




import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
				import transformers
def 		snake_case_   (UpperCamelCase	:  str				):





							'''simple docstring'''


							random.seed(A_				)
							np.random.seed(A_				)
							torch.manual_seed(A_				)
							torch.cuda.manual_seed_all(A_				)
							# ^^ safe to call this function even if cuda is not available

class       A   :







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Iterable[torch.nn.Parameter]							,      lowerCAmelCase_	:  float = 0.9_9_9_9							,      lowerCAmelCase_	:  float = 0.0							,      lowerCAmelCase_	:  int = 0							,      lowerCAmelCase_	:  bool = False							,      lowerCAmelCase_	:  Union[float, int] = 1.0							,      lowerCAmelCase_	:  Union[float, int] = 2 / 3							,      lowerCAmelCase_	:  Optional[Any] = None							,      lowerCAmelCase_	:  Dict[str, Any] = None							,      **lowerCAmelCase_	:  List[str]							,      )      ->							Dict:


														"""simple docstring"""


														if isinstance(lowercase_							,      torch.nn.Module ):
																					_a     					=    (
																					    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
																					    '''Please pass the parameters of the module instead.'''
																					)
																					deprecate(
																					    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''							,      '''1.0.0'''							,      lowercase_							,      standard_warn=lowercase_							,      )
																					_a     					=    parameters.parameters()

																					# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																					_a     					=    True

														if kwargs.get('''max_value'''							,      lowercase_ ) is not None:
																					_a     					=    '''The `max_value` argument is deprecated. Please use `decay` instead.'''
																					deprecate('''max_value'''							,      '''1.0.0'''							,      lowercase_							,      standard_warn=lowercase_ )
																					_a     					=    kwargs['''max_value''']

														if kwargs.get('''min_value'''							,      lowercase_ ) is not None:
																					_a     					=    '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
																					deprecate('''min_value'''							,      '''1.0.0'''							,      lowercase_							,      standard_warn=lowercase_ )
																					_a     					=    kwargs['''min_value''']

														_a     					=    list(lowercase_ )
														_a     					=    [p.clone().detach() for p in parameters]

														if kwargs.get('''device'''							,      lowercase_ ) is not None:
																					_a     					=    '''The `device` argument is deprecated. Please use `to` instead.'''
																					deprecate('''device'''							,      '''1.0.0'''							,      lowercase_							,      standard_warn=lowercase_ )
																					self.to(device=kwargs['''device'''] )

														_a     					=    None

														_a     					=    decay
														_a     					=    min_decay
														_a     					=    update_after_step
														_a     					=    use_ema_warmup
														_a     					=    inv_gamma
														_a     					=    power
														_a     					=    0
														_a     					=    None  # set in `step()`

														_a     					=    model_cls
														_a     					=    model_config







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Optional[int]							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  Tuple )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    model_cls.load_config(lowercase_							,      return_unused_kwargs=lowercase_ )
														_a     					=    model_cls.from_pretrained(lowercase_ )

														_a     					=    cls(model.parameters()							,      model_cls=lowercase_							,      model_config=model.config )

														ema_model.load_state_dict(lowercase_ )
														return ema_model







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  Tuple )      ->							Optional[int]:


														"""simple docstring"""


														if self.model_cls is None:
																					raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )

														if self.model_config is None:
																					raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )

														_a     					=    self.model_cls.from_config(self.model_config )
														_a     					=    self.state_dict()
														state_dict.pop('''shadow_params'''							,      lowercase_ )

														model.register_to_config(**lowercase_ )
														self.copy_to(model.parameters() )
														model.save_pretrained(lowercase_ )







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  int )      ->							Any:


														"""simple docstring"""


														_a     					=    max(0							,      optimization_step - self.update_after_step - 1 )

														if step <= 0:
																					return 0.0

														if self.use_ema_warmup:
																					_a     					=    1 - (1 + step / self.inv_gamma) ** -self.power
														else:
																					_a     					=    (1 + step) / (10 + step)

														_a     					=    min(lowercase_							,      self.decay )
														# make sure decay is not smaller than min_decay
														_a     					=    max(lowercase_							,      self.min_decay )
														return cur_decay_value







							@torch.no_grad()
							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Iterable[torch.nn.Parameter] )      ->							Optional[Any]:


														"""simple docstring"""


														if isinstance(lowercase_							,      torch.nn.Module ):
																					_a     					=    (
																					    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
																					    '''Please pass the parameters of the module instead.'''
																					)
																					deprecate(
																					    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''							,      '''1.0.0'''							,      lowercase_							,      standard_warn=lowercase_							,      )
																					_a     					=    parameters.parameters()

