

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]					)	->					List[Any]:




				'''simple docstring'''





				return EnvironmentCommand()



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					Tuple:




				'''simple docstring'''





				return EnvironmentCommand(args.accelerate_config_file					)





class     a__  (    snake_case	):
			"""simple docstring"""







			@staticmethod
			def    UpperCamelCase						(			lowercase				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	parser.add_parser("env"				)
							download_parser.set_defaults(func=lowercase				)
							download_parser.add_argument(
							    "--accelerate-config_file"					,					default=lowercase					,					help="The accelerate config file to use for the default values in the launching script."					,					)
							download_parser.set_defaults(func=lowercase				)


			def __init__(			self					,					lowercase					,					*lowercase				)     ->  None:
							'''simple docstring'''






							A__						=	accelerate_config_file


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	"not installed"
							if is_safetensors_available():
											import safetensors

											A__						=	safetensors.__version__
							elif importlib.util.find_spec("safetensors"				) is not None:
											import safetensors

											A__						=	F'{safetensors.__version__} but is ignored because of PyTorch version too old.'

							A__						=	"not installed"
							A__						=	A__						=	"not found"
							if is_accelerate_available():
											import accelerate
											from accelerate.commands.config import default_config_file, load_config_from_file

											A__						=	accelerate.__version__
											# Get the default from the config file.
											if self._accelerate_config_file is not None or os.path.isfile(lowercase				):
															A__						=	load_config_from_file(self._accelerate_config_file				).to_dict()

											A__						=	(
											    "\n".join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()]				)
											    if isinstance(lowercase					,					lowercase				)
											    else F'\t{accelerate_config}'
											)

							A__						=	"not installed"
							A__						=	"NA"
							if is_torch_available():
											import torch

											A__						=	torch.__version__
											A__						=	torch.cuda.is_available()

							A__						=	"not installed"
							A__						=	"NA"
							if is_tf_available():
											import tensorflow as tf

											A__						=	tf.__version__
											try:
															# deprecated in v2.1
															A__						=	tf.test.is_gpu_available()
											except AttributeError:
															# returns list of devices, convert to bool
															A__						=	bool(tf.config.list_physical_devices("GPU"				)				)

							A__						=	"not installed"
							A__						=	"not installed"
							A__						=	"not installed"
							A__						=	"NA"
							if is_flax_available():
											import flax
											import jax
											import jaxlib

											A__						=	flax.__version__
											A__						=	jax.__version__
											A__						=	jaxlib.__version__
											A__						=	jax.lib.xla_bridge.get_backend().platform

							A__						=	{
							    "`transformers` version": version,
							    "Platform": platform.platform(),
							    "Python version": platform.python_version(),
							    "Huggingface_hub version": huggingface_hub.__version__,
							    "Safetensors version": F'{safetensors_version}',
							    "Accelerate version": F'{accelerate_version}',
							    "Accelerate config": F'{accelerate_config_str}',
							    "PyTorch version (GPU?)": F'{pt_version} ({pt_cuda_available})',
							    "Tensorflow version (GPU?)": F'{tf_version} ({tf_cuda_available})',
							    "Flax version (CPU?/GPU?/TPU?)": F'{flax_version} ({jax_backend})',
							    "Jax version": F'{jax_version}',
							    "JaxLib version": F'{jaxlib_version}',
							    "Using GPU in script?": "<fill in>",
							    "Using distributed or parallel set-up in script?": "<fill in>",
							}

							print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"				)
							print(self.format_dict(lowercase				)				)

							return info


			@staticmethod
			def    UpperCamelCase						(			lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]				) + "\n"



import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


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



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
