

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	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_		:
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Iterable[torch.nn.Parameter]       ,SCREAMING_SNAKE_CASE__		:      float = 0.9999       ,SCREAMING_SNAKE_CASE__		:      float = 0.0       ,SCREAMING_SNAKE_CASE__		:      int = 0       ,SCREAMING_SNAKE_CASE__		:      bool = False       ,SCREAMING_SNAKE_CASE__		:      Union[float, int] = 1.0       ,SCREAMING_SNAKE_CASE__		:      Union[float, int] = 2 / 3       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = None       ,SCREAMING_SNAKE_CASE__		:      Dict[str, Any] = None       ,**SCREAMING_SNAKE_CASE__		:      str       ,):

								if isinstance(SCREAMING_SNAKE_CASE__       ,torch.nn.Module):
									__lowerCamelCase    :    str 	=			(
									    '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'       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__       ,)
									__lowerCamelCase    :    List[str] 	=			parameters.parameters()

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

								if kwargs.get('max_value'       ,SCREAMING_SNAKE_CASE__) is not None:
									__lowerCamelCase    :    List[str] 	=			'The `max_value` argument is deprecated. Please use `decay` instead.'
									deprecate('max_value'       ,'1.0.0'       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Any 	=			kwargs['max_value']

								if kwargs.get('min_value'       ,SCREAMING_SNAKE_CASE__) is not None:
									__lowerCamelCase    :    Any 	=			'The `min_value` argument is deprecated. Please use `min_decay` instead.'
									deprecate('min_value'       ,'1.0.0'       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			kwargs['min_value']

								__lowerCamelCase    :    Union[str, Any] 	=			list(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			[p.clone().detach() for p in parameters]

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

								__lowerCamelCase    :    List[str] 	=			None

								__lowerCamelCase    :    Dict 	=			decay
								__lowerCamelCase    :    List[str] 	=			min_decay
								__lowerCamelCase    :    int 	=			update_after_step
								__lowerCamelCase    :    str 	=			use_ema_warmup
								__lowerCamelCase    :    Optional[int] 	=			inv_gamma
								__lowerCamelCase    :    Any 	=			power
								__lowerCamelCase    :    Any 	=			0
								__lowerCamelCase    :    Optional[int] 	=			None  # set in `step()`

								__lowerCamelCase    :    Optional[Any] 	=			model_cls
								__lowerCamelCase    :    Union[str, Any] 	=			model_config
							@classmethod
							def    lowerCAmelCase					(		cls		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase	,			__lowerCamelCase    :    int 	=			model_cls.load_config(SCREAMING_SNAKE_CASE__       ,return_unused_kwargs=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			model_cls.from_pretrained(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Dict 	=			cls(model.parameters()       ,model_cls=SCREAMING_SNAKE_CASE__       ,model_config=model.config)

								ema_model.load_state_dict(SCREAMING_SNAKE_CASE__)
								return ema_model
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      str):
								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__.')

								__lowerCamelCase    :    str 	=			self.model_cls.from_config(self.model_config)
								__lowerCamelCase    :    int 	=			self.state_dict()
								state_dict.pop('shadow_params'       ,SCREAMING_SNAKE_CASE__)

								model.register_to_config(**SCREAMING_SNAKE_CASE__)
								self.copy_to(model.parameters())
								model.save_pretrained(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    List[Any] 	=			max(0       ,optimization_step - self.update_after_step - 1)

								if step <= 0:
									return 0.0

								if self.use_ema_warmup:
									__lowerCamelCase    :    List[str] 	=			1 - (1 + step / self.inv_gamma) ** -self.power
								else:
									__lowerCamelCase    :    List[Any] 	=			(1 + step) / (1_0 + step)

								__lowerCamelCase    :    Tuple 	=			min(SCREAMING_SNAKE_CASE__       ,self.decay)
								# make sure decay is not smaller than min_decay
								__lowerCamelCase    :    List[Any] 	=			max(SCREAMING_SNAKE_CASE__       ,self.min_decay)
								return cur_decay_value
							@torch.no_grad()
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Iterable[torch.nn.Parameter]):
								if isinstance(SCREAMING_SNAKE_CASE__       ,torch.nn.Module):
									__lowerCamelCase    :    Optional[int] 	=			(
									    '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'       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__       ,)
									__lowerCamelCase    :    Dict 	=			parameters.parameters()

