



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   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


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



					def      _a				(  self       :							str       ):






								"""simple docstring"""


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

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

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

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

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

								# shard inputs and rng
								A__	  =     replicate(_snake_case       )
								A__	  =     jax.random.split(_snake_case					,       jax.device_count()       )
								A__	  =     shard(_snake_case       )
								A__	  =     shard(_snake_case       )
								A__	  =     shard(_snake_case       )

								A__	  =     pipeline(
								    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       jit=_snake_case       )

								A__	  =     output.images.reshape(_snake_case					,       5_12					,       5_12					,       3       )

								A__	  =     images[0, 2_53:2_56, 2_53:2_56, -1]

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase			:	Any								={
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[Any]								=[
	    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MegatronBertForCausalLM',
	    'MegatronBertForMaskedLM',
	    'MegatronBertForMultipleChoice',
	    'MegatronBertForNextSentencePrediction',
	    'MegatronBertForPreTraining',
	    'MegatronBertForQuestionAnswering',
	    'MegatronBertForSequenceClassification',
	    'MegatronBertForTokenClassification',
	    'MegatronBertModel',
	    'MegatronBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_megatron_bert import (
		    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegatronBertForCausalLM,
		    MegatronBertForMaskedLM,
		    MegatronBertForMultipleChoice,
		    MegatronBertForNextSentencePrediction,
		    MegatronBertForPreTraining,
		    MegatronBertForQuestionAnswering,
		    MegatronBertForSequenceClassification,
		    MegatronBertForTokenClassification,
		    MegatronBertModel,
		    MegatronBertPreTrainedModel,
		)

else:
	import sys

	_UpperCamelCase			:	Optional[int]								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



