gc.collect()




 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-inpaint/init_image.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

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

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

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

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

       # shard inputs and rng
       SCREAMING_SNAKE_CASE_							=			replicate(snake_case__						)
       SCREAMING_SNAKE_CASE_							=			jax.random.split(snake_case__				,				jax.device_count()						)
       SCREAMING_SNAKE_CASE_							=			shard(snake_case__						)
       SCREAMING_SNAKE_CASE_							=			shard(snake_case__						)
       SCREAMING_SNAKE_CASE_							=			shard(snake_case__						)

       SCREAMING_SNAKE_CASE_							=			pipeline(
           snake_case__				,				snake_case__				,				snake_case__				,				snake_case__				,				snake_case__				,				snake_case__				,				jit=snake_case__						)

       SCREAMING_SNAKE_CASE_							=			output.images.reshape(snake_case__				,				512				,				512				,				3						)

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

       SCREAMING_SNAKE_CASE_							=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
       SCREAMING_SNAKE_CASE_							=			jnp.array(
           [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]						)
       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							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "megatron-bert"



   def __init__(self		:					Optional[int]    ,						snake_case__		:					List[str]=2_90_56    ,						snake_case__		:					List[Any]=10_24    ,						snake_case__		:					str=24    ,						snake_case__		:					Tuple=16    ,						snake_case__		:					Union[str, Any]=40_96    ,						snake_case__		:					str="gelu"    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Optional[int]=0.1    ,						snake_case__		:					Tuple=5_12    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					Dict=0.02    ,						snake_case__		:					List[Any]=1e-12    ,						snake_case__		:					int=0    ,						snake_case__		:					Tuple="absolute"    ,						snake_case__		:					Any=True    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Optional[Any]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						**snake_case__ )

          snake_case       :   Tuple							=      vocab_size
          snake_case       :   str							=      hidden_size
          snake_case       :   str							=      num_hidden_layers
          snake_case       :   str							=      num_attention_heads
          snake_case       :   Optional[int]							=      hidden_act
          snake_case       :   int							=      intermediate_size
          snake_case       :   List[str]							=      hidden_dropout_prob
          snake_case       :   Union[str, Any]							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   List[str]							=      type_vocab_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   Tuple							=      layer_norm_eps
          snake_case       :   int							=      position_embedding_type
          snake_case       :   str							=      use_cache





