

'''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 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Optional[Any]:
						random.seed(__magic_name__						)
						np.random.seed(__magic_name__						)
						torch.manual_seed(__magic_name__						)
						torch.cuda.manual_seed_all(__magic_name__						)
						# ^^ safe to call this function even if cuda is not available







class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]		,					UpperCAmelCase__     :		float = 0.99_99		,					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__     :		Dict		,					):
										'''simple docstring'''

										if isinstance(UpperCAmelCase__		,					torch.nn.Module   ):
																lowercase :							Tuple    		=(
																    '''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'''		,					UpperCAmelCase__		,					standard_warn=UpperCAmelCase__		,					)
																lowercase :							Optional[int]    		=parameters.parameters()

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

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

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

										lowercase :							Any    		=list(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=[p.clone().detach() for p in parameters]

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

										lowercase :							Dict    		=None

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

										lowercase :							Union[str, Any]    		=model_cls
										lowercase :							Dict    		=model_config







				@classmethod
				def  lowerCamelCase_	(       cls     :		Optional[Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Dict    		=model_cls.load_config(UpperCAmelCase__		,					return_unused_kwargs=UpperCAmelCase__   )
										lowercase :							int    		=model_cls.from_pretrained(UpperCAmelCase__   )

										lowercase :							int    		=cls(model.parameters()		,					model_cls=UpperCAmelCase__		,					model_config=model.config   )

										ema_model.load_state_dict(UpperCAmelCase__   )
										return ema_model







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''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 :							List[str]    		=self.model_cls.from_config(self.model_config   )
										lowercase :							Tuple    		=self.state_dict()
										state_dict.pop('''shadow_params'''		,					UpperCAmelCase__   )

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







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[str]    		=max(0		,					optimization_step - self.update_after_step - 1   )

										if step <= 0:
																return 0.0

										if self.use_ema_warmup:
																lowercase :							List[str]    		=1 - (1 + step / self.inv_gamma) ** -self.power
										else:
																lowercase :							List[str]    		=(1 + step) / (10 + step)

										lowercase :							Union[str, Any]    		=min(UpperCAmelCase__		,					self.decay   )
										# make sure decay is not smaller than min_decay
										lowercase :							Any    		=max(UpperCAmelCase__		,					self.min_decay   )
										return cur_decay_value







				@torch.no_grad()
				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]   ):
										'''simple docstring'''
										if isinstance(UpperCAmelCase__		,					torch.nn.Module   ):
																lowercase :							List[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'''		,					UpperCAmelCase__		,					standard_warn=UpperCAmelCase__		,					)
																lowercase :							Union[str, Any]    		=parameters.parameters()

										lowercase :							str    		=list(UpperCAmelCase__   )

										self.optimization_step += 1

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

										lowercase :							Union[str, 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		,					UpperCAmelCase__   ):
																if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																						lowercase :							Tuple    		=deepspeed.zero.GatheredParameters(UpperCAmelCase__		,					modifier_rank=UpperCAmelCase__   )

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







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=list(UpperCAmelCase__   )
										for s_param, param in zip(self.shadow_params		,					UpperCAmelCase__   ):
																param.data.copy_(s_param.to(param.device   ).data   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Tuple=None		,					UpperCAmelCase__     :		List[str]=None   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=[
										    p.to(device=UpperCAmelCase__		,					dtype=UpperCAmelCase__   ) if p.is_floating_point() else p.to(device=UpperCAmelCase__   )
										    for p in self.shadow_params
										]







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''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]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=[param.detach().cpu().clone() for param in parameters]







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]   ):
										'''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		,					UpperCAmelCase__   ):
																param.data.copy_(c_param.data   )

										# Better memory-wise.
										lowercase :							str    		=None







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		dict   ):
										'''simple docstring'''
										lowercase :							Tuple    		=copy.deepcopy(UpperCAmelCase__   )

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

										lowercase :							Optional[Any]    		=state_dict.get('''optimization_step'''		,					self.optimization_step   )
										if not isinstance(self.optimization_step		,					UpperCAmelCase__   ):
																raise ValueError('''Invalid optimization_step'''   )

										lowercase :							int    		=state_dict.get('''update_after_step'''		,					self.update_after_step   )
										if not isinstance(self.update_after_step		,					UpperCAmelCase__   ):
																raise ValueError('''Invalid update_after_step'''   )

										lowercase :							Dict    		=state_dict.get('''use_ema_warmup'''		,					self.use_ema_warmup   )
										if not isinstance(self.use_ema_warmup		,					UpperCAmelCase__   ):
																raise ValueError('''Invalid use_ema_warmup'''   )

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

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

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








'''simple docstring'''





import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Dict:
						lowercase :							List[str]    		=R'''\w+[.]\d+'''
						lowercase :							List[str]    		=re.findall(__magic_name__   , __magic_name__						)
						for pat in pats:
												lowercase :							Optional[int]    		=key.replace(__magic_name__   , '''_'''.join(pat.split('''.'''						)						)						)
						return key






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Dict						)				->  str:
						lowercase :							Dict    		=pt_tuple_key[:-1] + ('''scale''',)
						if (
						    any('''norm''' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												lowercase :							str    		=pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												lowercase :							str    		=pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												lowercase :							Dict    		=pt_tuple_key[:-1] + ('''embedding''',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowercase :							Tuple    		=pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												lowercase :							Tuple    		=pt_tensor.transpose(2   , 3   , 1   , 0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowercase :							str    		=pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight":
												lowercase :							Optional[Any]    		=pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowercase :							Dict    		=pt_tuple_key[:-1] + ('''weight''',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowercase :							Union[str, Any]    		=pt_tuple_key[:-1] + ('''bias''',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor







def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Union[str, Any]=42						)				->  List[str]:
						# Step 1: Convert pytorch tensor to numpy
						lowercase :							Optional[Any]    		={k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						lowercase :							str    		=flax_model.init_weights(PRNGKey(__magic_name__						)						)

						lowercase :							Dict    		=flatten_dict(__magic_name__						)
						lowercase :							Dict    		={}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowercase :							Dict    		=rename_key(__magic_name__						)
												lowercase :							Optional[int]    		=tuple(renamed_pt_key.split('''.'''						)						)

												# Correctly rename weight parameters
												lowercase		,	lowercase :							Any    		=rename_key_and_reshape_tensor(__magic_name__   , __magic_name__   , __magic_name__						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # also add unexpected weight so that warning is thrown
												lowercase :							Tuple    		=jnp.asarray(__magic_name__						)

						return unflatten_dict(__magic_name__						)





