



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      ):

					"""simple docstring"""






					def 			snake_case					(							self :     List[str]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Any													=					load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png"					)
									lowercase__					:   Optional[Any]													=					load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)

									lowercase__					:   int													=					"xvjiarui/stable-diffusion-2-inpainting"
									lowercase__					:   List[str]													=					FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE				,       safety_checker=SCREAMING_SNAKE_CASE					)

									lowercase__					:   str													=					"Face of a yellow cat, high resolution, sitting on a park bench"

									lowercase__					:   Dict													=					jax.random.PRNGKey(0					)
									lowercase__					:   Optional[int]													=					50

									lowercase__					:   List[str]													=					jax.device_count()
									lowercase__					:   List[Any]													=					num_samples * [prompt]
									lowercase__					:   Tuple													=					num_samples * [init_image]
									lowercase__					:   Union[str, Any]													=					num_samples * [mask_image]
									lowercase__					:   Union[str, Any]													=					pipeline.prepare_inputs(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

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

									lowercase__					:   Optional[int]													=					pipeline(
									    SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       jit=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					output.images.reshape(SCREAMING_SNAKE_CASE				,       512				,       512				,       3					)

									lowercase__					:   Union[str, Any]													=					images[0, 253:256, 253:256, -1]

									lowercase__					:   List[str]													=					jnp.asarray(jax.device_get(image_slice.flatten()					)					)
									lowercase__					:   List[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




