
# 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


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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int="no"      ,					UpperCAmelCase__					:   str = default_json_config_file      ,					UpperCAmelCase__					:   bool = False							)	->     List[str]:
							lowerCamelCase_					=       Path(UpperCAmelCase__							)
							path.parent.mkdir(parents=UpperCAmelCase__      ,					exist_ok=UpperCAmelCase__							)
							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
							lowerCamelCase_					=       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}'''							)
							lowerCamelCase_					=       {
							    """compute_environment""": """LOCAL_MACHINE""",
							    """mixed_precision""": mixed_precision,
							}
							if torch.cuda.is_available():
														lowerCamelCase_					=       torch.cuda.device_count()
														lowerCamelCase_					=       num_gpus
														lowerCamelCase_					=       False
														if num_gpus > 1:
																					lowerCamelCase_					=       """MULTI_GPU"""
														else:
																					lowerCamelCase_					=       """NO"""
							elif is_xpu_available() and use_xpu:
														lowerCamelCase_					=       torch.xpu.device_count()
														lowerCamelCase_					=       num_xpus
														lowerCamelCase_					=       False
														if num_xpus > 1:
																					lowerCamelCase_					=       """MULTI_XPU"""
														else:
																					lowerCamelCase_					=       """NO"""
							elif is_npu_available():
														lowerCamelCase_					=       torch.npu.device_count()
														lowerCamelCase_					=       num_npus
														lowerCamelCase_					=       False
														if num_npus > 1:
																					lowerCamelCase_					=       """MULTI_NPU"""
														else:
																					lowerCamelCase_					=       """NO"""
							else:
														lowerCamelCase_					=       0
														lowerCamelCase_					=       True
														lowerCamelCase_					=       1
														lowerCamelCase_					=       """NO"""
							lowerCamelCase_					=       ClusterConfig(**UpperCAmelCase__							)
							config.to_json_file(UpperCAmelCase__							)
							return path



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   Tuple							)	->     Any:
							lowerCamelCase_					=       parser.add_parser("""default"""      ,					parents=UpperCAmelCase__      ,					help=UpperCAmelCase__      ,					formatter_class=UpperCAmelCase__							)
							parser.add_argument(
							    """--config_file"""      ,					default=UpperCAmelCase__      ,					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=UpperCAmelCase__      ,					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=UpperCAmelCase__							)
							return parser



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]							)	->     Dict:
							lowerCamelCase_					=       write_basic_config(args.mixed_precision      ,					args.save_location							)
							if config_file:
														print(F'''accelerate configuration saved at {config_file}'''							)







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class    __A(					UpperCAmelCase  ,		UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     StableDiffusionInstructPixaPixPipeline
					SCREAMING_SNAKE_CASE           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
					SCREAMING_SNAKE_CASE           =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					SCREAMING_SNAKE_CASE           =     IMAGE_TO_IMAGE_IMAGE_PARAMS
					SCREAMING_SNAKE_CASE           =     IMAGE_TO_IMAGE_IMAGE_PARAMS

					def  lowercase__   (						self       :  List[Any] ):
												torch.manual_seed(0 )
												lowerCamelCase_					=       UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4) ,       layers_per_block=2 ,       sample_size=3_2 ,       in_channels=8 ,       out_channels=4 ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,       cross_attention_dim=3_2 ,       )
												lowerCamelCase_					=       PNDMScheduler(skip_prk_steps=__UpperCamelCase )
												torch.manual_seed(0 )
												lowerCamelCase_					=       AutoencoderKL(
												    block_out_channels=[3_2, 6_4] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,       latent_channels=4 ,       )
												torch.manual_seed(0 )
												lowerCamelCase_					=       CLIPTextConfig(
												    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=3_2 ,       intermediate_size=3_7 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_0_0_0 ,       )
												lowerCamelCase_					=       CLIPTextModel(__UpperCamelCase )
												lowerCamelCase_					=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

												lowerCamelCase_					=       {
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """safety_checker""": None,
												    """feature_extractor""": None,
												}
												return components

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Any=0 ):
												lowerCamelCase_					=       floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 )[0]
												lowerCamelCase_					=       Image.fromarray(np.uinta(__UpperCamelCase ) ).convert("""RGB""" )
												if str(__UpperCamelCase ).startswith("""mps""" ):
																			lowerCamelCase_					=       torch.manual_seed(__UpperCamelCase )
												else:
																			lowerCamelCase_					=       torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
												lowerCamelCase_					=       {
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """guidance_scale""": 6.0,
												    """image_guidance_scale""": 1,
												    """output_type""": """numpy""",
												}
												return inputs

