






"""simple docstring"""
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         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "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      SCREAMING_SNAKE_CASE  (				_UpperCAmelCase						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		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__}"""
			)
			A_		:						Optional[Any]	    =   self.model.config
		else:
			A_		:						Tuple	    =   config

		A_		:						str	    =   data_args
		A_		:						List[Any]	    =   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:
			A_		:						Union[str, 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

			A_		:						List[str]	    =   label_smoothed_nll_loss





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if self.optimizer is None:
			A_		:						Optional[Any]	    =   ["""bias""", """LayerNorm.weight"""]
			A_		:						Dict	    =   [
			    {
			        """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,
			    },
			]
			A_		:						Optional[int]	    =   Adafactor if self.args.adafactor else AdamW
			if self.args.adafactor:
				A_		:						Dict	    =   Adafactor
				A_		:						Union[str, Any]	    =   {"""scale_parameter""": False, """relative_step""": False}
			else:
				A_		:						Union[str, Any]	    =   AdamW
				A_		:						Optional[int]	    =   {
				    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
				    """eps""": self.args.adam_epsilon,
				}
			A_		:						int	    =   self.args.learning_rate
			if self.sharded_ddp:
				A_		:						Union[str, Any]	    =   OSS(
				    params=A_     ,     optim=A_     ,     **A_     ,     )
			else:
				A_		:						List[Any]	    =   optimizer_cls(A_     ,     **A_							)

		if self.lr_scheduler is None:
			A_		:						Dict	    =   self._get_lr_scheduler(A_							)
		else:  # ignoring --lr_scheduler
			logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   arg_to_scheduler[self.args.lr_scheduler]
		if self.args.lr_scheduler == "constant":
			A_		:						Any	    =   schedule_func(self.optimizer							)
		elif self.args.lr_scheduler == "constant_w_warmup":
			A_		:						Tuple	    =   schedule_func(self.optimizer     ,     num_warmup_steps=self.args.warmup_steps							)
		else:
			A_		:						Dict	    =   schedule_func(
			    self.optimizer     ,     num_warmup_steps=self.args.warmup_steps     ,     num_training_steps=A_							)
		return scheduler





	def    lowerCamelCase(self							):
		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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		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
				A_		:						int	    =   model(**A_     ,     use_cache=A_							)[0]
				A_		:						int	    =   self.loss_fn(logits.view(-1     ,     logits.shape[-1]							)     ,     labels.view(-1							)							)
			else:
				# compute usual loss via models
				A_    ,     A_		:						Union[str, Any]	    =   model(**A_     ,     labels=A_     ,     use_cache=A_							)[:2]
		else:
			# compute label smoothed loss
			A_		:						Optional[int]	    =   model(**A_     ,     use_cache=A_							)[0]
			A_		:						int	    =   torch.nn.functional.log_softmax(A_     ,     dim=-1							)
			A_    ,     A_		:						int	    =   self.loss_fn(A_     ,     A_     ,     self.args.label_smoothing     ,     ignore_index=self.config.pad_token_id							)
		return loss, logits





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Optional[Any]	    =   inputs.pop("""labels"""							)
		A_    ,     A_		:						Optional[int]	    =   self._compute_loss(A_     ,     A_     ,     A_							)
		return loss





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     ):
		A_		:						int	    =   self._prepare_inputs(A_							)

		A_		:						Any	    =   {
		    """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:
			A_		:						List[Any]	    =   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"]:
				A_		:						Optional[int]	    =   self._pad_tensors_to_max_len(A_     ,     gen_kwargs["""max_length"""]							)

		A_		:						Any	    =   inputs.pop("""labels"""							)
		with torch.no_grad():
			# compute loss on predict data
			A_    ,     A_		:						List[Any]	    =   self._compute_loss(A_     ,     A_     ,     A_							)

		A_		:						int	    =   loss.mean().detach()
		if self.args.prediction_loss_only:
			return (loss, None, None)

