
"""simple docstring"""





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__						(     __snake_case      )				->						Dict:


					"""simple docstring"""





					return EnvironmentCommand()



def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					return EnvironmentCommand(args.accelerate_config_file      )






class        _UpperCAmelCase(       lowerCamelCase      ):



		@staticmethod
		def 		UpperCAmelCase				(   __a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    parser.add_parser('''env''')
							download_parser.set_defaults(func=__a)
							download_parser.add_argument(
							    '''--accelerate-config_file'''							,						default=__a							,						help='''The accelerate config file to use for the default values in the launching script.'''							,						)
							download_parser.set_defaults(func=__a)



		def __init__(   self							,						__a							,						*__a)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    accelerate_config_file



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    '''not installed'''
							if is_safetensors_available():
												import safetensors

												_UpperCamelCase									=    safetensors.__version__
							elif importlib.util.find_spec('''safetensors''') is not None:
												import safetensors

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

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

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

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

							_UpperCamelCase									=    '''not installed'''
							_UpperCamelCase									=    '''NA'''
							if is_torch_available():
												import torch

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

							_UpperCamelCase									=    '''not installed'''
							_UpperCamelCase									=    '''NA'''
							if is_tf_available():
												import tensorflow as tf

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

							_UpperCamelCase									=    '''not installed'''
							_UpperCamelCase									=    '''not installed'''
							_UpperCamelCase									=    '''not installed'''
							_UpperCamelCase									=    '''NA'''
							if is_flax_available():
												import flax
												import jax
												import jaxlib

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

							_UpperCamelCase									=    {
							    '''`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(__a))

							return info




		@staticmethod
		def 		UpperCAmelCase				(   __a)			->			Union[str, Any]:






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








"""simple docstring"""





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DPMSolverSDEScheduler,)
		lowercase__         = 10



		def 		UpperCAmelCase				(   self							,						**__a)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''noise_sampler_seed''': 0,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							for timesteps in [10, 50, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,						[0.0002, 0.002, 0.02]):
												self.check_over_configs(beta_start=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
												assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
												assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3
							else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
												assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
												assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3
							else:
												assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
												assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps							,						device=__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
												assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
												assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3
							else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a							,						use_karras_sigmas=__a)

							scheduler.set_timesteps(self.num_inference_steps							,						device=__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for t in scheduler.timesteps:
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
							else:
												assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2