								__lowerCamelCase    :    Union[str, Any] 	=			list(SCREAMING_SNAKE_CASE__)

								self.optimization_step += 1

								# Compute the decay factor for the exponential moving average.
								__lowerCamelCase    :    Dict 	=			self.get_decay(self.optimization_step)
								__lowerCamelCase    :    Any 	=			decay
								__lowerCamelCase    :    str 	=			1 - decay

								__lowerCamelCase    :    List[str] 	=			contextlib.nullcontext
								if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									import deepspeed

								for s_param, param in zip(self.shadow_params       ,SCREAMING_SNAKE_CASE__):
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
										__lowerCamelCase    :    List[Any] 	=			deepspeed.zero.GatheredParameters(SCREAMING_SNAKE_CASE__       ,modifier_rank=SCREAMING_SNAKE_CASE__)

									with context_manager():
										if param.requires_grad:
											s_param.sub_(one_minus_decay * (s_param - param))
										else:
											s_param.copy_(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Iterable[torch.nn.Parameter]):
								__lowerCamelCase    :    str 	=			list(SCREAMING_SNAKE_CASE__)
								for s_param, param in zip(self.shadow_params       ,SCREAMING_SNAKE_CASE__):
									param.data.copy_(s_param.to(param.device).data)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Dict=None       ,SCREAMING_SNAKE_CASE__		:      List[Any]=None):
								__lowerCamelCase    :    str 	=			[
								    p.to(device=SCREAMING_SNAKE_CASE__       ,dtype=SCREAMING_SNAKE_CASE__) if p.is_floating_point() else p.to(device=SCREAMING_SNAKE_CASE__)
								    for p in self.shadow_params
								]
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								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		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Iterable[torch.nn.Parameter]):
								__lowerCamelCase    :    Tuple 	=			[param.detach().cpu().clone() for param in parameters]
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Iterable[torch.nn.Parameter]):
								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       ,SCREAMING_SNAKE_CASE__):
									param.data.copy_(c_param.data)

								# Better memory-wise.
								__lowerCamelCase    :    Optional[int] 	=			None


							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      dict):
								__lowerCamelCase    :    List[str] 	=			copy.deepcopy(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    int 	=			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')

								__lowerCamelCase    :    List[Any] 	=			state_dict.get('min_decay'       ,self.min_decay)
								if not isinstance(self.min_decay       ,SCREAMING_SNAKE_CASE__):
									raise ValueError('Invalid min_decay')

								__lowerCamelCase    :    List[Any] 	=			state_dict.get('optimization_step'       ,self.optimization_step)
								if not isinstance(self.optimization_step       ,SCREAMING_SNAKE_CASE__):
									raise ValueError('Invalid optimization_step')

								__lowerCamelCase    :    Dict 	=			state_dict.get('update_after_step'       ,self.update_after_step)
								if not isinstance(self.update_after_step       ,SCREAMING_SNAKE_CASE__):
									raise ValueError('Invalid update_after_step')

								__lowerCamelCase    :    Optional[int] 	=			state_dict.get('use_ema_warmup'       ,self.use_ema_warmup)
								if not isinstance(self.use_ema_warmup       ,SCREAMING_SNAKE_CASE__):
									raise ValueError('Invalid use_ema_warmup')

								__lowerCamelCase    :    Optional[Any] 	=			state_dict.get('inv_gamma'       ,self.inv_gamma)
								if not isinstance(self.inv_gamma       ,(float, int)):
									raise ValueError('Invalid inv_gamma')

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

								__lowerCamelCase    :    Optional[int] 	=			state_dict.get('shadow_params'       ,SCREAMING_SNAKE_CASE__)
								if shadow_params is not None:
									__lowerCamelCase    :    Union[str, Any] 	=			shadow_params
									if not isinstance(self.shadow_params       ,SCREAMING_SNAKE_CASE__):
										raise ValueError('shadow_params must be a list')
									if not all(isinstance(SCREAMING_SNAKE_CASE__       ,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_torch_available, is_vision_available


a		={
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Mask2FormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Mask2FormerForUniversalSegmentation""",
					    """Mask2FormerModel""",
					    """Mask2FormerPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)