		A_		:						int	    =   generated_tokens if self.args.predict_with_generate else logits

		if labels.shape[-1] < gen_kwargs["max_length"]:
			A_		:						str	    =   self._pad_tensors_to_max_len(A_     ,     gen_kwargs["""max_length"""]							)

		return (loss, logits, labels)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		# If PAD token is not defined at least EOS token has to be defined
		A_		:						List[str]	    =   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}"""							)

		A_		:						str	    =   pad_token_id * torch.ones(
		    (tensor.shape[0], max_length)     ,     dtype=tensor.dtype     ,     device=tensor.device							)
		A_		:						List[str]	    =   tensor
		return padded_tensor








"""simple docstring"""
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_lowerCAmelCase         =							logging.getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   git.Repo(search_parent_directories=snake_case__					)
	A_		:						List[str]	    =   {
	    """repo_id""": str(snake_case__					),
	    """repo_sha""": str(repo.head.object.hexsha					),
	    """repo_branch""": str(repo.active_branch					),
	}

	with open(os.path.join(snake_case__   ,   """git_log.json"""					)   ,   """w"""					) as f:
		json.dump(snake_case__   ,   snake_case__   ,   indent=4					)






def 			__UpperCamelCase	(		snake_case__					):
	if params.n_gpu <= 0:
		A_		:						Dict	    =   0
		A_		:						str	    =   -1
		A_		:						int	    =   True
		A_		:						Union[str, Any]	    =   False
		return

	assert torch.cuda.is_available()

	logger.info("""Initializing GPUs"""					)
	if params.n_gpu > 1:
		assert params.local_rank != -1

		A_		:						str	    =   int(os.environ["""WORLD_SIZE"""]					)
		A_		:						int	    =   int(os.environ["""N_GPU_NODE"""]					)
		A_		:						int	    =   int(os.environ["""RANK"""]					)

		# number of nodes / node ID
		A_		:						Optional[int]	    =   params.world_size // params.n_gpu_per_node
		A_		:						Optional[int]	    =   params.global_rank // params.n_gpu_per_node
		A_		:						Tuple	    =   True

		assert params.n_nodes == int(os.environ["""N_NODES"""]					)
		assert params.node_id == int(os.environ["""NODE_RANK"""]					)

	# local job (single GPU)
	else:
		assert params.local_rank == -1

		A_		:						Dict	    =   1
		A_		:						Tuple	    =   0
		A_		:						Dict	    =   0
		A_		:						Optional[int]	    =   0
		A_		:						List[str]	    =   1
		A_		:						List[Any]	    =   1
		A_		:						List[Any]	    =   False

	# sanity checks
	assert params.n_nodes >= 1
	assert 0 <= params.node_id < params.n_nodes
	assert 0 <= params.local_rank <= params.global_rank < params.world_size
	assert params.world_size == params.n_nodes * params.n_gpu_per_node

	# define whether this is the master process / if we are in multi-node distributed mode
	A_		:						Optional[int]	    =   params.node_id == 0 and params.local_rank == 0
	A_		:						Optional[Any]	    =   params.n_nodes > 1

	# summary
	A_		:						str	    =   F"""--- Global rank: {params.global_rank} - """
	logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes					)
	logger.info(PREFIX + """Node ID        : %i""" % params.node_id					)
	logger.info(PREFIX + """Local rank     : %i""" % params.local_rank					)
	logger.info(PREFIX + """World size     : %i""" % params.world_size					)
	logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node					)
	logger.info(PREFIX + """Master         : %s""" % str(params.is_master					)					)
	logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node					)					)
	logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu					)					)
	logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()					)

	# set GPU device
	torch.cuda.set_device(params.local_rank					)

	# initialize multi-GPU
	if params.multi_gpu:
		logger.info("""Initializing PyTorch distributed"""					)
		torch.distributed.init_process_group(
		    init_method="""env://"""   ,   backend="""nccl"""   ,   )






def 			__UpperCamelCase	(		snake_case__					):
	np.random.seed(args.seed					)
	torch.manual_seed(args.seed					)
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed					)

