




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
							from fairscale.optim import OSS


lowerCamelCase	:	List[str]				=   logging.get_logger(__name__)

lowerCamelCase	:	Tuple				=   {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}




class 						__lowercase		(UpperCamelCase__							):




						"""simple docstring"""





						def __init__(				self    ,				A=None    ,				A=None    ,				*A    ,				**A     )		->    Union[str, Any]:
									super().__init__(*A    ,				**A     )

									if config is None:
												assert isinstance(self.model    ,				A     ), (
												    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
												    f""" {self.model.__class__}"""
												)
												snake_case				:    Optional[int]           =	self.model.config
									else:
												snake_case				:    Dict           =	config

									snake_case				:    Tuple           =	data_args
									snake_case				:    str           =	self.config.tgt_vocab_size if isinstance(self.config    ,				A     ) else self.config.vocab_size

									if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
												assert self.config.pad_token_id is not None, (
												    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
												    " calculation or doing label smoothing."
												)

									if self.config.pad_token_id is None and self.config.eos_token_id is not None:
												logger.warning(
												    f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
												    """ padding.."""     )

									if self.args.label_smoothing == 0:
												snake_case				:    Optional[Any]           =	torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id     )
									else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												snake_case				:    Dict           =	label_smoothed_nll_loss

						def      UpperCAmelCase				(				self    ,				A     )		->    Tuple:
									if self.optimizer is None:
												snake_case				:    str           =	["""bias""", """LayerNorm.weight"""]
												snake_case				:    int           =	[
												    {
												        """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay     )],
												        """weight_decay""": self.args.weight_decay,
												    },
												    {
												        """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay     )],
												        """weight_decay""": 0.0,
												    },
												]
												snake_case				:    Any           =	Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
															snake_case				:    int           =	Adafactor
															snake_case				:    str           =	{"""scale_parameter""": False, """relative_step""": False}
												else:
															snake_case				:    List[str]           =	AdamW
															snake_case				:    List[str]           =	{
															    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
															    """eps""": self.args.adam_epsilon,
															}
												snake_case				:    Tuple           =	self.args.learning_rate
												if self.sharded_ddp:
															snake_case				:    Optional[Any]           =	OSS(
															    params=A    ,				optim=A    ,				**A    ,				)
												else:
															snake_case				:    Tuple           =	optimizer_cls(A    ,				**A     )

									if self.lr_scheduler is None:
												snake_case				:    Optional[Any]           =	self._get_lr_scheduler(A     )
									else:  # ignoring --lr_scheduler
												logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""     )

						def      UpperCAmelCase				(				self    ,				A     )		->    Optional[int]:
									snake_case				:    str           =	arg_to_scheduler[self.args.lr_scheduler]
									if self.args.lr_scheduler == "constant":
												snake_case				:    Optional[Any]           =	schedule_func(self.optimizer     )
									elif self.args.lr_scheduler == "constant_w_warmup":
												snake_case				:    int           =	schedule_func(self.optimizer    ,				num_warmup_steps=self.args.warmup_steps     )
									else:
												snake_case				:    Tuple           =	schedule_func(
												    self.optimizer    ,				num_warmup_steps=self.args.warmup_steps    ,				num_training_steps=A     )
									return scheduler

						def      UpperCAmelCase				(				self     )		->    Optional[torch.utils.data.Sampler]:
									if isinstance(self.train_dataset    ,				torch.utils.data.IterableDataset     ):
												return None
									elif is_torch_tpu_available():
												return get_tpu_sampler(self.train_dataset     )
									else:
												if self.args.sortish_sampler:
															self.train_dataset.make_sortish_sampler(
															    self.args.per_device_train_batch_size    ,				distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)    ,				)

												return (
												    RandomSampler(self.train_dataset     )
												    if self.args.local_rank == -1
												    else DistributedSampler(self.train_dataset     )
												)

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A     )		->    List[str]:
									if self.args.label_smoothing == 0:
												if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
															# force training to ignore pad token
															snake_case				:    str           =	model(**A    ,				use_cache=A     )[0]
															snake_case				:    List[str]           =	self.loss_fn(logits.view(-1    ,				logits.shape[-1]     )    ,				labels.view(-1     )     )
												else:
															# compute usual loss via models
															snake_case				:    Tuple           =	model(**A    ,				labels=A    ,				use_cache=A     )[:2]
									else:
												# compute label smoothed loss
												snake_case				:    Dict           =	model(**A    ,				use_cache=A     )[0]
												snake_case				:    List[str]           =	torch.nn.functional.log_softmax(A    ,				dim=-1     )
												snake_case				:    Optional[Any]           =	self.loss_fn(A    ,				A    ,				self.args.label_smoothing    ,				ignore_index=self.config.pad_token_id     )
									return loss, logits

						def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    Union[str, Any]:
									snake_case				:    List[str]           =	inputs.pop("""labels"""     )
									snake_case				:    Any           =	self._compute_loss(A    ,				A    ,				A     )
									return loss

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A = None    ,				)		->    Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
									snake_case				:    Optional[int]           =	self._prepare_inputs(A     )

									snake_case				:    Dict           =	{
									    """max_length""": self.data_args.val_max_target_length
									    if self.data_args is not None
									    else self.config.max_length,
									    """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
									}

									if self.args.predict_with_generate and not self.args.prediction_loss_only:
												snake_case				:    List[str]           =	self.model.generate(
												    inputs["""input_ids"""]    ,				attention_mask=inputs["""attention_mask"""]    ,				**A    ,				)
												# in case the batch is shorter than max length, the output should be padded
												if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
															snake_case				:    List[str]           =	self._pad_tensors_to_max_len(A    ,				gen_kwargs["""max_length"""]     )

									snake_case				:    Union[str, Any]           =	inputs.pop("""labels"""     )
									with torch.no_grad():
												# compute loss on predict data
												snake_case				:    int           =	self._compute_loss(A    ,				A    ,				A     )

									snake_case				:    Union[str, Any]           =	loss.mean().detach()
									if self.args.prediction_loss_only:
												return (loss, None, None)

									snake_case				:    Dict           =	generated_tokens if self.args.predict_with_generate else logits

									if labels.shape[-1] < gen_kwargs["max_length"]:
												snake_case				:    Optional[int]           =	self._pad_tensors_to_max_len(A    ,				gen_kwargs["""max_length"""]     )

									return (loss, logits, labels)

						def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    List[str]:
									# If PAD token is not defined at least EOS token has to be defined
									snake_case				:    Tuple           =	self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

									if pad_token_id is None:
												raise ValueError(
												    """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
												    f""" padded to `max_length`={max_length}"""     )

									snake_case				:    Dict           =	pad_token_id * torch.ones(
									    (tensor.shape[0], max_length)    ,				dtype=tensor.dtype    ,				device=tensor.device     )
									snake_case				:    Tuple           =	tensor
									return padded_tensor











def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   return int((input_a, input_a).count(1					) != 0					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       None:
   assert or_gate(0						,0					) == 0
   assert or_gate(0						,1					) == 1
   assert or_gate(1						,0					) == 1
   assert or_gate(1						,1					) == 1


if __name__ == "__main__":
       print(or_gate(0, 1))
       print(or_gate(1, 0))
       print(or_gate(0, 0))
       print(or_gate(1, 1))





