
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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

           lowercase							:					List[Any]	     =				"""xvjiarui/stable-diffusion-2-inpainting"""
           lowercase							:					Tuple	     =				FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case		,safety_checker=snake_case				)

           lowercase							:					Optional[int]	     =				"""Face of a yellow cat, high resolution, sitting on a park bench"""

           lowercase							:					Optional[int]	     =				jax.random.PRNGKey(0				)
           lowercase							:					Any	     =				50

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

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

           lowercase							:					List[Any]	     =				pipeline(
               snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,jit=snake_case				)

           lowercase							:					Tuple	     =				output.images.reshape(snake_case		,512		,512		,3				)

           lowercase							:					Optional[Any]	     =				images[0, 253:256, 253:256, -1]

           lowercase							:					Any	     =				jnp.asarray(jax.device_get(image_slice.flatten()				)				)
           lowercase							:					str	     =				jnp.array(
               [0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084]				)
           print(f"output_slice: {output_slice}"				)
           assert jnp.abs(output_slice - expected_slice				).max() < 1e-2


from ...processing_utils import ProcessorMixin

class __snake_case		(				lowerCAmelCase					):
    _a		:						Union[str, Any]=    "WhisperFeatureExtractor"
    _a		:						int=    "WhisperTokenizer"
    def __init__(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case		,snake_case				)
           lowercase							:					Optional[int]	     =				self.feature_extractor
           lowercase							:					Tuple	     =				False
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=None		,snake_case=None		,snake_case=True				):



           '''simple docstring'''







           return self.tokenizer.get_decoder_prompt_ids(task=snake_case		,language=snake_case		,no_timestamps=snake_case				)
    def __call__(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor(*snake_case		,**snake_case				)

           lowercase							:					Optional[Any]	     =				kwargs.pop("""audio"""		,snake_case				)
           lowercase							:					str	     =				kwargs.pop("""sampling_rate"""		,snake_case				)
           lowercase							:					Dict	     =				kwargs.pop("""text"""		,snake_case				)
           if len(snake_case				) > 0:
                  lowercase							:					List[Any]	     =				args[0]
                  lowercase							:					Tuple	     =				args[1:]

           if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

           if audio is not None:
                  lowercase							:					Any	     =				self.feature_extractor(snake_case		,*snake_case		,sampling_rate=snake_case		,**snake_case				)
           if text is not None:
                  lowercase							:					str	     =				self.tokenizer(snake_case		,**snake_case				)

           if text is None:
                  return inputs

           elif audio is None:
                  return encodings
           else:
                  lowercase							:					List[Any]	     =				encodings["""input_ids"""]
                  return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case="np"				):



           '''simple docstring'''







           return self.tokenizer.get_prompt_ids(snake_case		,return_tensors=snake_case				)