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]
												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       """french fries"""
												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ,       negative_prompt=__UpperCamelCase )
												lowerCamelCase_					=       output.images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]

												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       [inputs["""prompt"""]] * 2

												lowerCamelCase_					=       np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
												lowerCamelCase_					=       torch.from_numpy(__UpperCamelCase ).unsqueeze(0 ).to(__UpperCamelCase )
												lowerCamelCase_					=       image / 2 + 0.5
												lowerCamelCase_					=       image.permute(0 ,       3 ,       1 ,       2 )
												lowerCamelCase_					=       image.repeat(2 ,       1 ,       1 ,       1 )

												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[-1, -3:, -3:, -1]

												assert image.shape == (2, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       EulerAncestralDiscreteScheduler(
												    beta_start=0.00085 ,       beta_end=0.012 ,       beta_schedule="""scaled_linear""" )
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]

												lowerCamelCase_					=       [round(__UpperCamelCase ,       4 ) for x in image_slice.flatten().tolist()]
												print(""",""".join([str(__UpperCamelCase ) for x in slice] ) )

												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  str ):
												super().test_inference_batch_single_identical(expected_max_diff=3E-3 )



					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       VaeImageProcessor(do_resize=__UpperCamelCase ,       do_normalize=__UpperCamelCase )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       pipe(**self.get_dummy_inputs_by_type(__UpperCamelCase ,       input_image_type="""pt""" ) )[0]

												lowerCamelCase_					=       components["""vae"""]
												lowerCamelCase_					=       self.get_dummy_inputs_by_type(__UpperCamelCase ,       input_image_type="""pt""" )

												for image_param in self.image_latents_params:
																			if image_param in inputs.keys():
																										lowerCamelCase_					=       vae.encode(inputs[image_param] ).latent_dist.mode()

												lowerCamelCase_					=       pipe(**__UpperCamelCase )[0]

												lowerCamelCase_					=       np.abs(out - out_latents_inputs ).max()
												self.assertLess(__UpperCamelCase ,       1E-4 ,       """passing latents as image input generate different result from passing image""" )





@slow
@require_torch_gpu
class    __A(					unittest.TestCase							):

					def  lowercase__   (						self       :  List[Any] ):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Union[str, Any]=0 ):
												lowerCamelCase_					=       torch.manual_seed(__UpperCamelCase )
												lowerCamelCase_					=       load_image(
												    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
												lowerCamelCase_					=       {
												    """prompt""": """turn him into a cyborg""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 3,
												    """guidance_scale""": 7.5,
												    """image_guidance_scale""": 1.0,
												    """output_type""": """numpy""",
												}
												return inputs

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )

												assert np.abs(expected_slice - image_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												lowerCamelCase_					=       LMSDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )

												assert np.abs(expected_slice - image_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												lowerCamelCase_					=       DDIMScheduler.from_config(pipe.scheduler.config )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )

												assert np.abs(expected_slice - image_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       0

												def callback_fn(__UpperCamelCase       :  int ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  torch.FloatTensor ) -> None:
																			lowerCamelCase_					=       True
																			nonlocal number_of_steps
																			number_of_steps += 1
																			if step == 1:
																										lowerCamelCase_					=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )

																										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
																			elif step == 2:
																										lowerCamelCase_					=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )

																										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2

												lowerCamelCase_					=       False

												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase ,       torch_dtype=torch.floataa )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												pipe(**__UpperCamelCase ,       callback=__UpperCamelCase ,       callback_steps=1 )
												assert callback_fn.has_been_called
												assert number_of_steps == 3

					def  lowercase__   (						self       :  int ):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase ,       torch_dtype=torch.floataa )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing(1 )
												pipe.enable_sequential_cpu_offload()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase )

												lowerCamelCase_					=       torch.cuda.max_memory_allocated()
												# make sure that less than 2.2 GB is allocated
												assert mem_bytes < 2.2 * 1_0**9



					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.get_inputs()
												# resize to resolution that is divisible by 8 but not 16 or 32
												lowerCamelCase_					=       inputs["""image"""].resize((5_0_4, 5_0_4) )

												lowerCamelCase_					=       """timbrooks/instruct-pix2pix"""
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    __UpperCamelCase ,       safety_checker=__UpperCamelCase ,       )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       pipe(**__UpperCamelCase )
												lowerCamelCase_					=       output.images[0]

												lowerCamelCase_					=       image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert image.shape == (5_0_4, 5_0_4, 3)
												lowerCamelCase_					=       np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3





