






"""simple docstring"""



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






class 					_UpperCAmelCase       :




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

							if isinstance(A    ,						torch.nn.Module   ):
									lowercase_ : List[Any]							       =   (
									    '''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'''    ,						A    ,						standard_warn=A    ,						)
									lowercase_ : int							       =   parameters.parameters()

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

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

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

							lowercase_ : Union[str, Any]							       =   list(A   )
							lowercase_ : int							       =   [p.clone().detach() for p in parameters]

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

							lowercase_ : Optional[Any]							       =   None

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

							lowercase_ : List[str]							       =   model_cls
							lowercase_ : int							       =   model_config




					@classmethod
					def   A  (				cls     :						int    ,						A     :						int    ,						A     :						Optional[int]   )  ->  "EMAModel":
							lowercase_       ,					lowercase_ : List[str]							       =   model_cls.load_config(A    ,						return_unused_kwargs=A   )
							lowercase_ : Optional[int]							       =   model_cls.from_pretrained(A   )

							lowercase_ : List[str]							       =   cls(model.parameters()    ,						model_cls=A    ,						model_config=model.config   )

							ema_model.load_state_dict(A   )
							return ema_model




					def   A  (				self     :						Optional[Any]    ,						A     :						List[str]   )  ->  Any:
							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_ : Union[str, Any]							       =   self.model_cls.from_config(self.model_config   )
							lowercase_ : Dict							       =   self.state_dict()
							state_dict.pop('''shadow_params'''    ,						A   )

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




					def   A  (				self     :						str    ,						A     :						int   )  ->  float:
							lowercase_ : Any							       =   max(0    ,						optimization_step - self.update_after_step - 1   )

							if step <= 0:
									return 0.0

							if self.use_ema_warmup:
									lowercase_ : Any							       =   1 - (1 + step / self.inv_gamma) ** -self.power
							else:
									lowercase_ : Optional[Any]							       =   (1 + step) / (10 + step)

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




					@torch.no_grad()
					def   A  (				self     :						Optional[int]    ,						A     :						Iterable[torch.nn.Parameter]   )  ->  Dict:
							if isinstance(A    ,						torch.nn.Module   ):
									lowercase_ : str							       =   (
									    '''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'''    ,						A    ,						standard_warn=A    ,						)
									lowercase_ : Optional[int]							       =   parameters.parameters()

							lowercase_ : List[Any]							       =   list(A   )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							lowercase_ : Union[str, Any]							       =   self.get_decay(self.optimization_step   )
							lowercase_ : Tuple							       =   decay
							lowercase_ : Optional[int]							       =   1 - decay

							lowercase_ : Optional[Any]							       =   contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									import deepspeed

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

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




					def   A  (				self     :						Tuple    ,						A     :						Iterable[torch.nn.Parameter]   )  ->  None:
							lowercase_ : int							       =   list(A   )
							for s_param, param in zip(self.shadow_params    ,						A   ):
									param.data.copy_(s_param.to(param.device   ).data   )




					def   A  (				self     :						Any    ,						A     :						Optional[int]=None    ,						A     :						Optional[int]=None   )  ->  None:
							lowercase_ : Union[str, Any]							       =   [
							    p.to(device=A    ,						dtype=A   ) if p.is_floating_point() else p.to(device=A   )
							    for p in self.shadow_params
							]




					def   A  (				self     :						Union[str, Any]   )  ->  dict:
							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   A  (				self     :						Tuple    ,						A     :						Iterable[torch.nn.Parameter]   )  ->  None:
							lowercase_ : Tuple							       =   [param.detach().cpu().clone() for param in parameters]




					def   A  (				self     :						Union[str, Any]    ,						A     :						Iterable[torch.nn.Parameter]   )  ->  None:
							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    ,						A   ):
									param.data.copy_(c_param.data   )

