

'''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				:     Tuple			)     ->   Tuple:





       '''simple docstring'''






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





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





       '''simple docstring'''






       _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   ,						_SCREAMING_SNAKE_CASE							   =FlaxStableDiffusionInpaintPipeline.from_pretrained(_a							, safety_checker=_a			)

       _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   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =pipeline.prepare_inputs(_a							, _a							, _a			)

       # shard inputs and rng
       _SCREAMING_SNAKE_CASE							   =replicate(_a			)
       _SCREAMING_SNAKE_CASE							   =jax.random.split(_a							, jax.device_count()			)
       _SCREAMING_SNAKE_CASE							   =shard(_a			)
       _SCREAMING_SNAKE_CASE							   =shard(_a			)
       _SCREAMING_SNAKE_CASE							   =shard(_a			)

       _SCREAMING_SNAKE_CASE							   =pipeline(
           _a							, _a							, _a							, _a							, _a							, _a							, jit=_a			)

       _SCREAMING_SNAKE_CASE							   =output.images.reshape(_a							, 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(
           [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]			)
       print(f"output_slice: {output_slice}"			)
       assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase			:				int 			=					logging.getLogger(__name__)

if __name__ == "__main__":
     lowerCamelCase			:				Optional[Any] 			=					argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0_5_2_2, type=int)
     lowerCamelCase			:				Optional[Any] 			=					parser.parse_args()

     logger.info(f'''Loading data from {args.data_file}''')
     with open(args.data_file, "rb") as fp:
          lowerCamelCase			:				Optional[int] 			=					pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     lowerCamelCase			:				Dict 			=					Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     lowerCamelCase			:				Tuple 			=					[0] * args.vocab_size
     for k, v in counter.items():
          lowerCamelCase			:				Any 			=					v

     logger.info(f'''Dump to {args.token_counts_dump}''')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
