



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







							def _a      (							self		):
											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(a_  ,  safety_checker=a_		)

											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(a_  ,  a_  ,  a_		)

											# shard inputs and rng
											a__				      =		replicate(a_		)
											a__				      =		jax.random.split(a_  ,  jax.device_count()		)
											a__				      =		shard(a_		)
											a__				      =		shard(a_		)
											a__				      =		shard(a_		)

											a__				      =		pipeline(
											    a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  jit=a_		)

											a__				      =		output.images.reshape(a_  ,  512  ,  512  ,  3		)

											a__				      =		images[0, 253:256, 253:256, -1]

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






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase							 =					"""3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
	import torch

	print("""Torch version:""", torch.__version__)
	print("""Cuda available:""", torch.cuda.is_available())
	print("""Cuda version:""", torch.version.cuda)
	print("""CuDNN version:""", torch.backends.cudnn.version())
	print("""Number of GPUs available:""", torch.cuda.device_count())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)

