

"""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


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "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    snake_case     (    __snake_case  ):

				def __init__(     self							:    int					,  UpperCamelCase__							:    Union[str, Any]=None					,  UpperCamelCase__							:    Dict=None					,  *UpperCamelCase__							:    Any					,  **UpperCamelCase__							:    str)->       Optional[int]:



						'''simple docstring'''


						super().__init__(*UpperCamelCase__					,  **UpperCamelCase__)

						if config is None:
								assert isinstance(self.model					,  UpperCamelCase__), (
								    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
								    f" {self.model.__class__}"
								)
								__lowerCAmelCase:	Dict   				=      self.model.config
						else:
								__lowerCAmelCase:	str   				=      config

						__lowerCAmelCase:	Any   				=      data_args
						__lowerCAmelCase:	Tuple   				=      self.config.tgt_vocab_size if isinstance(self.config					,  UpperCamelCase__) 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:
								__lowerCAmelCase:	Dict   				=      torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
						else:
								# dynamically import label_smoothed_nll_loss
								from utils import label_smoothed_nll_loss

								__lowerCAmelCase:	List[str]   				=      label_smoothed_nll_loss

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    int)->       Dict:



						'''simple docstring'''


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

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

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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      arg_to_scheduler[self.args.lr_scheduler]
						if self.args.lr_scheduler == "constant":
								__lowerCAmelCase:	str   				=      schedule_func(self.optimizer)
						elif self.args.lr_scheduler == "constant_w_warmup":
								__lowerCAmelCase:	Any   				=      schedule_func(self.optimizer					,  num_warmup_steps=self.args.warmup_steps)
						else:
								__lowerCAmelCase:	int   				=      schedule_func(
								    self.optimizer					,  num_warmup_steps=self.args.warmup_steps					,  num_training_steps=UpperCamelCase__)
						return scheduler

				def 							lowercase_					(     self							:    List[str])->       Optional[torch.utils.data.Sampler]:



						'''simple docstring'''


						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 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    List[str])->       Dict:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Optional[Any])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      inputs.pop("labels")
						__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      self._compute_loss(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						return loss

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    nn.Module					,  UpperCamelCase__							:    Dict[str, Union[torch.Tensor, Any]]					,  UpperCamelCase__							:    bool					,  UpperCamelCase__							:    Optional[List[str]] = None					,  )->       Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self._prepare_inputs(UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      {
						    "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:
								__lowerCAmelCase:	int   				=      self.model.generate(
								    inputs["input_ids"]					,  attention_mask=inputs["attention_mask"]					,  **UpperCamelCase__					,  )
								# in case the batch is shorter than max length, the output should be padded
								if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
										__lowerCAmelCase:	Tuple   				=      self._pad_tensors_to_max_len(UpperCamelCase__					,  gen_kwargs["max_length"])

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

						__lowerCAmelCase:	Optional[Any]   				=      loss.mean().detach()
						if self.args.prediction_loss_only:
								return (loss, None, None)

						__lowerCAmelCase:	Any   				=      generated_tokens if self.args.predict_with_generate else logits

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

						return (loss, logits, labels)




				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Optional[int])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      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}")

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





"""simple docstring"""


from math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Tuple   				=      list(range(0 ,			__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Optional[Any]   				=      [item for sublist in list(device_map.values()			) for item in sublist]

		# Duplicate check
		__lowerCAmelCase:	List[Any]   				=      []
		for i in device_map_blocks:
				if device_map_blocks.count(__SCREAMING_SNAKE_CASE			) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(__SCREAMING_SNAKE_CASE			)
    # Missing blocks
		__lowerCAmelCase:	Optional[Any]   				=      [i for i in blocks if i not in device_map_blocks]
		__lowerCAmelCase:	List[Any]   				=      [i for i in device_map_blocks if i not in blocks]

		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
				    " These attention blocks were specified more than once: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
				    "blocks to a device on the device_map: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
				    + str(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	List[Any]   				=      list(range(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Dict   				=      int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE			)			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      [layers[i : i + n_blocks] for i in range(0 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)]

		return dict(zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)


