
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort







class        A_   (			__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       """ssube/stable-diffusion-x4-upscaler-onnx"""





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=0       ):
		lowercase       =    floats_tensor((1, 3, 128, 128)		,    rng=random.Random(snake_case       )       )
		lowercase       =    torch.manual_seed(snake_case       )
		lowercase       =    {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'image': image,
		    'generator': generator,
		    'num_inference_steps': 3,
		    'guidance_scale': 7.5,
		    'output_type': 'numpy',
		}
		return inputs





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'       )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    self.get_dummy_inputs()
		lowercase       =    pipe(**snake_case       ).images
		lowercase       =    image[0, -3:, -3:, -1].flatten()

		# started as 128, should now be 512
		assert image.shape == (1, 512, 512, 3)
		lowercase       =    np.array(
		    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]       )
		assert np.abs(image_slice - expected_slice       ).max() < 1E-1





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'       )
		lowercase       =    PNDMScheduler.from_config(pipe.scheduler.config		,    skip_prk_steps=snake_case       )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    self.get_dummy_inputs()
		lowercase       =    pipe(**snake_case       ).images
		lowercase       =    image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		lowercase       =    np.array(
		    [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]       )
		assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'       )
		lowercase       =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    self.get_dummy_inputs()
		lowercase       =    pipe(**snake_case       ).images
		lowercase       =    image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		lowercase       =    np.array(
		    [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]       )

		assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'       )
		lowercase       =    EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    self.get_dummy_inputs()
		lowercase       =    pipe(**snake_case       ).images
		lowercase       =    image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		lowercase       =    np.array(
		    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]       )
		assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'       )
		lowercase       =    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    self.get_dummy_inputs()
		lowercase       =    pipe(**snake_case       ).images
		lowercase       =    image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		lowercase       =    np.array(
		    [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]       )

		assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-1







@nightly
@require_onnxruntime
@require_torch_gpu
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return (
		    "CUDAExecutionProvider",
		    {
		        "gpu_mem_limit": "15000000000",  # 15GB
		        "arena_extend_strategy": "kSameAsRequested",
		    },
		)





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ort.SessionOptions()
		lowercase       =    False
		return options





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/img2img/sketch-mountains-input.jpg'       )
		lowercase       =    init_image.resize((128, 128)       )
		# using the PNDM scheduler by default
		lowercase       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(
		    'ssube/stable-diffusion-x4-upscaler-onnx'		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    'A fantasy landscape, trending on artstation'

		lowercase       =    torch.manual_seed(0       )
		lowercase       =    pipe(
		    prompt=snake_case		,    image=snake_case		,    guidance_scale=7.5		,    num_inference_steps=10		,    generator=snake_case		,    output_type='np'		,    )
		lowercase       =    output.images
		lowercase       =    images[0, 255:258, 383:386, -1]

		assert images.shape == (1, 512, 512, 3)
		lowercase       =    np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]       )
		# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

		assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2E-2





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/img2img/sketch-mountains-input.jpg'       )
		lowercase       =    init_image.resize((128, 128)       )
		lowercase       =    LMSDiscreteScheduler.from_pretrained(
		    'ssube/stable-diffusion-x4-upscaler-onnx'		,    subfolder='scheduler'       )
		lowercase       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(
		    'ssube/stable-diffusion-x4-upscaler-onnx'		,    scheduler=snake_case		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    'A fantasy landscape, trending on artstation'

		lowercase       =    torch.manual_seed(0       )
		lowercase       =    pipe(
		    prompt=snake_case		,    image=snake_case		,    guidance_scale=7.5		,    num_inference_steps=20		,    generator=snake_case		,    output_type='np'		,    )
		lowercase       =    output.images
		lowercase       =    images[0, 255:258, 383:386, -1]

		assert images.shape == (1, 512, 512, 3)
		lowercase       =    np.array(
		    [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]       )
		# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

		assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2E-2



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase	      = '''Create a default config file for Accelerate with only a few flags set.'''






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE="no" ,							__SCREAMING_SNAKE_CASE = default_json_config_file ,							__SCREAMING_SNAKE_CASE = False							):
	lowercase       =    Path(__SCREAMING_SNAKE_CASE							)
	path.parent.mkdir(parents=__SCREAMING_SNAKE_CASE ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	if path.exists():
		print(
		    F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''							)
		return False
	lowercase       =    mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''							)
	lowercase       =    {
	    'compute_environment': 'LOCAL_MACHINE',
	    'mixed_precision': mixed_precision,
	}
	if torch.cuda.is_available():
		lowercase       =    torch.cuda.device_count()
		lowercase       =    num_gpus
		lowercase       =    False
		if num_gpus > 1:
			lowercase       =    'MULTI_GPU'
		else:
			lowercase       =    'NO'
	elif is_xpu_available() and use_xpu:
		lowercase       =    torch.xpu.device_count()
		lowercase       =    num_xpus
		lowercase       =    False
		if num_xpus > 1:
			lowercase       =    'MULTI_XPU'
		else:
			lowercase       =    'NO'
	elif is_npu_available():
		lowercase       =    torch.npu.device_count()
		lowercase       =    num_npus
		lowercase       =    False
		if num_npus > 1:
			lowercase       =    'MULTI_NPU'
		else:
			lowercase       =    'NO'
	else:
		lowercase       =    0
		lowercase       =    True
		lowercase       =    1
		lowercase       =    'NO'
	lowercase       =    ClusterConfig(**__SCREAMING_SNAKE_CASE							)
	config.to_json_file(__SCREAMING_SNAKE_CASE							)
	return path






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    parser.add_parser('default' ,							parents=__SCREAMING_SNAKE_CASE ,							help=__SCREAMING_SNAKE_CASE ,							formatter_class=__SCREAMING_SNAKE_CASE							)
	parser.add_argument(
	    '--config_file' ,							default=__SCREAMING_SNAKE_CASE ,							help=(
	        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
	        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
	        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
	        'with \'huggingface\'.'
	    ) ,							dest='save_location' ,							)

	parser.add_argument(
	    '--mixed_precision' ,							choices=['no', 'fp16', 'bf16'] ,							type=__SCREAMING_SNAKE_CASE ,							help='Whether or not to use mixed precision training. '
	    'Choose between FP16 and BF16 (bfloat16) training. '
	    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' ,							default='no' ,							)
	parser.set_defaults(func=__SCREAMING_SNAKE_CASE							)
	return parser






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    write_basic_config(args.mixed_precision ,							args.save_location							)
	if config_file:
		print(F'''accelerate configuration saved at {config_file}'''							)

