


'''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 							__magic_name__(						lowerCamelCase):
						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       ,			__lowercase       ,			__lowercase = 0.9_9_9_9       ,			__lowercase = 0.0       ,			__lowercase = 0       ,			__lowercase = False       ,			__lowercase = 1.0       ,			__lowercase = 2 / 3       ,			__lowercase = None       ,			__lowercase = None       ,			**__lowercase       ,			):

										if isinstance(__lowercase       ,			torch.nn.Module		):
																__lowerCAmelCase							       =				(
																    '''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       ,			)
																__lowerCAmelCase							       =				parameters.parameters()

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

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

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

										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				[p.clone().detach() for p in parameters]

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

										__lowerCAmelCase							       =				None

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

										__lowerCAmelCase							       =				model_cls
										__lowerCAmelCase							       =				model_config




				@classmethod
				def 					_snake_case  (cls       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				model_cls.load_config(__lowercase       ,			return_unused_kwargs=__lowercase		)
										__lowerCAmelCase							       =				model_cls.from_pretrained(__lowercase		)

										__lowerCAmelCase							       =				cls(model.parameters()       ,			model_cls=__lowercase       ,			model_config=model.config		)

										ema_model.load_state_dict(__lowercase		)
										return ema_model




				def 					_snake_case  (self       ,			__lowercase		):
										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							       =				self.model_cls.from_config(self.model_config		)
										__lowerCAmelCase							       =				self.state_dict()
										state_dict.pop('''shadow_params'''       ,			__lowercase		)

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




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				max(0       ,			optimization_step - self.update_after_step - 1		)

										if step <= 0:
																return 0.0

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

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




				@torch.no_grad()
				def 					_snake_case  (self       ,			__lowercase		):
										if isinstance(__lowercase       ,			torch.nn.Module		):
																__lowerCAmelCase							       =				(
																    '''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       ,			)
																__lowerCAmelCase							       =				parameters.parameters()

										__lowerCAmelCase							       =				list(__lowercase		)

										self.optimization_step += 1

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

										__lowerCAmelCase							       =				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():
																						__lowerCAmelCase							       =				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 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				list(__lowercase		)
										for s_param, param in zip(self.shadow_params       ,			__lowercase		):
																param.data.copy_(s_param.to(param.device		).data		)




				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				[
										    p.to(device=__lowercase       ,			dtype=__lowercase		) if p.is_floating_point() else p.to(device=__lowercase		)
										    for p in self.shadow_params
										]




				def 					_snake_case  (self		):
										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 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[param.detach().cpu().clone() for param in parameters]




				def 					_snake_case  (self       ,			__lowercase		):
										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.
										__lowerCAmelCase							       =				None




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				copy.deepcopy(__lowercase		)

										__lowerCAmelCase							       =				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							       =				state_dict.get('''min_decay'''       ,			self.min_decay		)
										if not isinstance(self.min_decay       ,			__lowercase		):
																raise ValueError('''Invalid min_decay'''		)

										__lowerCAmelCase							       =				state_dict.get('''optimization_step'''       ,			self.optimization_step		)
										if not isinstance(self.optimization_step       ,			__lowercase		):
																raise ValueError('''Invalid optimization_step'''		)

										__lowerCAmelCase							       =				state_dict.get('''update_after_step'''       ,			self.update_after_step		)
										if not isinstance(self.update_after_step       ,			__lowercase		):
																raise ValueError('''Invalid update_after_step'''		)

										__lowerCAmelCase							       =				state_dict.get('''use_ema_warmup'''       ,			self.use_ema_warmup		)
										if not isinstance(self.use_ema_warmup       ,			__lowercase		):
																raise ValueError('''Invalid use_ema_warmup'''		)

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

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

										__lowerCAmelCase							       =				state_dict.get('''shadow_params'''       ,			__lowercase		)
										if shadow_params is not None:
																__lowerCAmelCase							       =				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'''		)







'''simple docstring'''

import numpy as np

from transformers import Pipeline

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				np.max(lowerCamelCase,				axis=-1,				keepdims=lowerCamelCase)
						__lowerCAmelCase							       =				np.exp(outputs - maxes)
						return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=lowerCamelCase)




class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{}
										if "second_text" in kwargs:
																__lowerCAmelCase							       =				kwargs['''second_text''']
										return preprocess_kwargs, {}, {}




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										return self.tokenizer(__lowercase       ,			text_pair=__lowercase       ,			return_tensors=self.framework		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.model(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				model_outputs.logits[0].numpy()
										__lowerCAmelCase							       =				softmax(__lowercase		)

										__lowerCAmelCase							       =				np.argmax(__lowercase		)
										__lowerCAmelCase							       =				self.model.config.idalabel[best_class]
										__lowerCAmelCase							       =				probabilities[best_class].item()
										__lowerCAmelCase							       =				logits.tolist()
										return {"label": label, "score": score, "logits": logits}

