
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 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()





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

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

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

												lowercase							=						jax.random.PRNGKey(0)
												lowercase							=						5_0

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

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

												lowercase							=						pipeline(
												    A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,jit=A__)

												lowercase							=						output.images.reshape(A__   ,5_1_2   ,5_1_2   ,3)

												lowercase							=						images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

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








"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "XGLMForCausalLM",
				    "XGLMModel",
				    "XGLMPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FlaxXGLMForCausalLM",
				    "FlaxXGLMModel",
				    "FlaxXGLMPreTrainedModel",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFXGLMForCausalLM",
				    "TFXGLMModel",
				    "TFXGLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm import XGLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm_fast import XGLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xglm import (
								    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXGLMForCausalLM,
								    TFXGLMModel,
								    TFXGLMPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure)






