


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  UpperCamelCase					(	__magic_name__     :							int     )   ->  List[str]:
		"""simple docstring"""




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







class 							A    :
					'''simple docstring'''






					def __init__(self    :						str      ,					_UpperCAmelCase    :						Iterable[torch.nn.Parameter]      ,					_UpperCAmelCase    :						float = 0.9_999      ,					_UpperCAmelCase    :						float = 0.0      ,					_UpperCAmelCase    :						int = 0      ,					_UpperCAmelCase    :						bool = False      ,					_UpperCAmelCase    :						Union[float, int] = 1.0      ,					_UpperCAmelCase    :						Union[float, int] = 2 / 3      ,					_UpperCAmelCase    :						Optional[Any] = None      ,					_UpperCAmelCase    :						Dict[str, Any] = None      ,					**_UpperCAmelCase    :						str      ,					)						->   str:




							"""simple docstring"""


							if isinstance(lowercase_      ,					torch.nn.Module		):
									lowercase__     			=		(
									    "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_      ,					)
									lowercase__     			=		parameters.parameters()

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

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

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

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

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

							lowercase__     			=		None

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

							lowercase__     			=		model_cls
							lowercase__     			=		model_config

					@classmethod
					def   lowerCamelCase__     (cls    :						Any      ,					_UpperCAmelCase    :						str      ,					_UpperCAmelCase    :						List[Any]		)						->   "EMAModel":




							"""simple docstring"""


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

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

							ema_model.load_state_dict(lowercase_		)
							return ema_model

					def   lowerCamelCase__     (self    :						int      ,					_UpperCAmelCase    :						List[str]		)						->   Tuple:




							"""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__."""		)

							lowercase__     			=		self.model_cls.from_config(self.model_config		)
							lowercase__     			=		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    :						Any      ,					_UpperCAmelCase    :						int		)						->   float:




							"""simple docstring"""


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

							if step <= 0:
									return 0.0

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

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

					@torch.no_grad()
					def   lowerCamelCase__     (self    :						List[str]      ,					_UpperCAmelCase    :						Iterable[torch.nn.Parameter]		)						->   int:




							"""simple docstring"""


							if isinstance(lowercase_      ,					torch.nn.Module		):
									lowercase__     			=		(
									    "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_      ,					)
									lowercase__     			=		parameters.parameters()

							lowercase__     			=		list(lowercase_		)

							self.optimization_step += 1

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

							lowercase__     			=		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():
											lowercase__     			=		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    :						Dict      ,					_UpperCAmelCase    :						Iterable[torch.nn.Parameter]		)						->   None:




							"""simple docstring"""


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

					def   lowerCamelCase__     (self    :						List[Any]      ,					_UpperCAmelCase    :						List[str]=None      ,					_UpperCAmelCase    :						List[Any]=None		)						->   None:




							"""simple docstring"""


							lowercase__     			=		[
							    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    :						Dict		)						->   dict:




							"""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    :						Tuple      ,					_UpperCAmelCase    :						Iterable[torch.nn.Parameter]		)						->   None:




							"""simple docstring"""


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

					def   lowerCamelCase__     (self    :						str      ,					_UpperCAmelCase    :						Iterable[torch.nn.Parameter]		)						->   None:




							"""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.
							lowercase__     			=		None


					def   lowerCamelCase__     (self    :						List[Any]      ,					_UpperCAmelCase    :						dict		)						->   None:




							"""simple docstring"""


							lowercase__     			=		copy.deepcopy(lowercase_		)

							lowercase__     			=		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"""		)

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

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

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

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

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

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

							lowercase__     			=		state_dict.get("""shadow_params"""      ,					lowercase_		)
							if shadow_params is not None:
									lowercase__     			=		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"""		)


from typing import TYPE_CHECKING

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


UpperCamelCase  							=						{
    '''configuration_groupvit''': [
        '''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''GroupViTConfig''',
        '''GroupViTOnnxConfig''',
        '''GroupViTTextConfig''',
        '''GroupViTVisionConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''GroupViTModel''',
          '''GroupViTPreTrainedModel''',
          '''GroupViTTextModel''',
          '''GroupViTVisionModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFGroupViTModel''',
          '''TFGroupViTPreTrainedModel''',
          '''TFGroupViTTextModel''',
          '''TFGroupViTVisionModel''',
      ]

if TYPE_CHECKING:
      from .configuration_groupvit import (
          GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          GroupViTConfig,
          GroupViTOnnxConfig,
          GroupViTTextConfig,
          GroupViTVisionConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_groupvit import (
                GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                GroupViTModel,
                GroupViTPreTrainedModel,
                GroupViTTextModel,
                GroupViTVisionModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_groupvit import (
                TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGroupViTModel,
                TFGroupViTPreTrainedModel,
                TFGroupViTTextModel,
                TFGroupViTVisionModel,
            )

else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