							# Better memory-wise.
							lowercase_ : int							       =   None






					def   A  (				self     :						Tuple    ,						A     :						dict   )  ->  None:
							lowercase_ : Dict							       =   copy.deepcopy(A   )

							lowercase_ : List[Any]							       =   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_ : Union[str, Any]							       =   state_dict.get('''min_decay'''    ,						self.min_decay   )
							if not isinstance(self.min_decay    ,						A   ):
									raise ValueError('''Invalid min_decay'''   )

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

							lowercase_ : Union[str, Any]							       =   state_dict.get('''update_after_step'''    ,						self.update_after_step   )
							if not isinstance(self.update_after_step    ,						A   ):
									raise ValueError('''Invalid update_after_step'''   )

							lowercase_ : List[str]							       =   state_dict.get('''use_ema_warmup'''    ,						self.use_ema_warmup   )
							if not isinstance(self.use_ema_warmup    ,						A   ):
									raise ValueError('''Invalid use_ema_warmup'''   )

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

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

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











"""simple docstring"""



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import torch





class 					_UpperCAmelCase       ( TensorFormatter[Mapping, "torch.Tensor", Mapping]							):




					def __init__(				self     :						Any    ,						A     :						int=None    ,						**A     :						str   )  ->  Union[str, Any]:
							super().__init__(features=A   )
							lowercase_ : Union[str, Any]							       =   torch_tensor_kwargs
							import torch  # noqa import torch at initialization




					def   A  (				self     :						Dict    ,						A     :						int   )  ->  List[Any]:
							import torch

							if isinstance(A    ,						A   ) and column:
									if all(
									    isinstance(A    ,						torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column   ):
											return torch.stack(A   )
							return column




					def   A  (				self     :						int    ,						A     :						Any   )  ->  Optional[Any]:
							import torch

							if isinstance(A    ,						(str, bytes, type(A   ))   ):
									return value
							elif isinstance(A    ,						(np.character, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.character   ):
									return value.tolist()

							lowercase_ : Any							       =   {}

							if isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.integer   ):
									lowercase_ : Any							       =   {'''dtype''': torch.intaa}
							elif isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.floating   ):
									lowercase_ : Dict							       =   {'''dtype''': torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(A    ,						PIL.Image.Image   ):
											lowercase_ : Dict							       =   np.asarray(A   )
							return torch.tensor(A    ,						**{**default_dtype, **self.torch_tensor_kwargs}   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[int]   )  ->  str:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(A    ,						'''__array__'''   ) and not isinstance(A    ,						torch.Tensor   ):
									lowercase_ : Optional[int]							       =   data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(A    ,						np.ndarray   ):
									if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
											return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							elif isinstance(A    ,						(list, tuple)   ):
									return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							return self._tensorize(A   )




					def   A  (				self     :						Dict    ,						A     :						dict   )  ->  Tuple:
							return map_nested(self._recursive_tensorize    ,						A    ,						map_list=A   )




					def   A  (				self     :						str    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[Any]							       =   self.numpy_arrow_extractor().extract_row(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_row(A   )
							return self.recursive_tensorize(A   )




					def   A  (				self     :						List[Any]    ,						A     :						pa.Table   )  ->  "torch.Tensor":
							lowercase_ : List[str]							       =   self.numpy_arrow_extractor().extract_column(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_column(A    ,						pa_table.column_names[0]   )
							lowercase_ : Optional[int]							       =   self.recursive_tensorize(A   )
							lowercase_ : Any							       =   self._consolidate(A   )
							return column






					def   A  (				self     :						List[str]    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[int]							       =   self.numpy_arrow_extractor().extract_batch(A   )
							lowercase_ : int							       =   self.python_features_decoder.decode_batch(A   )
							lowercase_ : Dict							       =   self.recursive_tensorize(A   )
							for column_name in batch:
									lowercase_ : Optional[Any]							       =   self._consolidate(batch[column_name]   )
							return batch



