





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   (      _SCREAMING_SNAKE_CASE	) ->   List[Any]:


      '''simple docstring'''


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



class 		UpperCamelCase__ :



     '''simple docstring'''

     def __init__(							self						:    int						,lowerCamelCase__						:    Iterable[torch.nn.Parameter]						,lowerCamelCase__						:    float = 0.9999						,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__						:    str						,)     ->		List[Any]:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,torch.nn.Module       ):
                 SCREAMING_SNAKE_CASE		=						(
                     """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"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						parameters.parameters()

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

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

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

           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						[p.clone().detach() for p in parameters]

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

           SCREAMING_SNAKE_CASE		=						None

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

           SCREAMING_SNAKE_CASE		=						model_cls
           SCREAMING_SNAKE_CASE		=						model_config






     @classmethod
     def 				SCREAMING_SNAKE_CASE__  (							cls						:    Any						,lowerCamelCase__						:    Tuple						,lowerCamelCase__						:    Optional[Any]       )     ->		"EMAModel":




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						model_cls.load_config(lowerCamelCase__						,return_unused_kwargs=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						model_cls.from_pretrained(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						cls(model.parameters()						,model_cls=lowerCamelCase__						,model_config=model.config       )

           ema_model.load_state_dict(lowerCamelCase__       )
           return ema_model






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




           '''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__."""       )

           SCREAMING_SNAKE_CASE		=						self.model_cls.from_config(self.model_config       )
           SCREAMING_SNAKE_CASE		=						self.state_dict()
           state_dict.pop("""shadow_params"""						,lowerCamelCase__       )

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    int       )     ->		float:




           '''simple docstring'''


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

           if step <= 0:
                 return 0.0

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

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






     @torch.no_grad()
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]						,lowerCamelCase__						:    Iterable[torch.nn.Parameter]       )     ->		str:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,torch.nn.Module       ):
                 SCREAMING_SNAKE_CASE		=						(
                     """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"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						parameters.parameters()

           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )

           self.optimization_step += 1

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

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

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

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






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




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    Optional[int]=None						,lowerCamelCase__						:    str=None       )     ->		None:




           '''simple docstring'''


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






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




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    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						,lowerCamelCase__       ):
                 param.data.copy_(c_param.data       )

           # Better memory-wise.
           SCREAMING_SNAKE_CASE		=						None






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    dict       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						copy.deepcopy(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						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"""       )

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

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

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

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

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

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

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









from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    Callable						,lowerCamelCase__						:    Optional[Features] = None						,lowerCamelCase__						:    str = None						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    Optional[dict] = None						,lowerCamelCase__						:    Optional[int] = None						,**lowerCamelCase__						:    Optional[Any]						,)     ->		List[str]:




           '''simple docstring'''


           super().__init__(
               features=lowerCamelCase__						,cache_dir=lowerCamelCase__						,keep_in_memory=lowerCamelCase__						,streaming=lowerCamelCase__						,num_proc=lowerCamelCase__						,**lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						Generator(
               cache_dir=lowerCamelCase__						,features=lowerCamelCase__						,generator=lowerCamelCase__						,gen_kwargs=lowerCamelCase__						,**lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           if self.streaming:
                 SCREAMING_SNAKE_CASE		=						self.builder.as_streaming_dataset(split="""train"""       )
           # Build regular (map-style) dataset
           else:
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None

                 self.builder.download_and_prepare(
                     download_config=lowerCamelCase__						,download_mode=lowerCamelCase__						,verification_mode=lowerCamelCase__						,base_path=lowerCamelCase__						,num_proc=self.num_proc						,)
                 SCREAMING_SNAKE_CASE		=						self.builder.as_dataset(
                     split="""train"""						,verification_mode=lowerCamelCase__						,in_memory=self.keep_in_memory       )
           return dataset


