




'''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 			_a  (	unittest.TestCase ):
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

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







       def     A      (	self						:	Tuple				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		'''xvjiarui/stable-diffusion-2-inpainting'''
           UpperCAmelCase       ,     UpperCAmelCase			       =		FlaxStableDiffusionInpaintPipeline.from_pretrained(lowercase   ,				safety_checker=lowercase				)

           UpperCAmelCase			       =		'''Face of a yellow cat, high resolution, sitting on a park bench'''

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

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

           # shard inputs and rng
           UpperCAmelCase			       =		replicate(lowercase				)
           UpperCAmelCase			       =		jax.random.split(lowercase   ,				jax.device_count()				)
           UpperCAmelCase			       =		shard(lowercase				)
           UpperCAmelCase			       =		shard(lowercase				)
           UpperCAmelCase			       =		shard(lowercase				)

           UpperCAmelCase			       =		pipeline(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				jit=lowercase				)

           UpperCAmelCase			       =		output.images.reshape(lowercase   ,				512   ,				512   ,				3				)

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

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











"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_     :						str						=					logging.get_logger(__name__)

lowerCamelCase_     :						Any						=					{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   """xmod"""

      def __init__(     self					,      snake_case_=3_0_5_2_2					,      snake_case_=7_6_8					,      snake_case_=1_2					,      snake_case_=1_2					,      snake_case_=3_0_7_2					,      snake_case_="gelu"					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=5_1_2					,      snake_case_=2					,      snake_case_=0.02					,      snake_case_=1E-12					,      snake_case_=1					,      snake_case_=0					,      snake_case_=2					,      snake_case_="absolute"					,      snake_case_=True					,      snake_case_=None					,      snake_case_=False					,      snake_case_=2					,      snake_case_=False					,      snake_case_=True					,      snake_case_=True					,      snake_case_=("en_XX",)					,      snake_case_=None					,      **snake_case_					,      ):

        """simple docstring"""

        super().__init__(pad_token_id=snake_case_					,      bos_token_id=snake_case_					,      eos_token_id=snake_case_					,      **snake_case_  )

        A_  :       Union[str, Any]										=  vocab_size
        A_  :       Any										=  hidden_size
        A_  :       List[str]										=  num_hidden_layers
        A_  :       Tuple										=  num_attention_heads
        A_  :       int										=  hidden_act
        A_  :       Any										=  intermediate_size
        A_  :       Any										=  hidden_dropout_prob
        A_  :       Dict										=  attention_probs_dropout_prob
        A_  :       Union[str, Any]										=  max_position_embeddings
        A_  :       List[Any]										=  type_vocab_size
        A_  :       List[str]										=  initializer_range
        A_  :       Any										=  layer_norm_eps
        A_  :       Optional[Any]										=  position_embedding_type
        A_  :       int										=  use_cache
        A_  :       Dict										=  classifier_dropout
        A_  :       int										=  pre_norm
        A_  :       Optional[Any]										=  adapter_reduction_factor
        A_  :       List[Any]										=  adapter_layer_norm
        A_  :       int										=  adapter_reuse_layer_norm
        A_  :       Dict										=  ln_before_adapter
        A_  :       List[str]										=  list(snake_case_  )
        A_  :       Union[str, Any]										=  default_language


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_  :       Dict										=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          A_  :       int										=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]  )