														_a     					=    list(lowercase_ )

														self.optimization_step += 1

														# Compute the decay factor for the exponential moving average.
														_a     					=    self.get_decay(self.optimization_step )
														_a     					=    decay
														_a     					=    1 - decay

														_a     					=    contextlib.nullcontext
														if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																					import deepspeed

														for s_param, param in zip(self.shadow_params							,      lowercase_ ):
																					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																												_a     					=    deepspeed.zero.GatheredParameters(lowercase_							,      modifier_rank=lowercase_ )

																					with context_manager():
																												if param.requires_grad:
																																			s_param.sub_(one_minus_decay * (s_param - param) )
																												else:
																																			s_param.copy_(lowercase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  Iterable[torch.nn.Parameter] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    list(lowercase_ )
														for s_param, param in zip(self.shadow_params							,      lowercase_ ):
																					param.data.copy_(s_param.to(param.device ).data )







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  int=None							,      lowerCAmelCase_	:  List[str]=None )      ->							str:


														"""simple docstring"""


														_a     					=    [
														    p.to(device=lowercase_							,      dtype=lowercase_ ) if p.is_floating_point() else p.to(device=lowercase_ )
														    for p in self.shadow_params
														]







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							List[str]:


														"""simple docstring"""


														return {
														    "decay": self.decay,
														    "min_decay": self.min_decay,
														    "optimization_step": self.optimization_step,
														    "update_after_step": self.update_after_step,
														    "use_ema_warmup": self.use_ema_warmup,
														    "inv_gamma": self.inv_gamma,
														    "power": self.power,
														    "shadow_params": self.shadow_params,
														}







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  Iterable[torch.nn.Parameter] )      ->							Dict:


														"""simple docstring"""


														_a     					=    [param.detach().cpu().clone() for param in parameters]







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  Iterable[torch.nn.Parameter] )      ->							List[Any]:


														"""simple docstring"""


														if self.temp_stored_params is None:
																					raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
														for c_param, param in zip(self.temp_stored_params							,      lowercase_ ):
																					param.data.copy_(c_param.data )

														# Better memory-wise.
														_a     					=    None







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  dict )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    copy.deepcopy(lowercase_ )

														_a     					=    state_dict.get('''decay'''							,      self.decay )
														if self.decay < 0.0 or self.decay > 1.0:
																					raise ValueError('''Decay must be between 0 and 1''' )

														_a     					=    state_dict.get('''min_decay'''							,      self.min_decay )
														if not isinstance(self.min_decay							,      lowercase_ ):
																					raise ValueError('''Invalid min_decay''' )

														_a     					=    state_dict.get('''optimization_step'''							,      self.optimization_step )
														if not isinstance(self.optimization_step							,      lowercase_ ):
																					raise ValueError('''Invalid optimization_step''' )

														_a     					=    state_dict.get('''update_after_step'''							,      self.update_after_step )
														if not isinstance(self.update_after_step							,      lowercase_ ):
																					raise ValueError('''Invalid update_after_step''' )

														_a     					=    state_dict.get('''use_ema_warmup'''							,      self.use_ema_warmup )
														if not isinstance(self.use_ema_warmup							,      lowercase_ ):
																					raise ValueError('''Invalid use_ema_warmup''' )

														_a     					=    state_dict.get('''inv_gamma'''							,      self.inv_gamma )
														if not isinstance(self.inv_gamma							,      (float, int) ):
																					raise ValueError('''Invalid inv_gamma''' )

														_a     					=    state_dict.get('''power'''							,      self.power )
														if not isinstance(self.power							,      (float, int) ):
																					raise ValueError('''Invalid power''' )

														_a     					=    state_dict.get('''shadow_params'''							,      lowercase_ )
														if shadow_params is not None:
																					_a     					=    shadow_params
																					if not isinstance(self.shadow_params							,      lowercase_ ):
																												raise ValueError('''shadow_params must be a list''' )
																					if not all(isinstance(lowercase_							,      torch.Tensor ) for p in self.shadow_params ):
																												raise ValueError('''shadow_params must all be Tensors''' )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case				:				int       							=		{'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[str]       							=		['YolosFeatureExtractor']
				_snake_case				:				Optional[int]       							=		['YolosImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Tuple       							=		[
				    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'YolosForObjectDetection',
				    'YolosModel',
				    'YolosPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_yolos import YolosFeatureExtractor
								from .image_processing_yolos import YolosImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_yolos import (
								    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    YolosForObjectDetection,
								    YolosModel,
								    YolosPreTrainedModel,
								)


else:
				import sys

				_snake_case				:				Dict       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

