
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					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      Optional[int]:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-inpaint/init_image.png"					)
          snake_case			:  Union[str, Any]     							= load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)

          snake_case			:  str     							= "xvjiarui/stable-diffusion-2-inpainting"
          snake_case							,   snake_case			:  Optional[Any]     							= FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__       ,		safety_checker=snake_case__					)

          snake_case			:  Dict     							= "Face of a yellow cat, high resolution, sitting on a park bench"

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

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

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

          snake_case			:  Any     							= pipeline(
              snake_case__       ,		snake_case__       ,		snake_case__       ,		snake_case__       ,		snake_case__       ,		snake_case__       ,		jit=snake_case__					)

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

          snake_case			:  List[Any]     							= images[0, 2_53:2_56, 2_53:2_56, -1]

          snake_case			:  int     							= jnp.asarray(jax.device_get(image_slice.flatten()					)					)
          snake_case			:  List[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 collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}






class     UpperCAmelCase						(		A_					):
   A__   :						Optional[int]			=	"ibert"



   def __init__(self						:   List[Any]       ,		snake_case__						:   int=3_05_22       ,		snake_case__						:   int=7_68       ,		snake_case__						:   Any=12       ,		snake_case__						:   str=12       ,		snake_case__						:   Optional[int]=30_72       ,		snake_case__						:   Union[str, Any]="gelu"       ,		snake_case__						:   Any=0.1       ,		snake_case__						:   List[Any]=0.1       ,		snake_case__						:   str=5_12       ,		snake_case__						:   Optional[int]=2       ,		snake_case__						:   Any=0.02       ,		snake_case__						:   List[Any]=1e-12       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   List[Any]=0       ,		snake_case__						:   str=2       ,		snake_case__						:   Tuple="absolute"       ,		snake_case__						:   List[str]=False       ,		snake_case__						:   Tuple="none"       ,		**snake_case__						:   Tuple       ,		)						->      Union[str, Any]:


          '''simple docstring'''


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

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






class     UpperCAmelCase						(		A_					):



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          if self.task == "multiple-choice":
                 snake_case			:  Optional[Any]     							= {0: "batch", 1: "choice", 2: "sequence"}
          else:
                 snake_case			:  List[Any]     							= {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]					)





